MASTER S THESIS. Spatial Network Analysis for Urban Cycling Networks

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1 INSTITUTE OF TRANSPORTATION - TECHNISCHE UNIVERSITÄT MÜNCHEN M.Sc. in Transportation Systems MASTER S THESIS Spatial Network Analysis for Urban Cycling Networks Author: Cesar Ricardo Criollo Preciado Supervisor(s): MSc Chenyi Ji, Department for Urban Structures and Transportation Planning MSc Dipl Ing Arch Christian Schwander, Rheform - EntwicklungsManagement GmbH Prof. Gebhard Wulfhorst, Department for Urban Structures and Transportation Planning München, November 2012

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3 Fachgebiet für Siedlungsstruktur und Verkehrsplanung Univ.-Prof. Dr.-Ing. Gebhard Wulfhorst Arcisstr München MASTER S THESIS for Mr. Cesar Ricardo Criollo Preciado Date of issue: Deadline for hand-in: Title: Spatial Network Analysis for Urban Cycling Networks Background: Space Syntax is a set of theories and techniques for the analysis of spatial configurations. It is based on assumptions on intuitive human orientation and spatial perception. For this purpose, Space Syntax network models are derived from axial line maps. Various studies have shown correlations between Space Syntax network indicators and pedestrian flows. From this, Bill Hillier, a founding father of Space Syntax, has developed a theory of Natural Movement. This Master Thesis will investigate the applicability of Space Syntax with regard to urban cycling networks. Aims and methodology: In the first part of the thesis, the candidate will introduce Space Syntax, its theoretical foundations and indicators. He will then analyse prior studies on Space Syntax analyses and their correlations with human movement in networks for all modes of travel. Especially, the candidate will point out findings that have been made for or appear to be transferable to cycling. In the second part the candidate will build up a Space Syntax Model for a case study area (to be specified with supervisors). A major challenge in this part of the work will be to transfer the axial line approach to an urban cycling network with dedicated infrastructure, limited crossing possibilities etc. The candidate will elaborate the according network modelling problems and suggest solutions. The result of this step will be a network model for the case study area that allows the calculation of all relevant Space Syntax indicators.

4 Beside the Space Syntax indicators, the network model should also allow the calculation of traditional network analysis indicators, mainly betweenness centrality based on distance and travel time. In the third part, the candidate will analyse the fit of the different network indicators that have been computed by the modelling work to real-world cycling behaviour. This analysis will consist of a quantitative part (correlation analysis of network indicators with bicycle traffic counts) and a qualitative part (expert interviews with local cycling experts). Supervision: The Master s Thesis is supervised by M.Sc. Chenyi Ji from TUM and Christian Schwander from Rheform GmbH. The scientific responsibility of the work fully lies with TUM. TUM also supports the candidate in acquiring bicycle counts for the study area. Rheform will provide methodological assistance regarding the Space Syntax Model development. The candidate will develop a structural draft of his thesis within two weeks from the date of issue of the thesis and discuss it with his supervisor. Further consultation appointments will be arranged between candidate and supervisor as required. Univ.-Prof. Dr.-Ing. Gebhard Wulfhorst (Fachgebiet für Siedlungsstruktur und Verkehrsplanung)

5 Table of Contents V Table of Contents ABSTRACT INTRODUCTION URBAN CYCLING Urban Cycling Networks CYCLING CULTURE IN MUNICH Munich s Transport Development Plan Bicycle Traffic Cycle Network and Transport Integration SPATIAL NETWORK ANALYSIS THE PRINCIPLE OF NATURAL MOVEMENT Axial, Street Segments and Road Centre Lines Spatial Indicators PEDESTRIANS AND CYCLISTS IN SSX Differences and Similarities STATE OF THE ART SSX Research Experience TRANSPORTATION NETWORK ANALYSIS Transportation Modelling METHODOLOGY DEFINING A SUBJECT OBTAIN DATA Spatial and Geographical Data Statistical Data BUILDING MODEL Urban Network Link Connectivity and Topology Check Link and Network Attributes Transitability and Accessibility Transport Network SSX Indicators and sdna BENCHMARKING Experts Interview Comparison Method... 52

6 Table of Contents VI 4. MODEL CONSTRUCTION AND OUTPUT CHALLENGES ON DATA Spatial Network Editing and Processing Data Cyclists Gates URBAN NETWORK MODEL TRANSPORT NETWORK MODEL MODEL BENCHMARKING Experts Interview Output Statistical Review OUTPUT ANALYSIS AND DISCUSSION CONCLUSIONS AND RECOMMENDATIONS FUTURE DEVELOPMENTS CENSUS DATA TRAFFIC ASSIGNMENT GLOSSARY OF TERMS ABBREVIATIONS SOURCE OF REFERENCE BIBLIOGRAPHY JOURNAL ARTICLES: NEWSPAPER ARTICLES: LIST OF LAWS AND REGULATIONS WEBLIOGRAPHY LIST OF OTHER SOURCES LIST OF FIGURES LIST OF TABLES APPENDIX A-DATA REQUIREMENTS CONTACT COMMUNICATION APPENDIX B-OFFICIAL FORM REQUESTING DATA APPENDIX C-ROAD HIERARCHY AND LANE CLASSIFICATION MAPS APPENDIX D-DETAIL OF COUNT LOCATIONS APPENDIX E-TRAFFIC VALUES ON COUNT LOCATIONS APPENDIX F-DAY, SEASON, ANNUAL CYCLING TRAFFIC AVERAGES APPENDIX G-PROJECTED TRAFFIC VALUES ON COUNT LOCATIONS APPENDIX H-NDS ATTRIBUTES, PARAMETERS, AND EVALUATORS APPENDIX I-URBAN NETWORK MAP FOR BETWEENNESS 2500 MTS APPENDIX J-MAP FOR CYCLING EXPERTS INTERVIEW APPENDIX K-CYCLING EXPERTS INTERVIEWS

7 Abstract VII Abstract The theory of Natural Movement developed by Bill Hillier (Hillier 1996) has been successful in explaining pedestrian movement and its relation with the patterns and structure of urban spaces. However, there is a great need of expanding horizons for modelling and prediction on cycling planning. This Master s Thesis will evaluate prediction of cyclists movement through an urban street environment. As Space Syntax tries to relate spatial patterns to movement, a city was chosen and a Geographic Information System model of the real street network was defined; in a later stage, a refined cycling network was selected based on local traffic regulations to apply Space Syntax indicators. A series of locations was selected to test their correlation to real cycling traffic counts and with a GIS based transport model. A network model of Munich was used for this exercise; specialized infrastructure for Cycling was considered and added to the network obtained from Open Street Map. After building a consistent network and calculating these Spatial Indicators suggested by Space Syntax were tested against empirical cyclists gate counts. The results of this test were very low compared to previous experience and expectations. These findings leaded to a cause analysis that will be developed at the end of the paper and these will indicate recommendations regarding higher quality for experimental data and how cycling infrastructure should be modelled. Instead of multiple parallel links, a unique link shall be considered, as human perception does. This thesis showed that it is possible to create an urban network from open data. Nevertheless, this requires further protocols to standardize intersections and link creation. Moreover, this can be used to develop a cyclist movement model of Munich City and urban area. However, model performance for explaining recorded cyclist flows is still poor, due to the accuracy of the model and shortcomings of the survey method. As a result, we propose to improve the model by taking into account visibility of the urban space and standardise the survey data, and covering more factors that influence the route choice for cyclists.

8 1-Introduction 8 1. Introduction After Copenhagen (The City of Copenhagen, 2011), many cities in continental Europe, have understood that urban mobility requires more than extensive and wide highways, a wide variety of mobility solutions that fit every single need, in terms of distance and time. Climate warming, has triggered the interest of numerous groups on finding solutions for reducing harming emissions into atmosphere, rather than this, there is a higher and more concerning reality, industry and local governments need to maintain the levels of mobility that we have experienced in the past, with a growing population number in a confined urban area. In other words, with a pipe full of water that cannot be grown, hence the only chance is to increase liquid s flow speed, which means in urban terms, to increase the efficiency of our mobility solutions and urban spaces. Making a more accurate use of different modes of transport is part of the solution, yet not the panacea. Understanding the urban environment and its implications with movement patterns is a point that hardly has evolved on the last years, furthermore when all the theories for transport have been developed for motorized vehicles and then roughly transferred to other modes with diverse mind-sets. Space Syntax (SSX) ( offers a mathematical point of view for this specific part of behaviour, the one assumed to move through an urban environment to reach a destination regardless of the activity and of a motorized vehicle. Pedestrians and cyclists are the most natural actors on these environments, hardly accounted, and it is time to embrace the most convenient means of transport are the ones far away from petrol and heavy steel. In Munich, rheform - EntwicklungsManagement GmbH (rheform) is applying the expertise developed by SSX for pedestrian movement, analysing it in commercial and academic facilities, in which improvements can be seen and quantified positively. The numerous and wide similarities on route choice and displacement range, brought the question. If this kind of assumptions can be applied for cyclists movement, and how can be implemented and what elements need to be considered to mimic the cyclists behaviour not as individual route choice but as aggregated traffic volume, as it has been demonstrated before for vehicular traffic (Paul 2012) Urban Cycling Travelling by bicycle is the fastest choice (Cyclists Touring Club, 2012), widely probed by urban experiences in Europe, Asia, or Latin America. This was

9 1-Introduction 9 acknowledged by transport planners, and integrated into urban mobility as an element that may serve for short and frequent trips. Along with this, connectivity and easy switching between modes has been widely spread all over the public transport facilities, fostering a more wise and clever commuter planning by the users. The travel time advantage offered is just the peak of the iceberg; health, environmental and other externalities can be positively addressed by fostering the recurrent use of the bicycle. We as society are starting to wake up from an illusion forged by the automotive and marketing industries. For many years they persistently suggested that motor vehicles are a symbol of progress, status, modernity and hence, prosperity. Motor vehicles are an excellent tool that must be used in a measured fashion, something that every big and metropolitan area is painfully realizing. The issues and consequences of its excessive use are much costlier than the benefits. Several cities have already applied different strategies to bring pedestrians and cyclists to urban areas by using NMT modes and they have probed widely the economical, environmental, and social benefits of this change (Tolley 2011). Figure 1 Sign posting in Munich's main routes (Landeshauptstadt München, June 2007) Developing a urban cycling culture, is basically returning the city ownership to its own citizens, broadly elaborated, to give back all public spaces to inhabitants to use them to life their lives, to interact, to spend some time in an open space, to run errands comfortably, and mainly to enjoy the fact of make their cities alive. This may sound as kind of utopia, but pedestrians traffic (Sauter 2008, Stadt Zurich 2003), facilitated leads to several positive health and economic effects. Cycling for daily commuting in urban areas is simply extending the range of those effects. As discussed above most of the commuters are willing to invest certain amount of time in commuting, and as supposition in most of the transport planning models, cycling extends the catching area of public transport stops.

10 1-Introduction 10 Sauter mentions that pedestrian traffic can be considered in two different categories, commuters with a fix destination while others are running errands and may stop wherever they find something attractive, creating a floating mass that moves very slowly. Something similar happens with cycling. Cyclists can stop anywhere whenever they wanted, fostering chances for slowing down traffic and to do many more things rather than unique commuting when one is moving in a car and gives no space for doing different activities. This is the trigger for several minor retails, the places where social activities takes place, cafes, bookshops, gyms, grocery shops, and many others that benefit from window and social shopping. These behaviours are forging a new urban culture around the bicycle. Not as a tool only for sports, instead a convenient means of transport, in which one has not to become a cyclists but integrate it seamlessly into their own life style, in some cases to the point that the bicycle itself becomes a fashion item, a statement of singularity as the car became once many years ago Urban Cycling Networks A city running on bicycles brings way more benefits that costs and externalities than any other means of transport. However, it is very important to remember that the vehicle is driven by a human being, and apart from motor vehicles, cyclists is the engine, navigation, body and passenger at the same time. The flexibility and capabilities of the bicycle to go from one environment to other proposes several advantages and restrictions that need to be addressed in a smart way to provide a swift combination with the other means of transport. Infrastructure as specific lanes, sing posting and signalling, are just a few elements that needs to be deployed all over the city to provide the elements that will bring the network alive and will foster the change and usage of the same. On the hard side of the measures there are some soft concepts that are resumed in the Cycling-Inclusive Policy Development: A Handbook (Godefrooij, April 2009), as follows: 1. Coherence: The network has to provide connections between all origins and destinations (OD), considering the main and most popular destinations and the possibility to switch from one mode to another, when using the bike not all way is desired. Thus, easy way finding, multiple route choices and parking facilities at both ends. 2. Directness: Detours along the network should be minimal reducing the travel distance between (OD), becoming an attractive substitute for car or even walking trips, increasing the chances for using more free time to other purposes. Traffic lights, crossing delays, one-way roads, and sharp curves among others must be minimized.

11 1-Introduction Safety: It is clear that diverse users converge in a public road, with different speeds, vehicles, experience, and confidence while driving. Thus, providing measures that will ensure a harmonic merge of all users must be enforced. Speed harmonization, traffic calming, driving awareness, simple and clear landscape, and urban design, among others are measures that are intended to reduce accidents and protect the weakest users. 4. Comfort: Ease the travel in every possible sense. In another words, provide constant road conditions, simple and swift crossings, clear and complete signalling and lighting, protection against weather changes, among others that makes cycling an attractive and logical option for commuting. 5. Attractiveness: It is the combination of all previous items. Tailored to the specific needs of the population of the location in which the network would be deployed. Therefore, local common sense will be attached to the main solution and social safety will be strongly pursued. It is clear that network design and planning is highly related to a long-term strategy, therefore the first and second principles mentioned above are the main guidelines to follow and to develop on this research. The last three principles are oriented to tactical measures, thus linked to the infrastructure on ground level, which is outside the scope of this work. Figure 2 Cycling Network in Munich's Old City (Landeshauptstadt München, June 2007) Munich as a city has a network that allows the deployment of these principles in a consistent and logic manner. A person living in any quarter of the city would be able to reach any other location by bike while following such network. This task would be accomplished in a safe fashion while one complies with etiquette and traffic signals, using the designed infrastructure but also being gentle and active citizen. A network can have many levels depending on how it is composed and the importance that links acquires once users understand the coherence and directness. This is visible on the cycling infrastructure that composes the city s network. Initially streets with speed restrictions like Tempo 30, which are the final and initial leg of any trip. Secondly cycle lanes provides exits and connections between neighbourhoods

12 1-Introduction 12 gaining relevance as seen on Figure 2 for green streets Finally, the Planning authorities in the city organized two rings and fifteen radial routes with signposting to interconnect separated city quarters as seen on Figure 3, extending the range of cycling to a city level. A natural consequence was to extend those routes to State level fostering cycling travel and tourism. Figure 3 Main Cycling Signposted Routes in Munich's Metropolitan Area (Landeshauptstadt München, June 2007) 1.2. Cycling Culture in Munich Cycling since the Nineteenth Century was a major part of daily life, a fact quickly forgotten in the 1970 s due to increase affordability of automobiles. Rapidly was acknowledged this as a major threat to the conditions of the city. Narrow and sneaky roads, lack of parking places in the town centre, in conclusion to transform an 800 years city for accommodating cars was not worth the try. Instead, the city planning authority gave a second thought on the usage of every mode of transport and developed a strategy to give to every mode a space and a time: On this way, a learning process for people was started, in which the appropriate mode for each occasion will fall as natural. For the generation that grew up in the 60 s and 70 s, purchasing a car was clearly a milestone for success and entering into an adult life. A symbol of responsibility and

13 1-Introduction 13 maturity. However, this trend (Kuhnimhof, Buehler, Dargay, 2012) is plummeting for younger generations. The automobile is not seen anymore as an asset or a symbol of that success, it is clearly a liability, money and time can be addressed in a clever resourceful way. Then the optimal choice was public transport, but once again, popularization erased the added value of it. Time and cost advantage disappear and once the efficiency of the system cannot offer any further return, new options supplying those aggregated values rose. During the 80 s the city of Munich acknowledged the importance of the bicycle as a vehicle, therefore a cycling street network was laid, very basic but covering the main roads and links that historically connected the different city quarters. This network developed to a complete system as shown on Figure 2 and Figure 3.The City provided infrastructure, the most experienced and confidence cyclists took the lead, and step by step a domino effect made this commuting option popular to the point that elderly people and kids took the chance. At this point safety became a concern, awareness between all actors on the road was needed. Radlhaupstadt (Landeshauptstadt München, September 2012), the local cycling campaign, has shaped all the soft rationales behind this choice. From technical and safety revision services on regular basis, to annual events that supports the creation of a cycling/commuting culture. All these events are intended to separate the fact of cycling being a sport and turning it into the best option for commuting in this urban environment. Extending the use of cycling to children and elderly people it is a work that takes years and a great number of awareness and education sessions, however, their return and domino effect can be assess over time. This educative process leads to a predictive behaviour of all actors on the road, hence to reduce the number of accidents, empowering the most vulnerable groups to take part on this interaction and gain greater benefits than the investment made Munich s Transport Development Plan The transport development plan for Munich (Landeshauptstadt München, March 2006), has the long term vision of compact urban - green which compromises a wide range of measures to achieve a certain level of harmony between every single traffic type, inhabitants, their activities and nature. Being conscious of the size of the city, density, and developments, that structure its identity, therefore a strategy was build and lead by the City Hall. This act included all possible stakeholders in this topic, from automotive companies, different types of

14 1-Introduction 14 commuters associations, public and transport companies, environmental and social activists, and universities among others. The main idea of this exercise was to involve all possible views on a topic that relates to every single individual and affects most of our interactions on a public environment. Base on their inputs, three different scenarios were built, first a private vehicle oriented, public transport oriented, and finally an active NMT oriented. The main rationales are widely explained on the very same development plan, yet the lead to a single conclusion, private vehicle is still very important for the inhabitants of the city. This is supported on the document, as there is a major need for jobs in areas in which the accessibility of the public transport network varies from medium to low, which means in terms of travel time, approximately 30 to 45 minutes, or more that 45 minutes. These kinds of conclusions may be deceiving, because most of the work force is required to make use of a vehicle in order to increase their accessibility to clients. Which in many cases is relevant, but does not mean that every single worker is required to commute to a different work location every time; many services are required to commute to the same location, on a regular basis. Initiatives like Bike to Work are shifting this situation. Nevertheless, cycling accounts for short length trips, frequent ones, these are performed by other groups that require a more regular and higher frequency in those displacements, for instance, grocery shopping, social gatherings, errands, are among others (Figure 5, BMVBS, 2010). This and all the measures taken to create a safe cycling environment has lead the raise the status of the bicycle from a recreation tool to a real option for daily commuting Bicycle Traffic Compact-Urban-Green states the Radverkehr in München (Landeshauptstadt München, 2010) as the rationale behind the policies drawn and implemented in Munich during the last 40 years. A dense compact city with an old core surrounded by several rings, reflecting its evolution through time. Nevertheless, this evolution never went too far from a tiny, tidy compact city, in which travel distances maintain a bearable travel time for the worst scenarios. This is easily seen in the Cycling Network Development Plan, almost every single block of the city should be part of the network, as seen on Figure 3, which shows the present signposted network, and main routes and Figure 6 show us the future vision to It is visible on the thoughts given to the city s main public transport network planning, that every citizen must be able to reach a desired location by using a combination of

15 1-Introduction 15 this provided network and by means that refer from motorized vehicles for all the cases when a vehicle is not absolutely required, even on family or group trips. Figure 4 Cycling Network Map for Munich (Landeshauptstadt München, June 2007) To achieve this vision it was need a number of hard and soft measures that will lead to a creation of a critical mass of commuters that will probe the right selection of those. Munich s Cycling plan, contemplated laying out a network of roads with parallel cycle lanes that will permit cyclists, pedestrians and drivers a safe passage through traffic. This solely infrastructure is not enough, dedicated traffic lights, ground and vertical signalling was complementing the scenario. This is easily seen in Figure 4, the urban street network has different types of infrastructure, from normal calmed streets, to double lanes along the river or in the middle of the parks. All these hard measures permitted in most of the city quarters a swift connection between housing areas and the ones with possible destinations for a number of activities. This cannot be achieved in a short period; therefore, a prioritization for building this entire infrastructure was required. Major recognized links for structuring a primary network was established as leading point. Once the cycling traffic grew and demand from users was perceived, an alternative and side network came into place extending the reach to more locations deeper into neighbourhoods, industrial areas and town centre. With time passing by the network matured and provide access to virtually all OD in the city. However, it is not only about building roads, the users must know how they can make their way based on their own choice. Thus, signposting is covering the entire main, side, and alternative routes (Landeshauptstadt München, June 2007). Bringing a last level of cohesion was required, in other words all the people does not live next to one of those routes, therefore Tempo 30 zones in between were established. In these areas, cyclists have the same rights on the road with cars, where traffic is instructed not to surpass a speed limit of thirty kilometres per hour.

16 1-Introduction 16 From hard to soft measures. Knowledge and awareness makes a real difference; this was achieved not only through awareness and through educational sessions, commuters and public in general is provided with maps and web based routing solutions to help them in finding their own way across the city. This clearly is a welcoming measure for new inhabitants, or relocating ones, easing the process of settling in a new area. Radlhauptstadt, is the main face on this role. Maps, routing, safety, rules and now cycling merging as an element on every life style, can be considered as the next phase on maturing cycling traffic and mixing it with motorized traffic. Part of this new approach is taking place while writing this report. As more commuters are using the public transport the conflict due to speed gap between cyclists and pedestrian is more common, at the same time more commuters want to ride faster to reach their destinations, breaking the former harmony with pedestrians and moving towards competing efficiently with motorized solutions. Briefly, cycling commuters are starting to ask for cyclists highways, rising from fifteen to thirty or more kilometre per hour, as average riding speed. Consequently, a new change is required on the hard measures, in this case to merge cycle lanes and car lanes in one. Lindwurmstraße is an example, a road with heavy car and cyclists traffic demand. Speeding commuters now (as August 2012) are sharing a floor signed lane with cars on the same rights of way. Obviously, this is fitted only to experience and confident riders, something that is not of the interest of this research exercise, as they are still not a representative group of cyclists in Munich Cycle Network and Transport Integration While the public transport network is intended to cover the major length of the trip, the first and the last kilometre of the trip, are covered on foot or bicycle, sometimes in car allowing to switch from private NMT and motorized vehicles to S-Bahn and U- Bahn within urban areas (MVV, 2010) and the whole metropolitan region (MVV, 2011). Therefore, the integration of systems is achieved by providing comfortable switching locations, in another words, provide spaces with a generous number of parking places, protected from environmental agents, and be prepare to offer a tariff schema that favours these practices in the long run. This may cover longer trips, but they are not the most frequent ones (BMVBS 2010). This has been seen for Munich, which follows the main trends in Germany, as shown on Figure 5 the vast majority of the trips are less than five kilometres, and from those more than a half is covered on bicycles, which are more frequent to happen. These higher frequencies in a vehicle-oriented culture like in the United States, is covered by car, having all the externalities that are of public knowledge. The aim established for Munich, is consequent with the development of a dedicated cycle lane network,

17 1-Introduction 17 safe oriented and segregated infrastructure, and several soft measures like, awareness campaigns, cycle fashion shows, technical and safety workshops, education in primary schools, and many others that aim to erase the boundary between cyclists and commuter. Figure 5 Accumulative Travel distances for each main mode of transport (BMVBS 2010) The unified transport fare inside Munich Public Transport System enables users to switch from one mode to another with no major concerns. Along with this is possible to travel with a bicycle in the metro, city, regional and intercity trains. By paying an additional ticket, a normal bike can be taken on the designated wagons, while for folding bikes there is no need. Measures for increasing accessibility of cyclists and their bikes into the train stations as infrastructure or tickets are not the only considerations to turn it into a bicycle friendly environment. Wagons must have designated locations for accommodating an expected number of bike users, doors and platform levels must be aligned to provide a quick and easy transition, and users should be aware of some basic manners for riding the vehicle together and defuse any possible conflict from one another.

18 1-Introduction 18 Figure 6 Network Concept for 2015, Munich's Traffic Development Plan (Landeshauptstadt München, March 2006)

19 2-Spatial Network Analysis Spatial Network Analysis Network Analysis falls into the realm of Spatial Analysis, as models real world in term of possible relations and interactions of two different elements, nodes, and links. By joining them, it is possible to create a network and hence to try to model the behaviour of a body moving through it. The mathematical equations behind their modelling are not simple, a limitation on the last century that delayed its development, with the increase of computational power simulations were possible, and therefore the complexity of these models was tamed and finally seen in a more comprehensive representation, this is where GIS helped. Time and space, are similar as they can have several dimensions, and distance between two points / times for a pair of events can have similarities that can be recurrent. Analysis like proximity, combination of variables in the same location for predicting a third one, map algebra, pattern recognition, and topology, these among others tried to transfer linear knowledge to a two dimensions and three dimensions. Figure 7 Basic Node Link representation It is here where relations of street and intersections can be analysed on two dimensions. In this case, streets are mostly linear, becoming links while intersections are locations where those street segments meet, becoming nodes. A simplification that can be extreme, as shown on Figure 7 (Source Imagery: Google Earth) straight streets can easily become a street segment, but in the case when there is physical separation, there will be parallel segments, for instance Friedensengel monument slides Prinzregentenstraße into two mirrored streets. Theory analysing relations between those two is called Graph Theory (Rodrigue 2009). Thus, representing real street conditions, will require a certain level of simplification, segments need to be straight and long as possible between two nodes.

20 2-Spatial Network Analysis 20 Once network has been simplified, it is necessary to ensure high precision on all connecting elements. All links must meet at nodes giving no space to sub-graphs, which are simply unconnected groups of links and nodes, likewise a link can be connected by both ends to the same node, a buckle should be avoid as it does not provide any relevant benefit. As transport networks lies on the ground, real terrain offers three dimensions, north, east and altitude; theory considered on this paper will only look to planar graphs, in which north and east are precisely represented while altitude will be the same for all nodes and segments. Measurements between all components on network can shed lights on the performance of it. For instance comparisons between the path and straight distances can indicate how dense and fine-grained network is, while a higher discrepancy may indicate longer detours and not enough segments to do the shortest possible trip, at the same time the number of segments and nodes per area unit or density can give a very good idea of how developed is. On the same sense, average length per segment will support the detour distance and density measure. Relations between different elements can be considered as well, the number of nodes against the number segments, would indicate how complex is the network and how different routes can be configured between a single pair of OD. Finally, segments can be assessed with the nodes, by comparing how many segments exist against the theoretical maximum of possible segments in between. These are among others, metrics that can be derived from the network; the ones of interest on this paper will be explained widely on the coming sections The Principle of Natural Movement This relationship between the structure of the urban grid and movement densities along lines can be called the principle of natural movement (Hillier 2007). Streets are public spaces in which movement or mobility takes place. This movement tends to imitate the simplest trajectories. A society determines space as a location for the interaction several forces, politics, ideologies, economy, among others that gives certain order through a simple struggle to connect global and local scales with inside and outside relations (Hillier, 1988). In other words, if a street is long and straight, will it be perceived as a more attractive route, and if so, why? One explanation can be that it provides longer lines of sight hence are easier to remember. This is not completely applicable to gravitational destinations, like Squares, Hospitals, Academic or Sport Facilities, Shopping Malls or Natural Attractions, as those can deviate and influence the route choice of the mass.

21 2-Spatial Network Analysis 21 Therefore, for modelling free aggregated movement in an urban environment may require to consider that any location can be at the same time an origin and a destination, a major back draw for any classical transport model. In this case, there are other considerations to take; for instance, the urban space must be simplified in certain way that every street will become a line as straight as possible connecting two different intersections. This process is well defined by Hillier (2007), in which open spaces are defined by the longest line of sight between two or more different intersections. Once the universe of street segments have been defined, they must converge in nodes, which represent crossroads or intersections. This will allow our subject to move through a line from one intersection to the next. Confirming the assumption previously done, the easiest routes to remember are the ones with the minimal number of direction changes. The short is the list of these changes to reach a destination will increase the chances to reach such destination. This is widely discussed by Penn (1998) and once again reviewed by Paul (2012). Figure 8 Basic Axial Line representation Axial, Street Segments and Road Centre Lines Convex spaces are defined by Hillier (2007) as areas, in which two subjects can see each other, and all possible lines of sight for each subject will describe a border, based on that closed set of lines, many will overlap, therefore they need to be purge, and only the longest will remain. This assumption helped to model in a mathematical way the so-called Convex Isovist, which is the base for the axial line, a line linking two nodes with the longest line of sight in between. As can be seen on Figure 8 (Source Imagery: Google Earth) the yellow lines have the longest line of sight before meeting any impassable barrier, yet they may ignore several levelled or legal barriers. This was the main input for SSX. However, accuracy was low, because trajectories and nodes were not as it is in reality, for instance, in an open square, there could be

22 2-Spatial Network Analysis 22 at least four axial lines with almost the same length, creating duplicity and therefore unreal paths across the square or open space. Potential movement was analysed by Turner (2007). Finding that axial lines can be converted into segments as seen in Figure 7, with many more changes of direction and therefore more nodes in between, yet these new ones are following the real spatial structure of the street, and thus nodes are placed as they should be with the difficulties that some turnings and approaches demands. Figure 9 Basic Centre Line representation Turner (2007), found that centre lines are related to segments, hence it has a better performance when compared to gate counts for traffic rather than axial lines, for the same model. This conclusion, open the chances to use mapping data and street digitalized axis as an input for the creation of SSX network models, thus on orange lines can be shown a more approximate street model, one that resembles movement on streets (Figure 9 Source Imagery: Google Earth). Latest developments on software to perform these analyses are using previous suppositions; sdna (Chiaradia, 2012) an application for network analysis took this concept and merged with the same one used in GIS, therefore calling it Link. Which is the same object consider for representing streets on a GIS application, therefore what all we use in navigation appliances and electronic maps. A resume of this development is seen on Figure 10, covering the assumptions made by Hillier, analysis, and evolution by Turner that finally merged with current cartography in GIS by Chiaradia. Figure 10 Evolution of Elements in Network Analysis

23 2-Spatial Network Analysis Spatial Indicators SSX modelled networks composed of axial lines, which represents the longest lines of sights in a convex space (Hillier 2007) and which must converge in a node to configure eventually a network. These elements were later upgraded to segment lines (Turner 2001), to ease the calculations allow the usage of angular analysis and improve accuracy with real world conditions, and more recently validated with central axis road lines (Turner 2007) to foster the integration of open source data on the Internet as Open Street Map. Once, lines and nodes have been defined, there are two main indicators developed by Hillier (1996) which are trying to relate the assumptions made on natural movement theory and spatial structures, thus known as centrality measures (Schwander, Law 2012). Figure 11 Closeness Centrality (C C ) in a general street network (Schwander, Law 2012) Closeness or Integration for SSX, is the inverse sum of the shortest paths from every destination (k) to the current origin (i), thus will represent the potential of movement to this segment (Hillier, Iida, 2005). More precisely, Integration is a measure of the deepness of every single segment measured to a particular segment of interest. Deepness can be expressed as the number of steps between these two links. In this case, a step is counted as each segment part of the path that joins destination and origin links (Hillier, 1988). Figure 12 Betweenness Centrality (C B ) in a General Street Network (Schwander, Law 2012) Betweenness or Choice for SSX is accumulating the number of paths between any given origin (j) and destination (k) through the link of interest, giving its potential (Hillier, Iida, 2005).

24 2-Spatial Network Analysis 24 Based on the spatial and topological relations between nodes and links the results of each measure can be weighted by: Euclidean distance: trip length in metres between OD, measured along every link on the path, therefore the sum of all lengths will be the final value. Topological distance: number of turns between OD, hence for each new change of direction its own value will increase. Angular distance: direction change in degrees between OD, every new change on direction will mean a positive or negative sum to the total turning sum. Along with these measures, it is important to remark that they represent two different dimensions, deepness, and permeability. Betweenness would be the second one, as Hillier explains, the last one gives an idea of how many parallel routes can be built in between the points of interest, allowing more options for a bigger number of routes. As the network in SSX, is the complete universe of analysis, one can take the whole network as OD, or can restrict the scope of analysis by defining a radius, for instance, 400 metres depending on the means of transport may mean one, two or five minutes of displacement. This shapes the idea of a potential model rather than a gravitational one. The second has been frequently used in traditional transport models; on the other hand, the potential one is relegated to social interactions, relations that clearly are seen in other systems. Figure 13 Interaction Models (Rodrigue 2009). Interactions (as shown on Figure 13) are described by using attributes (Vi at origin, Wj at destination) so they can be measured and compared at both ends. Separation (Sij) between those can be considered as a measure of resistance, for instance, distance, travel time, travel distance, angular change, etc. How they are compared, pair by pair, once versus many, is a matter of mathematical and processing power. Thus, their correlation with real life variables would depend on how those can be certainly explained by others.

25 2-Spatial Network Analysis 25 SSX makes use of a Potential Model as grounds to measure all possible relations of a single link with all links on the network at a particular time. Nevertheless, this analysis as Hillier explains is not done on the geographical segments (orange), but inversely done on links (nodes on blue), which are treated as nodes and nodes as links (light blue) as shown on Figure 14 (Source Imagery: Google Earth). This puts all the weight of the analysis on the physical links, where in reality all movement and activities happens. Figure 14 SSX symbolization of Nodes and Links Light Blue lines on Figure 14 are the former nodes; therefore, they represent the logical connections of one link with the others, leading to create a mathematical model of the relations of all links in a topological manner Pedestrians and Cyclists in SSX As Hillier (1988) describes space and the fashion in which urban spaces converge is a clear variable for assessing the behaviour of masses of traffic. Therefore, how the navigant understands the complexity of the topological configurations of the street networks plays a major role on the choice for selecting a particular route. This area requires extensive research. To establish the arguments that lead to individual route choice are far ahead from complete understanding. However, mass traffic, groups of individuals navigating through a grid is a measurement of the configuration of such grid. Pedestrians have a lower movement speed, in average three to five kilometres per one hour, common sense in most of the cultures leads to find faster means of transport but not to choose routes that are more complex, but the ones that allows maximum possible speed. In this regard, always the easiest route is the one with less turns or changes of direction; more changes means more indications to follow, increasing complexity. This does not imply that distance or travel time will be minimized, an assumption that directly conflicts with the previous hypothesis.

26 2-Spatial Network Analysis 26 Hillier (2005) states, There is enough probe on research and consultancy studies demonstrating that the assumption of the shortest path is not accurate. Rather, Natural Movement may implies more considerations related to the human perception of the urban realm, what we can see, what we can remember, are great factors that prevail and become the main argument for mass non-motorized traffic. Finally, Hillier explains that these entire stimuli from the urban structure are the most decisive factors, as there is no chance to acquire precise motivations for route choice from every single pedestrian, aggregated movement can be explained and shows better performance on statistical analysis rather than simple minimum travel distance. In this sense, SSX considers that pedestrians do not have concerns on spending more time or walking longer distances, while they can reach their destination safely and timely. A situation that is very easy to face when a commuter is planning a journey through a public transport system, direct connections are easier to remember and more convenient, while transfers require more planning and effort that one can consider unnecessary or tiresome. Briefly, it has been shown (Hillier 2005) that shape of the grid and the configuration of the urban realm is strong enough to shape the movements of the mass. Thus, SSX and its readings on how humans perceive the urban structure are not far from correct, even more are able to explain satisfactory a greater proportion of all potential movement with less intrusive and heavy collection methods Differences and Similarities To a normal car driver, cyclists, and pedestrians are the same type of moving objects, slow, fragile and in many situations with unpredictable behaviour. However, looking closely to each one of them, age ranges are wider for cyclists than for car drivers and even more for pedestrians. Inversely, the level of skills required, for instance, either an elderly person or a kindergarten child cannot operate a car, but instead they can walk primarily and then ride a bicycle. At the same physical limitations can lead to have lower skilfulness levels for each means of transport, very particular to each individual case. Since this research is focused on the mass of traffic, how every individual behaves is out of the scope, yet understanding how the mass of a certain type of user may interact with the urban environment will make a difference on how they can be considered similar in modelled network. Since links are the most important element in this network analysis, a detailed analysis based on three different facets of the urban environment namely, Function, Shape and Use. Explaining how their relations affect any user will lead to describe the most common similarities for every mass of traffic.

27 2-Spatial Network Analysis 27 Figure 15 Street Mobility Functions (Godefrooij, April 2009) Roads can have as described before by Sauter, two different types of pedestrian traffic, floating, and passing, as shown on Figure 15, there is a similar transition from a social use to a commuting use. Then, looking at the functions that can take place will define requirements of space, speed limits, signalling and infrastructure, in other words once we move from floating traffic to a majority of passing traffic, there will be less space and safety features for non-commuting activities. Dedicated infrastructure needs to be provided to allow those activities to happen; therefore, frequent changes on objects on the urban environment will occur like in their colours, sizes and occasional elements become distracters. Figure 16 Street Mobility Shapes (Godefrooij, April 2009) On the other hand, highways require a monotonous and predictable panorama, allowing drivers to quickly react to any distraction or change on the route. This will allow a safe increase of travelling speed, creating a funnel, a separation effect if underpasses or bridges are not in place to connect the living areas aside. Figure 17 Street Mobility Use (Godefrooij, April 2009) Based on road s purpose; it is possible to have a clear idea of how space for activities can be addressed, in terms of traffic controls and changes on infrastructure. Thus, defining how well connected and dense this social tissue can be, leading to the

28 2-Spatial Network Analysis 28 factors that will draw a border for every type of traffic and the right place for each user. The response to all exposed facets of the urban realm is very similar for all types of NMT traffic, since is least than thirty km/h in residential areas and there is enough physical separation in main roads between motorized and NMT vehicles. Dimension Specific to walking Specific to cycling Key differences Participants Almost everyone except some with mobility impairments. There are at least 3 different types of cyclists: a (advanced), B (Basic), and C (Children) Cyclists demand more specific environments, depending on participants or purpose; also require more physical skills (e.g., balance). Range/ Scale Mostly up to a mile (1.6 km) in length. The average trip length in the U.S. is 1.2 miles (1.93 km); between 47% and 60% of walking trips are less than 0.5 miles (0.8 km). Recreation/ work trips tend to be longer. Local and regional cycling. The average trip length is 4 miles (6.44 km) and 57% of cycling trips are less than 2 miles (3.22 km). Cyclists travel much further. Speed Depends on the purpose of trip; ranging from 1 mph (1.6 km/h) (dawdling) to top speeds around 4-5 mph ( km/h) for more active walking. Usually range from 8 mph (12.9 km/h) to 20 mph (32 km/h). Cyclists travel much faster. Infrastructure Infrastructure requirements for safe use include sidewalks (or paths, esp. for children). However, exemplary pedestrian environments may also contain attractive streetscaping. Can share roads with cars though with safety issues; lanes and paths are options; need infrastructure at destinations (parking, showers) Cyclists require more infrastructure at destinations Infrastructure planning responsibility Local land use planners, and transportation planners; also considered in subdivision layout and urban design. Engineers and transportation planners responsible for on-road infrastructure; parks and recreation planners for off-road. Responsibility does not always coincide, making coordination more difficult.

29 2-Spatial Network Analysis 29 Dimension Specific to walking Specific to cycling Key differences Trip purpose Transportation (including accessing other modes, e.g., parked cars, transit) and for recreation travel In the U.S., a clear majority of bicycle trips are related to exercise, health or recreation; cycling for transportation often plays a stronger role in many other cultural settings. Cycling primarily viewed as a recreational activity, at least predominantly in the U.S. Interface with transit Focus on the area around bus or LRT stops to make them pedestrian accessible and attractive for walkers. Require front racks or other means to accommodate bicycles. Requires parking at transit stops. Cyclists are more cost prohibitive to account for Interface with automobiles Mainly at intersections, but also any locale without sidewalks. Bicycles are often perceived as unwanted distractions in existing roadway space; conflicts also occur where trails intersect with streets. Cyclists often perceived to be competing for limited roadway space with automobile drivers. Key barriers Distance or perceived distance? Safety from crime or traffic. Distance. Safety from traffic. Cost of equipment? Safety concerns Crime (real and perceived); safety from traffic at crossings and on streets without sidewalks. Safety from traffic, particularly in narrow streets and at intersections with roads. Pedestrians are concerned about avoiding areas of high crime; bicyclists prominent safety concern often stems from automobile traffic. Table 1 Main Differences between Cyclists and Pedestrian in USA (LéGaré 2009) In Table 1, it is shown all items that can define the interaction between the users and the built environment as seen by LéGaré (2009), from a perspective completely developed and matured in Australia, where automobiles have a clear dominance as means of transport, therefore cannot be directly considered for other societies, but shaped based on the local preferences. In conclusion, Speed, Range and Reaction time to incidents, are completely different once speed increases, yet pedestrian and cyclists as commuter have speeds lower than thirty km/h, which is enough to control most the changes in infrastructure shown in Figure 15, Figure 16 or Figure 17. Hence, the differences between how these two masses need to be found in a complete different level. After looking at all these elements and based on Hillier assertions, there are not significant drawbacks for the

30 2-Spatial Network Analysis 30 theory of Natural Movement, orientation means, lines of sight, physical barriers and controls are still the same. Furthermore, LéGaré suggests that models for NMT prediction are based on traditional models assumptions that clearly are not accounting for different spatial distribution patterns, for aggregations that neglects a more detailed and robust model with smaller planning areas State of the Art SSX has been under development during last 30 years, evolving a theory that intends to explain how movement happens on the urban environment, considering the basic references for self-orientation. This is widely explore in all literature by Hillier, in which movement along straight spaces is more favoured than in complicated paths in which direction changes constantly. On the other hand, traditional transportation models are moving any further from classical theories established for motorized traffic in the early fifties. Thus, models followed the four-step Urban Transportation Planning Process (UTPP), (Weiner, 1997). By considering a set of separate elements that contribute to explain demand and offer of transport, interacting in an open market in which land uses generate or attract users, however the levels of aggregation for defining TAZ are too big for the demands of pedestrians and cyclists, thus always is considered as micro simulation. More recently, there are some appearances of Agent and activity base Modelling, however defining such agents and the chain of activities is still under research and it is segregated to either motorized vehicle modelling or pedestrians in intersection or closed facilities but not at urban level SSX Research Experience Raford and Chiaradia (2007) were the first in exploring the transference of SSX to cycling movement modelling. They highlighted not only the lack of research on the applicability of SSX as a traffic predictor but in cycling itself, and the existent research focuses on census data based models to try to uncover the most important factors for cycling as mode choice or infrastructure impacts on safety and traffic volumes but not for route choice. Literature suggests that appropriate infrastructure is the strongest argument for increasing cycling volumes, however if that infrastructure does lead anywhere but not to your destination nobody will use it. Therefore, route choice was explored, based on angular analysis and SSX, showing strong correlation between angular minimization routes combined with the presence

31 2-Spatial Network Analysis 31 of cycling lanes and GPS traces of cyclists in London s CBD. There are some important remarks from this paper to recall, as paths with the lowest angular depth are the easiest to remember and correlated better with the aggregated traffic. Thus, OD for individual traffic must be modelled in a different way considering that at disaggregated level users consider their route choice based on accessibility and how easy are those routes to remember and to follow, in other words, minimizing travel time and distance are not the main factors for such choice. Furthermore, leisure cycling must be considered as part of the studies including GPS tracing as a feasible source for this purpose. While McCahill and Garrick (2008) focused on how to establish a methodology for assessing the quality of the links within a network, they argue that a consistent model for addressing needs in a network can be easily done from Census Data and the SSX Indicators, more precisely Betweenness. In order to do such analysis FHWA has developed QUOVADIS-BICYCLE, which is simply a Four Stage traditional traffic model and MVCycle, which is another model, doing route assignment based on traditional transport modelling assumptions. Nevertheless, they found on their analysis these tools take only into factors related to the infrastructure like BCI and LOS, but on their conclusions, there is not a reference to the way the network is constituted, and mainly SSX has been used to found relations between safety concerns and their indicators, which is a different interpretation of BCI. Thus, all analysis related to SSX is based on the minimization of Angular Depth, developed by Turner, in which there are no drawbacks from moving their assumptions from pedestrian to cyclists movement. Finally, they conclude that SSX shows a strong value as a tool for addressing needs within the network, not only assigning traffic volumes but also to determine which links are more relevant or strategic for the whole system. Combined along with census data potentials for movement are much more accurate and can improve the performance of the spatial prediction in a more accurate manner Transportation Network Analysis A road network is a grid laid on the ground to connect different locations and allow users to move through it to reach destinations from several origins. Nevertheless, to make a transport network (TN) is necessary not only to guarantee connectivity to make the network a single body (more detailed as in section 3.3). Thus, a TN is characterized by links and nodes correctly connected, and by attributes that allows different modes of transport to use their designed links. In other words, rules need to be placed to ensure a swift and safe operation; along with this, a cost should be induced to model the real world conditions.

32 2-Spatial Network Analysis 32 Bell (1997) explains that a TN allows the movement of vehicles, cargo, or people throughout links and nodes where turns takes place, under a set of rules and costs based on the mode of transport selected. Classic theory emphasizes that route and mode choice is a function of the cost of completing such path. By comparing these to a normal network, transport users will obey rules like one way streets ; assume a cost as time, money, or any other. In the case of links will have a capacity maximum for its usage or traffic while a node, may have penalty on turning and certain traffic rules that may increase the travel time or cost to be evaluated. The structure of the network is clear, nodes, and links. The users can be divided on 2 different groups, Individual and Collective Transport, looking at the literature, refers always as a mode of transport to any vehicle motorized or non-motorized, however the usage of bicycles roller skates or walking are not considered as significant as private vehicles in the case of individual traffic. In resume, the supposition is that a commuter will always use a vehicle and a common network with the explained rules above. Once network and mode of transport are defined is necessary to find a balance between the network capacity and the demand of the users, in other words traffic is very similar to a market economy, therefore is required to establish a set of procedures to balance such demand and offer. In Section is widely explained how links are attributed with costs, time, money or any other penalty that will lead to a rational and economical decision pattern from the user. User Equilibrium tries to model the way that users perceive costs and restrictions to achieve their objectives, in this case to move from one origin to a destination with the minimum cost. Deterministic and Stochastic theories have been developed to find a balance between the users preferences and what is truly available on the network. This economic model mainly refers to two variables time and money. Users will try to maximize those two resources to achieve maximum mobility with a fixed budget for both. However, current models and theories that tries to achieve this balance, are clearly not considering all the externalities generated by transport, those are marginally included as components on the current economic models, and as result ignoring a major drawback on the traditional models, a topic in discussion that is out of the scope of this work Transportation Modelling Pucher and Buehler (2009), follow a similar line to traditional approaches, in which improvements on road design and infrastructure are the most important factors for increasing cycling traffic volumes supported by a heavy group of hard and soft measures that ensures higher safety for cycling users and press car drivers to merge

33 2-Spatial Network Analysis 33 with cycling traffic in a swift fashion. Also is remarked the fact that changes on land use avoiding long commuting distances and urban sprawl favour widely the use of the cycle as a substitute for motorized vehicles. On the same direction Jacobsen (2009), argues that use of segregated infrastructure is the best way to increase the use of the bicycle as means of transport. However, none of them point out quality and connectivity play a major role on making possible to reach any destination from every origin on the network, neither what is the role of a particular link within the system. The FHWA has been working on this direction, doing evaluations of the current technologies to increase the awareness and knowledge of cyclists and pedestrian movement. However, conceptions of a motorized transport culture are hardly breakable if cycling and walking are still being shown as means of leisure and sport rather than a real option for increasing mobility in an environmental friendly way within urban environments. At the same time, thoughts on the traditional models does not account for all the variables that leads decision making for NMT as a mobility solution. It is shown that microeconomic hypothesis minimizing travel time and distance are not the main factors, on Figure 18 are shown several factors that combines relations between hard and soft measures that have a strong influence on NMT. Figure 18 Relations between factors influencing NMT travel (FHWA 1999) By considering and analysing these factors is possible to have more clues on how to model in a disaggregated fashion NMT. For instance, TAZ must be significantly smaller, as users can vary from block to block; moreover, when traditional TAZ are defined many of these NMT trips will be inside the very same TAZ. Something similar happens to demographic indexes shaping a city, these kind of aggregation from blocks to city districts induces noise and reduces the efficiency of models that account for single individuals on the move, a common obstacle for modelling high spatial resolution data, the acquisition of the same and the concerns with privacy.

34 2-Spatial Network Analysis 34 Taking a look at factors exposed on Figure 18, it is clear that environment, soft measures, and infrastructure, before, during, and after the trip are major concerns, yet Network is finally considered as an important one. If one cannot reach from its own doorstep to his or her destination, by using those means of transport, will take another that will do it. In 1999, FHWA did a review on the methods and modelling techniques available for forecasting NMT, since that date there is no update on this document. Yet, it is a good resume of the available techniques, which have been improving their resolution thanks to the increase computing power available to manage bigger models with high and dense attribute structure as the one shown on Figure 18. The following is a resume from Guidebook on Methods to Estimate Non-Motorized Travel: Overview of Methods, regarding these methods for forecasting traffic demand. Demand Estimation: Methods that can be used to derive quantitative estimates of demand. Method Comparison Studies Aggregate Behaviour Studies Sketch Plan Methods Discrete Choice Models Regional Travel Models Description Methods that predict non-motorized travel on a facility by comparing it to usage and to surrounding population and land use characteristics of other similar facilities. Methods that relate non-motorized travel in an area to its local population, land use, and other characteristics, usually through regression analysis. Methods that predict non-motorized travel on a facility or in an area based on simple calculations and rules of thumb about trip lengths, mode shares, and other aspects of travel behaviour. Models that predict an individual's travel decisions based on characteristics of the alternatives available to them. Models that predict total trips by trip purpose, mode, ODand distribute these trips across a network of transportation facilities based on land use characteristics such as population and employment and on characteristics of the transportation network. Table 2 Demand Estimation (FHWA 1999) Relative Demand Potential: Methods that do not predict actual demand levels, but which can be used to assess potential demand for or relative levels of non- motorized travel. Method Market Analysis Facility Demand Potential Description Methods that identify a likely or maximum number of bicycle or pedestrian trips that may be expected given an ideal network of facilities. Methods that use local population and land use characteristics to prioritize projects based on their relative potential for use. Table 3 Relative Demand Potential (FHWA 1999)

35 2-Spatial Network Analysis 35 Supply Quality Analysis: Methods that describe the quality of non-motorized facilities (supply) rather than the demand for such facilities. These may be useful for estimating demand if demand can be related to the quality of available facilities. Method Bicycle and Pedestrian Compatibility Measures Environment Factors Description Measures that relate characteristics of a specific facility such as safety to its overall attractiveness for bicycling or walking. Measures of facility and environment characteristics at the area level that describe how attractive the area is to bicycling or walking. Table 4 Supply Quality Analysis (FHWA 1999) Supporting Tools and Techniques: Analytical methods to support demand forecasting. Method Geographic Information Systems Preference Surveys Description Emerging information management tools, with graphic or pictorial display capabilities that can be used in many ways to evaluate both potential demand and supply quality. Survey techniques that can be used on their own to determine factors that influence demand, and that serve as the foundation for quantitative forecasting methods such as discrete choice modelling. Table 5 Supporting Tools and Techniques (FHWA 1999) Similar classification and remarks were found by Porter (1999).In his paper an analysis of current methodologies for forecasting NMT demand and network qualities was done. Yet, in conclusion, BCI and LOS or Levels of Stress are not standardized, therefore, it is not completely defined or agreed to support an appropriate choosing methodology for friendly road infrastructure. Neither a clear combination of all these factors as a complete and integral measurement of the real impact of each one on the final forecasting of the demand. On the other side, in Cycling-Inclusive Policy Development: A Handbook (Godefrooij, April 2009), there is complete development of all the factors exposed before, how they need to be addressed to acquire a system of NMT that is integrated into the current public transport system, allowing commuters to choose the mode more convenient for each individual. Finally, yet importantly, several institutions are making major efforts, like GTZ, Dutch and Danish Cycling Embassies, all together are promoting the methods that they have used on their own cities to measure and forecast more efficiently demand and supply. However, there is still needed wide research and close supervision on these methodologies applied in different environments to achieve data and complete cycles of research and implementation that will confirm the assumptions made from empirical evidence.

36 3-Methodology Methodology Building an Urban Network model that allows the calculation of spatial indicators like the ones developed by SSX requires spatial data to describe urban space while for a Transport Model requires many more sources to complete all attributes that constitutes its own restrictions. As discussed in section 2.4 transport is seamlessly related even concealed sometimes with daily activities and many more factors, thus statistics regarding these factors and the share of every transport mode is simply not enough, there are more economical, socio-demographic and environmental variables linked to this matter. It is the interest of this research to model as close as possible reality of aggregate cycling traffic in Munich; considering time and financial limitations, there will be a number of assumptions to be taken and supported on previous research that the author considers feasible and validated by the Supervisors. Figure 19Main Steps in Spatial Network Analysis To accomplish the aims of this research work, there are four stages defined as shown on Figure 19 and detailed sub stages (1A, 1B,, etc.) and steps in Figure 20. First, it is necessary to define a subject of research, as in section 1.2, Munich was introduced as a urban and hence Study Area, allowing the selection of a more focused Research Area, in which cyclists gate counts are available, with a belt around the research area, chances for having edges effect on the calculations are minimized. On the Second Stage Obtain Data, it is necessary to establish requirements as a border for contacting data sources. Once there is clarity, appropriate open and official sources for spatial and statistical data must be found, contacted and requested, then it is provided one must verify data quality, this as a normal measure to ensure integrity and coherence with the selected study area. Model Building stage, is the moment when all data is placed in only one repository, network created and link connectivity an link attributes, are verified so there is only one network without sub graphs, buckles or any disturbance that can affect the performance of the analysis. The whole network must have a single link between two nodes, thus by doing a topology check this can be ensured unless it is required as it

37 3-Methodology 37 is in reality. Finally, the model must have a network attributes in each link to allow an accurate analysis and classification to be used in further transportation analysis. Figure 20 Detail Steps on Network Analysis Last but not least, the model needs to be assessed, this in a qualitative manner through Interview with Experts and then in a quantitative scientific manner on Regression Model Defining a Subject In Section 1.2 Munich was introduced as a city with a developed cycling network and cycling culture. These two factors are decisive for providing spaces for developing safely and comfortable, significant numbers of cycling commuters. Based on these assumptions the city was analysed, with a Transport Development Plan considering this as a means of transport (section 1.2.1), means that there is a clear set of legal elements that frames bicycles as a vehicle and not a tool for sports or recreation only. This in other words means, attach some rights and duties to cyclists and commit them to use specific roads. Besides rules for behaving on the street and a set of limitations for road usage as explained on section 3.2.1, there are political and administrative limits where those rules apply. Along with geographical barriers and structural barriers may create a border for the network. In the case of Munich, the external ring formed by Autobahn

38 3-Methodology 38 arriving to the urban area of the city, which matches the administrative limits therefore became the limits for the OSM data extract showed on Figure 30. The research area would be defined based on the statistical data acquired; therefore will be a sub set of the study area, allowing an appropriate statistical extension for final assessment Obtain Data In order to build an accurate model that represents the cycling network in Munich urban area, it is necessary to acquire spatial data with a geographical datum that will maintain fidelity in northing and easting. Moreover, as the network requires attributes to ensure correct usage of the transport analysis tools, a reliable link hierarchy and classification must be enforced. The collector or recipient for all this data is the local government. Their departments mainly use all this data for planning and construction purposes, which are the executive level of any governance strategy, and hence the main input for achieving a successful implementation of any policy. Moreover, Radlhaupstadt and München Rathaus developed the Fahrrad-Routenplaner für München as shown on Figure 21 (Source: which is a web based tool that allows users to plan in advance their cycling routes along the city by given an origin and destination addresses, then giving some criteria for selecting an appropriate route. Figure 21 Cycling Route Planner München.de This is a web based GIS service, which is based on the same kind of spatial data to feed the model of this research. As it allows routing, it is an attributed network describing the existing infrastructure, thus can have a cost function defined by user based on criteria selection and consequently giving a specific route as answer to user s request.

39 3-Methodology 39 The natural sources for these kinds of data are clearly the Local Government, Planning authorities and surveying institutions, for Munich: VEP, VEP-R, Konzeption Fahrradparken, Bike+Ride, Zählungen, Öffentlichkeitsarbeit: Referat für Stadtplanung und Bauordnung (VEP, VEP-R, conceptional planning of bicycle parking, Bike+Ride, counts, public relations work: Department of Urban Planning) Radfahrverbindungen, Fahrradwegweisung, Infrastruktur, AK Radverkehr, Fahrradstellplätze: Baureferat (Cycling network and infrastructure, signposting of bicycle routes, bicycle parking, Committee for Bicycle Traffic: Department of Public Construction) Fahrradstraßen, Einbahnstraßenöffnung, Mobilitäts- management, Marketing, Lichtsignalanlagen: Kreisverwaltungsreferat (Bicycle roads, contra flow cycling in one-way streets, traffic lights, marketing, and mobility management: Department of District Administration) Radlstadtplan, Umwelt, Gesundheit: Referat für Gesundheit und Umwelt (Munich Cycle Map, environment, public health: Department of Public Health and Environment) A detailed explanation of the required data is presented in Table 6 following below. Apart from the data required for this research work, it is detailed in which network what data is required as supply, therefore when is present, such data is required, and when is present would be otherwise. Data Type Data Item Data Source Cycling and Pedestrian Master Development Plan. Urban Model Transport Model Legal Frame Maps with dedicated signposted cycling network Local Government Traffic Rules for Motorized and Non- Motorized Vehicles Road Network as lines, attributed (1). Cycling Network as lines, attributed (1). Spatial Public Transport Network as lines, attributed (1) Public Transport Facilities (Stations, Park and Ride, e.g.) as points or polygons, attributed (1) Local Government / Open Street Map Street Environment (Sidewalks, Squares, Ramps, e.g.) as Polygons, attributed (2)

40 3-Methodology 40 Data Type Data Item Data Source Buildings and Constructions as Polygons, attributed (2) Natural Environment (Lakes, Parks, Forest, Swamps, e.g.) as polygons, attributed (2) Urban Model Transport Model Rivers as lines, attributed (2) Traffic counts, covering several, several locations across the city, with geolocalization, time stamp, aggregation time and modal split. Statistical Records from Transportation Survey as trips for the city area with, trip length as time and distance, purpose, starting and finishing date or time. Records from Census Data as records describing main demographic indicators for the smallest planning unit possible (City District, Block, e.g.) with land use, density use, age, gender and vehicle ownership Local or National Government Origin Destination Matrix, with the trips for each mode of transportation including cycling for the smallest possible planning unit. Table 6 Data Requirements for Model Building Note 1: Attributes describing right of way, allowed way, speed limits, physical, geometrical, hierarchical, and geographical characteristics of each segment, however not required for SSX Model. Note 2: Attributes describing physical, geometrical, hierarchical, right of way, and geographical characteristics of each element, however not required for SSX Model Spatial and Geographical Data Street segments are elements highly related to their geographical location, geographic accuracy is required for building a plausible transport model, which will reflect the reality in a simplified manner. The simplifications considered in Section for representing the street roads are already included on almost every single mapping application recognized by any cadastral or geographic authority. In this research work due to the fact of the extension of Munich, the spatial data will be referenced to a Global Coordinate System to provide a credible base for calculations related to space and distance.

41 3-Methodology 41 Other data related to locations of interest, public transport stations, or areas as buildings or even administrative borders will be consequently projected on the same coordinate system to ensure congruence and integrity. Colours and styles used to represent this data on a GIS will be exclusively to this research in particular; they will follow the most common mapping and cartographic practices, for readability and legibility. In Section 3.2, local VEP-R and Department of Public Health and Environment, were contacted to acquire such data, however on the time frame planned for that purpose the data was not acquired, similar constraints were faced with private providers, therefore OSM raised as an option to consider and analyse carefully Open Street Map Open Street Map (OSM) was founded in 2004 (Open Street Map, 2012), as an open collaborative method for constructing a global map in the Internet. This service runs under an open source agreement, which allows an infinite number of users to upload, edit and verify the data that has been added to the global map. This data, follows a number of rules and codes that allows it to be shared and edit it in any platform or language, however requires a reasonable level of expertise and self-training to complete the process in a satisfactory way. Figure 22 Access Hierarchy based on mean of transport (OSM) The way that this map has been conceived allows the input of data of any kind, segregated in categories and sub-categories that answers to a predefined system of tags and attributes, therefore all tagged as highway either lines, points or polygons which regards of transport, are the kind of source required in this research for building a sound transport model. Besides all data integrity and collection, this can be used for routing purposes, several services on the Internet like Nokia maps ( allows users to make use of OSM data and get directions to get to their destination. This kind of facilities from third vendors probes that OSM can be used for Transport Modelling, however how much processing needs to be done is yet to be determined.

42 3-Methodology 42 Routing is determined by the network and the vehicle to be considered on the problem, as seen before access needs to be established in physical and legal form. This means that vehicles can overcome different types of terrain or surfaces. Depending on the local laws, a specific vehicle is able to use a defined set of street links. On Figure 22 (Source: it is explained vehicle hierarchy and how access is given for street links usage, following it on Figure 23 (Source: Restrictions#Germany) is explained how depending on the vehicle chosen for the routing, street links can be crossed and used. Figure 23 Legal Access Restrictions by vehicle and street link type (OSM) This classification and restriction will be considered for the GIS-T model, for instance a cyclists cannot be in a motorway or on a footway, giving a limitation for paths and solutions of traffic assignment. This data is embedded on the street links attributes for further interpretation on the network data set Statistical Data Traffic counts are the main input of plain data for this research, tables collecting in a chronological order the number of cyclists in selected locations. Therefore, this data will have an established order that can be traceable and from which statistical descriptors can be obtain and verified easily. In order to acquire such data Munich s VEP-R was contacted, initially a set for seven locations with automated counters was provided. Based on the Literature Review a census data based model is a reliable way to explain the patterns of movement as it accounts for the most general attraction and generation activities and hence their locations. This census data is provided in Germany by the BMVBS, this through the Mobilität in Deutschland, on its latest

43 3-Methodology 43 release for 2008, is complete sample of the movement patterns in Germany and Munich Automated Traffic Counters Since 2008 these devices allows daily automatic collection of traffic data, counting cyclists crossing the sensors every fifteen minutes, 24 hours a day, instantaneous transfer and processing for storage. The counters were installed in seven different locations around Munich (Detailed on Appendix D): Figure 24 Sensor for Bicycles (Source: Schuh & Co.) Each one of them is equipped with a pressure sensor like the one shown on Figure 24 that sends an electric pulse every time there is enough pressure for joining the two ends of the tube sensor; this tube is located perpendicular to the main axis of the road across the lanes. A detector as the unit shown on Figure 25 process the sensor signal filters it and convert it into a codified message with a count of bicycles, time stamp, serial number and additionally there is another weather sensor that records temperature and sunlight level and embed it along. Figure 25 GPS Communication Device for Bicycle Sensor (Source: Schuh & Co.) Once there is data compiled for a predetermined time, a data package is sent to a remote location through a GPRS connection, similar to the ones used in mobile telephones. The server in the other end receives the data package and provides support by checking the integrity of the data and additional information that allows a remote diagnostic of the operability of the detector Manual Traffic Counts Twenty-Three locations were selected based on those seven permanent as shown on Appendix D, as they cover in two axes the city, north to south and east to west, allowing a quick pick on the cycling traffic across the Old City Centre and the urban area inside the Mittlerer Ring. These locations have counts for cycling traffic, made all by hand on different dates on periods of six and four hours during dawn block (00:00 to 06:00), morning block (06:00 to 10:00) and afternoon block (15:00 to 19:00).

44 3-Methodology 44 These values will serve as input for the calculation of the Daily, Season, and Annual Averages; along with location conditions, weather as rain or no rain, date and timing blocks, the traffic averages will be calculated by using the tool to be explained on section Projection Model of sample counts for cycling As explained on section , cyclists counts are taken on intervals of fifteen minutes, which leads to a very big number of readings per day and even major per month or per year. This is not practical for planning and analysis purposes. Transport modelling techniques suggests that daily, season and annual averages for traffic are accurate indicators to undertake those procedures without inducing a significant error due to aggregation. Schiller (2011) and TU-Dresden developed a model based on automated traffic counts and the data set from Mobilität in Deutschland (BMVBS, 2010), accounting the effects of different person groups, activities, weather, rain, season and topography. This model was packed on an Ms Excel tool that allows user to input short time counts, person group share, time of the year, and details regarding the structure of the street network surrounding the counting location, in order to estimate average volumes for several time frames, like day, week and year. Every count location was analysed by using this tool in order to get standardized values that can be comparable in certain manner. The graphs and tables with the results are part of the Appendix F Building Model Based on the spatial data input, which for this research work will be OSM, and after the usage of OSM2NDS, a geographically oriented network was built. Every link in the network will possess a series of attributes that will reflect the hierarchy of the street network and some of the physical and logical characteristics that will be used later on for modelling access restrictions, costs, and usage of the same for several vehicle types Urban Network A SSX network model requires a series of elements as described before nodes and segments. Centre line simplification, as the one used in mapping is valid (Turner 2007) for performing an Angular Segment Analysis. The scope of this research work demands the analysis of an urban area, in this case the city of Munich. The city has embraced a transport policy that favours NMT transport and provides not only segregated high quality infrastructure but soft measures that educates and raise the

45 3-Methodology 45 awareness of users for selecting routes and behaving in a predictable way that can be easily interpreted by other mobility agents and reduce the chance of conflict or even fatal accidents. All the street elements in Munich can be eligible for cycling, however German traffic law ( 2(4) Straßenbenutzung durch Fahrzeuge) forbids cyclists from pedestrian areas, Autobahn and other high-speed roads. Therefore, all physically accessed roads can be used for cycling. This a vast quantity of roads, based on the network developed in Munich, there is already a classification of the infrastructure on those roads built specifically for cycling commuting. Figure 26 Münchner Radlstadtplan (Radlhaupstadt 2012) The current infrastructure considers single and double lanes. This allows single and double way traffic; surface can have pavement on roads or gravel in green areas, and signposting can be present or not, depending if the road lies in one of the main routes previously discussed; this leads to 18 different types that were coded on the street network used for modelling, as shown on Table 7. Two types were considered, T30 for all the roads part of Tempo 30 Areas. Finally, Motorroad, which includes all roads and streets than can be physically accessible by bicycle but cannot be used by cyclists, like Autobahn or Pedestrian streets. Lane Surface Share Signposting Symbol Code Double Gravel Exclusive Shared Clean Signposted Clean Signposted dgec dges dgsc dgss Road Exclusive Clean drec

46 3-Methodology 46 Lane Surface Share Signposting Symbol Code Signposted dres Shared Clean Signposted drsc drss Single Gravel Road Exclusive Shared Exclusive Shared Clean Signposted Clean Signposted Clean Signposted Clean Signposted ogec oges ogsc ogss orec ores orsc orss Double Asphalt Shared Signposted T30 Double Tarmac Exclusive Clean Motorroad Table 7 Cycle Lane Classification On Table 7, a detailed classification used as basis to select the roads that can be ran by bicycles. In the column Lane is stored the possible directions for a street link, hence single of both ways are possible. Column Surface refers to the type of road s surface, four types were found, road or gravel were identified from Münchner Radlstadtplan as shown on Figure 26. Additional types Asphalt and Tarmac were used as dummy types for allowing further processing on the NDS. Column Signposting identifies is there any shielding offering information to cycle users. Symbol column collects the representation used on GIS. Finally, on Code column the label used on the geodatabase for further processing, results of these classifications can be seen on the Appendix C. As previously discussed, the source for spatial data in this exercise will be OSM, the line elements with the tag highway will be considered as feasible parts of the network for analysis. This main network needs to be filtered, based on the grounds that, there are regulations that allow other mobility agents to interact on the road in an organized manner, therefore access restrictions will be evaluated for choosing such roads, following a predetermined road hierarchy. Physical and Legal restrictions will be considered for modelling Cycling Traffic in this exercise. There is special attention to all roads which are allowed for cycling but that does not have any legal restriction, for instance all roads in areas with Tempo 30 or traffic

47 3-Methodology 47 calming measures were included, does out of this classification were not, as they usually are laid in industrial areas or roads with heavy traffic. Figure 27 Topology Rules on the NDS The selected method of building an urban model stems from transport modelling and is very detailed. It considers the classification of the road infrastructure as key determination for cyclist traffic. In this way, it is different from a traditional Space Syntax approach where visual access to the urban space plays the key role, not the classification of the space. This thesis will test, which approach is more relevant for modelling a cyclist network Link Connectivity and Topology Check Links and nodes compose the NDS; therefore, between those two there are relations that need to be audited to ensure a proper connection among them. As OSM offers the facility to a multitude of user to make inputs, not all of them have the same standards. Ensuring uniformity in the whole network is crucial to allow the Network Analyst to do an impartial assignment. At the same time is important to ensure that all the links are connected in the right place under the desired precision. Under the schema of ArcGIS, it is possible to define topological relations between nodes and links, to ensure appropriate connectivity and placement; an example of this is shown on Figure 27 (Source ArcGIS Help). A set of rules was defined for software to find errors by analysing the structure of every link and how they are related. In this case it is necessary to connect links only at end nodes which means no gaps in nodes, no overlapping, no self-intersects, and a special case in which between two nodes there should not be more than one link, thus in case there are two or more links they should be merged.

48 3-Methodology Link and Network Attributes Once the structure of the network has been verified, it is necessary to ensure that the attributes for each link are properly assigned, this is vital for further calculations that depend on those values, for instance based on class hierarchy maximum speed can be assigned, an incorrect assignment can lead to erroneous selections. OSM2NDS generates from OSM tags the attributes for every link as shown on Appendix H Table 16, those allow the logic in the NDS to recognize street hierarchy, to assign the proper access to the vehicle of interest in every solution and hence to review limits for speed, height and traffic calming areas Transitability and Accessibility As seen on section 3.3.1, building a network for a SSX analysis for a dedicated mode of transport is not a very simple task. Since there is not an official source for the data, and the source data is an open collaboration project is necessary to define to types of access or use of the infrastructure. As defined in OSM Access Restrictions Section, roads can have limitations due to their usage characteristics. For instance, in Autobahn is possible for any type of vehicle, motorized and non-motorized to run, since there is no physical barrier or impediments for the vehicle to reach the road and use it. Which accomplishes to the definition of accessibility, however, that specific road is not transitable for certain types of vehicles, namely non-motorized ones, because of safety and comfort for the rest of the users. On the other hand, in residential roads heavy trucks can run on some desirable hours, therefore they can access those roads physically speaking, yet if they are too heavy, too wide or too tall they may be forbidden from using some roads, even because of noise can be forbidden from certain areas. Therefore, it is necessary to establish some rules to build a consistent model based on those two premises, accessibility, and transitability. To define which barriers must be honoured and which can be overridden, which roads can be used and which ones need to be forbidden Transport Network Similar to the process to build a network for SSX as explained in section 3.3.1, road elements on the urban realm will be simplified as links and the meeting points of those will become nodes. Once the elements are in place, links and nodes can be attributed, direction restrictions, length and capacity. Those are basic attributes that lead to a number of arguments, part of a rational decision process.

49 3-Methodology 49 Based on those arguments user will move through the TN, thus a flow will occur between origins, which are the sources for commuters and destinations, which are a selective group of sites that will attract those commuters and will invite them to come. For instance, students will start their trips at home, and will have as destination a school or university, likewise with workers with their homes and working locations. How detail are those will depend on the resolution of the available data and disaggregation level of the zones of study and its extension. In conclusion, graph theory defines a network as a set of nodes and links interconnected, allowing the application of topological rules and further calculations. Up to this point, a TN will have the same structure, nonetheless a deeper collection of attributes that allows applying a rational and economical model, intended to achieve equilibrium when demand is faced and it is necessary to allocate scare resources. As there are several modes of transport specific infrastructure has been placed for each one of them to circulate without major conflicts. These multiples types of roads are considered and should be modelled with a clear hierarchy and accessibility restrictions to ensure safety, desired levels of service, and consequently the expected capacity. Based on these motivations, it is necessary to ensure that all infrastructure is modelled with separate links, for instance a highway with parallel service lanes, cycle lanes and sidewalks, serve to different modes of transport, and consequently a minimum of four parallel links must be modelled, if there are restrictions of direction the number will be doubled SSX Indicators and sdna As was explored before on section 2.1.2, SSX and Natural Movement developed by Hillier require a massive amount of calculations, which implies a massive number of elements at a time. This was a major drawback to refine theory and find fields for applicability. Development of GIS and computers allowed more daring software and networks to put this theory into practice. Alain Chiaradia, an active member of the SSX Community has developed sdna, (Chiaradia, 2012) which is a plug-in for ArcGIS, a very simple application that runs as a toolbox and allows first to review the network structure and secondly to calculate SSX indicators. Taking advantage of ArcGIS capabilities sdna is ready to process a massive network in matter of hours and to calculate SSX, Centrality measures, Network Detour, and Shape analysis.

50 3-Methodology Analysis Options In section was discussed the main indicators of the SSX analysis for spatial networks. Among all the indicators that can be calculated with sdna, a brief analysis of their definitions leaded to choose some of them for further trial against the traffic counts, those are shown on Table 8 below. Highlighted on Red colour the main two spatial indicators Indicator Name Measure name Definition Mean Angular Distance Mean Angular Length Closeness Centrality Betweenness Centrality Two Faced Betweenness Centrality Mean Ang/Euc distance MeanGeoLen Ang/Euc NetQuantPD Betweenness Ang/Euc TPBetweenness Ang/Euc Mean geodesic in the radius from link x Mean of Euclidean/Angular/Custom geodesic Euclidean length in the radius. Network Quantity Penalized for Distance: Destination weight / geodesic for all links in the radius Sum of geodesics that pass through a link x Two phase betweenness: sum of geodesics that pass through a link x, weighted by the proportion of network quantity accessible from geodesic origin y that is represented by geodesic destination z. Street Segment Count Links Number of links in the radius Node Count Junctions Number of junctions in the radius Table 8 Indicators to be assessed Despite the fact, sdna calculates a wide variety of indicators, based on the literature research and the practical experience the indicators on Table 8, will be statistically analysed against empirical data. Thus, several cases and combinations will be contemplated for this analysis. Those combinations are listed on Figure 28. Figure 28 Analysis Options and Cases The space for the analysis can be considered in two different manners, either discrete or continuous. This means that when a half or less of a link falls within the desired radius, this element in the analysis border will be considered for the analysis. On the other hand, a continuous space will take that fraction of the link that lies within the desired radius. Weight, means simply that the results will be weighted with a third

51 3-Methodology 51 variable, for instance the simplest one can be planar distance, thus each result of the analysis for each indicator will be divided by this selected weight. Spatial indicators calculated in sdna, can be measured in several ways. As Angular or Euclidean measure, this means that the main metric for the analysis is a traditional distance measure like in a Euclidean space or Angular, in which the metric to measure is the angle between one link and the following one. Therefore, it is measured the trip length or the total spin compared to the initial angle orientation, in other words how much one has turned to reach a destination from the point of interest. For this case the Euclidean distance will be measured in linear meters, while Angular will be measured in decimal degrees, units that are congruent with the data obtained from OSM and stored in a geodatabase Benchmarking As usual in any transport research, empirical proof taken from the streets will be used to measure the performance of the model and how this is able to replicate the results in a consistent way. Moreover, since research for cycling traffic modelling is in such early stage and depends on the experience and judgement of experts, has been advised to conduct interviews with local experts Experts Interview After the cycle network model was completed and verified, SSX indicators were calculated by using sdna. With the results, a thematic map was prepared (as seen on Appendix J). The colour range for the links in the network is a simple classification on deciles, in which the lower values are coded in blue and higher values following a rainbow type colour scale to red, which has the higher values. The map was based on the results for Betweenness at global radius, which means no radius was selected, assuming the entire network will participate in the analysis. This is a first approach strategy in which identification of particularities on the network, radius, and areas usually used for cycling. The first step was to locate a permanent starting location, most of the cases their own living location, in some others the working location. From this central point the subject marked with a loop the area of the city that usually rides, for each case was different, and fitted with the fact of the most well know neighbourhood, the area of confident cycling. After a starting location and boundary were set, subjects were asked to highlight with two different colours cycling traffic. Defining Low and High Cycling traffic thresholds

52 3-Methodology 52 was a complicated task, as the perception of every single person interviewed was completely different. This leaded to a workaround; paraphrasing the question as please highlight the roads with more and less cyclists, which was more clear and simple to answer. After rating the roads based on their experience, some subjects commented their own cycling experience along those roads. Certain statements shed lights on adjacent roads and paths that are recommended or more used based on subjective needs and preferences. Moreover, subjects were able to explain some patterns of movement and appointed in most of the cases that the performance SSX indicators on the assessment of the cyclists network were very close to the reality. All statements and comments were registered and they can be found on Appendix K. The interview subjects are persons who use daily a bicycle to commute in Munich urban area; doing this for at least last ten years, members of any kind of governmental or nongovernmental institution related to cycling. Matching these criteria were found and contacted: ADFC ( Radlhauptstadt ( Green City e.v., München ( Stadtradeln München ( Kreisverwaltungsreferat Mobilitätsmanagement, Fahrradmarketing Once contacted by mail and after getting a positive answer, the interview was done and the dialogue started. Outputs are collected on the Appendix K, after analysing them those were used to improve and tune the cycling network, by checking attributes, detailing intersections and links that may hamper the performance of the model Comparison Method Traffic count locations are the main points for testing the model, since they are finite and spatially distributed this aiming to reduce any biased or trendy behaviour. A table matching the counting gates with the values calculated from SSX will be used as final source for the statistical assessment. Literature suggests that statistical methods are the most reliable way to assess the performance and the likelihood of the readings of one method to explain in a mathematical way the results in another. Therefore can be considered that cyclists gate counts are random continuous variables. Figure 29 Steps for Statistical Assessment

53 3-Methodology 53 By taking, the seven indicators to assess, shown on Table 8 and its results for each radius, will be spatial joined with the cyclists gate count. These means that based on location the values on the links will be assigned to cyclists gates; therefore, they will maintain spatial coherence with the values calculated for each link on the network. This will generate a matrix with values for each cyclists gate, those will be analysed on a correlation matrix following a traditional Least Squares (LS) method to find which indicators are able to explain most of the empirical counts. After selecting which indicators present the highest correlation value with the cyclists counts, their radius will be refined to ensure the maximum correlation possible. After this is done, SSX indicators will be taken as independent variables, traffic counts, and traffic assignment values as dependent variables. A linear multivariate regression method will be applied to obtain a model equation; based on the r 2, several combinations between the top correlated variables will be done, this for each radius, in order to find the highest r 2. Once all models have been built, the ones with the highest fit will be tested to check their mathematical correlation and probe the fit that they got in the step before.

54 4-Model Construction and Output Model Construction and Output As explained in Chapter 3 above, there are several steps to follow to complete the milestones of this research. During the process many times challenges were faced, those are explained in this section, as grounds for taking further decisions that will shape the developments proposed in Chapter 3 above Challenges on Data Departments of the Local Government of Munich were contacted on June 2012, as example in Appendix A, a mail contacting VEP-R asking for support and guidance on how to obtain such data. After several communications Cyclists counts were obtain, however Spatial and Geographical Data was not obtain, thus a work around is explained in Section Private data providers of navigation solutions like TomTom ( who produces maps, able to contain navigable and routable networks, or GPS tracking companies as Endomondo ( collects GPS traces of users that can be used as empirical counts, were contacted. Network spatial sources and statistical empirical data sets, two parts that works together for the purpose of this research, yet they have commercial purposes as well. However, after exchanging mails with more than 15 companies dealing with this kind of data, anything could not be obtain as there are high monetary charges that could not be afford by this research project. Therefore, OSM is a common repository of spatial attributed data; this kind of required data can be extracted and converted into any other format that a GIS can process. This process will be followed with the data for Munich, since editing the geographical from OSM, is not of the interest of this research, an extract will be made and converted into ArcGIS Shape file format for further edition and calculations. OSM allows several and even user extractions, thus several web services for data retrieval were tested, GeoFabrik ( CloudMade ( bbbike ( which provide daily or weekly dumps of metropolitan areas for further use in several GIS file formats. After checking their data output, there is a clear lost of the tags available in OSM, therefore all the input made by users is partially lost which means that any of those tags lost may need to be added once again into the attributes for each segment that will be part of the model.

55 4-Model Construction and Output 55 The process to take data from OSM and make it usable for a SSX or a transport model is very detail and extensive. As explained right above OSM relies on the contributions of millions of users around the globe, something that cannot guarantee the unity of concept and consistency of classification and attribute assignation. On the same sense, the digitalization of links or in other words the drawing process of the links and nodes suffers the same type of drawback. The digitalization process for links can be done in several manners. Always is desired to have one start and end node when there is a straight line, with no vertexes in between, yet in curves those vertexes in between are necessary to maintain the required radius. However, since this is done from satellite images there are many sources for error. After digitalization is complete, every link needs to be assigned with values on all attributes, most of them are filled on the tags from OSM, nevertheless once again, and subjectivity of the users doing this may lead to wrong classifications Spatial Network An OSM dump for the city was taken on July 25, 2012 from the download section of Geofabrik ( Such dump lies between the coordinates shown on Figure 30, which cover the urban area framed by the external Autobahn ring, which creates a barrier effect and enough separation for considering it as a natural limit for the urban road network. Figure 30 Location of Munich City as in Open Street Map web site, with limits 48º232N, 48º022N, 11º763E and 11º390E. This dump was consequently processed with OSM2NDS (Open Street Map to Network Data Set) as seen on Figure 31, a tool created by Eva Zimmermann on the course of her bachelor thesis (Zimmermann, 2010). This tool takes data dumps from OSM and by using a XML file as definition for the transport network, creating an ArcGIS Network Data Set (NDS).

56 4-Model Construction and Output 56 Figure 31 OSM2NDS processing a dump OSM file for Munich Any GIS-Transport model requires a NDS, because the connectivity (as in section 3.3.2) and rules derived from it allows the usage of the attributes collected from OSM, is possible to explore the road classification and do a selection of the roads that will participate in a specific traffic analysis for GIS or lately for SSX. NDS allows to solve in a replicable way different transportation problems, as every single street links has attributes related to physical and legal hierarchies, those can be evaluated, measured and assessed to simulate the most common arguments behind the patterns of traffic and calculate them based on the self-structure of the network. The XML file not only considers restrictions and calculates attributes from the tags available on OSM, but also serves as a verification tool for the data stored on OSM and thus to create a consistent NDS. More details on OSM2NDS can be found at 52 North ( an open source initiative that is developing different open source projects that supports OSM and their further usage on related disciplines that can be benefited by using this common source Editing and Processing Data Based on above shown topology rules in ArcGIS (section 3.3.2), these are applied and all elements on the network were checked. Results will show all the areas to be edited manually, because the actual conformation of those elements does not comply with the defined rules. Hence, this is a manual editing process, like digitalizing any normal layer. Although, the program shows all errors but allows to mark exceptions, places where it is allowed to override those rules, for instance in the case of tunnels or bridges, the links will cross other links in points different to nodes, but there should not be a node since they are in different levels and there is not a real intersection.

57 4-Model Construction and Output 57 The logics behind topology and how to apply them to a layer are out of the scope of this paper, yet for the reader is recommended to find more details in ArcGIS help. Once the structure of the network is purged, the following step is to consider the attributes that define multi-modality. There will be a different link for each mode, accordingly will occur with nodes. One can infer that for each mode a TN can exist, however for an urban TN, all modes physically can access all links on the network; rules and classification will allow the establishment of logical rules to assign transit to each mode. For this exercise, Munich road network will consider those restrictions and classification based on the data available on OSM (please refer to section 3.2.1). Every link will possess an attribute to classify their accessibility and transitability, therefore to calculate their capacity based on traditional traffic theory, allowing a single mode traffic assignment, precisely for cyclists indifferent from other modes, considering the most basic costs as travel time and travel distance. This will be developed in section 7.2 below, in which requirements to complete an assignment are explored based on the experimental constraints faced to build this model and benchmark it. In order to enforce the right assignment several attributes must be used to conform that single mode network. The fields maxwidth, tracktype, surface and smoothness are additions developed by Zimmermann in a later update of OSM2NDS. In case of Bridge and CycleLane fields, those are additions for this research work to accommodate attributes that will allow further processing for the creation of the cycle network; tracktype field values have been changed as shown on Table 7 to include the specific cycling infrastructure in the network. traffcalm field was modified to include the Tempo 30 zones. These attributes are mapped on the Appendix C. There are many kinds of obstacles that need to be considered on the model, in this case, they will be called barriers, they represent any kind of obstacle for the free movement of the vehicle through a link, and this could be a gate, a bollard, a speed breaker, even a fence. OSM2NDS reads them from OSM and takes the point barriers, collects them on a layer called barriers, the attributes for these elements are shown on Table 17, this is not only for traffic controls but also for elements that restrict access from public roads to private areas or areas with certain kind of traffic restriction. Based on these two features classes and their attributes, the NDS reads them and evaluates if for a desired set of restrictions is possible to use a link for taking part into a path or not. This is a result of the logic based on parameters read from feature classes attributes. This are the arguments that the NDS checks before assigning or using a link, which ones are they and how are they composed is shown on Table 18,

58 4-Model Construction and Output 58 as part of the logic the parameters feed internal tables that serves the evaluators to take the decisions based on the chosen restrictions at running the NDS solvers in ArcGIS. As explained before the NDS is composed of the Feature classes Road and Barrier, all stored in a geodatabase as shown on Figure 32, the Cycling NDS is composed of those and as result the Point Feature Cycling_Junctions contains the nodes in the desired locations were the links are supposed to meet. Figure 32 Geodatabase for Munich Cycling Model Once the parameters have been generated, evaluators need to be populated on run time, in other words they are measurements that can be taken during solving a specific task with specific requirements from these parameters that feed the evaluators for giving a result, in this case a path meeting the desired conditions. Evaluators in short are taking parameters and assessing them, therefore becoming the NDS attributes as shown on Table 19. These can be manipulated to get different results without changing the classification of the whole network, just adjustments on its own logic. The network attributes allows calculating the values that will define the assignment while running the network analyst solvers, therefore they try to model the reality. In this case types refers about the source, when they are Field they are read from a layer attribute, VBScript means a decision is taken based on some logic, Function is the simplest logic as there is only a threshold, finally constant means that values attributes with the same value will be avoided, other will be allowed. The detail logic for these expressions and evaluators fitted for this research work can be found on the Appendix H, the non-referenced Scripts are still as in OSM2NDS (Zimmermann, 2010) Cyclists Gates Automated Counters generates data packages are dumped into a database that feeds the monthly report for traffic readings in each location, along with these traffic counts, the device also records the temperature at every aggregation that is sent to the main server.

59 Counter Arnulfstr Südseite Erhardtstr. (Deutsches Museum) Hirschgarten HLP (Birketweg) Bad-Kreuther- Str Margaretenstr. (Harras) Olympia Park (Rudolf-Harbig- Weg) Residenzstr. 4-Model Construction and Output Automated Counts Data collected with the Automatic Sensors is sampled every fifteen minutes; therefore, for one day there are 96 records, which means records per year. In order to handle this data the sum for each day was calculated and from scattering this result the flaws are shown on Table 9, in this case when there is a present, there is more than 5 months of data recorded otherwise a will be marked and that year cannot be used for a comparative assessment. Record Count First Record Table 9 Data Verification for Automatic Counters Figure 33 Automatic Counter Comparison 2011

60 4-Model Construction and Output 60 Results for 2011 were plotted on Figure 33, after a visual inspection of the data was concluded that the only month were all detectors collected data was July 2011, which coincidentally has an average volume for almost all locations, based on these observations the data from manual counts was requested to VEP-R. Other specific behaviours found for each counting location will be considered for conclusions and benchmarking, as they are local impacts that do not mean any threats to the quality of the same Manual Counts For this case, they were made in intersections therefore each movement has been collected during the counting period; thus, there are two readings and another two in brackets. These are peak values for the rush hour in each one of the blocks. It is important to clarify that values are aggregated in gates, thus every movement will be counted twice, as one entering and one outing for each movement. This will maintain the balance on the traffic through the gates; these are the values to be considered for statistical benchmarking later on. Figure 34 Sample of Manual Count Isarparallele (Schuh & Co 2011)

61 4-Model Construction and Output 61 The counts were made by persons who stood up for the periods explained right above, counting the cyclists moving from one street to a second one, therefore the number of street destinations depends on how many arms an intersection has, and how many of those crossings are possible by using a bicycle. It is necessary to clarify that manual counts differ from automated not only on the counting periods, but in the number of possible gates, while automated counts considers one (from A to B and back) manual counts can consider as many gates as possible in an intersection with more than three arms. An example of the manual reading record is shown above on Figure 34. As example on Figure 34 is shown the normal configuration for a four-arm intersection, this means that there are twelve possible traffic flows between those arms, here registered in red, blue, green, and black colour. Therefore if the gates are set just right before those movements take place, there will not be any lost. In order to imitate the assumptions made in SSX model, the movements entering and outing the gate will be aggregated, which means that each gate will have three entering and three outings, in total there will be twelve entering and outings, completing the movements described just right above. The data set acquired from VEP-R in Munich was composed of 23 different locations; details on them can be found on Table 12 Details on Count Locations in Research Area on Appendix D-Detail of Count Locations. On this appendix details on location and the information from the empirical gates is described. From this is possible to conclude: The Research Area will be limited to the Mittlerer Ring of Munich City. 23 Locations with Manual Counts and 7 with Automated Counts The Count Timing Blocks are three: o Dawn Block 00:00 to 06:00 hours with 10 count Locations o Morning Block 06:00 to 10:00 hours with 31 count Locations o Afternoon Block 15:00 to 19:00 hours with 28 count Locations One Count in first week of April Four counts were taken in May Second Week Five Counts in July second week, three on the Third Week. One Count on First week, Three on the Second Week and Two on the third week of October Four counts on the Third week of November One count was taken on 2008 Seven counts were taken on 2009 One count was taken on Counts were taken on 2011, Seven of them are from the automated Counters

62 4-Model Construction and Output 62 One count was taken on Count days had Rain 14 Count days were Dry This leads to a major question, the standardization of the empirical data is very difficult. Having such a different time and season distribution is very complicated to obtain a normal or uniform distribution of the data. This can be seen already on map view of the data, to be discussed below. It is important to clarify that the values mapped on all three blocks corresponds to the total of observations for each block Cyclists Gates Dawn Block The values for this timing block were mapped on a colour scale covering the whole range of values for the counts, assuring uniformity on the interpretation. As it is shown on Appendix E-Traffic Values on Count Locations in Figure 45 Cyclists Gates Dawn Block, the ten locations with readings have low values that do not cross the first level (dark blue colour). It is clear on the map that traffic stills alive in the centre of the city, following the need for specific purposes. Residenzstr. in the very core has the highest value. On the right side of the map, the Histogram with the gates is shown, revealing that the values are concentrated on the lower levels. However, this is not enough to explain in any manner the movement patterns, the values will not be considered for further analysis Cyclists Gates Morning Block Following the same representation in Figure 46 Cyclists Gates Morning Block, the values of the gates for this block are mapped. For this block, all count locations have values covering almost the whole range of possible values; once again, Residenzstr. has the highest reading. Locations like Max Joseph Bridge and Margaretenstr. have very high values due to the fact that they are the best option to connect locations on the east and west with the centre of the city. Lindwurmstr, Isarradwege, Sonnenstr, and Maximilianstr, is the following group with high values, this as they collect traffic volumes from surrounding areas and bring them to the centre. As happened with the dawn block the readings once they are displayed on the histogram, there is a major accumulation of those in the lowest rank, values on the middle rank are not even a half of the lowest, while the highest values are minimal, and this reflects a beta distribution. The cyclists gates with values in the middle rank, can be considered as locations where the traffic flows, passes by, therefore collecting traffic from other roads to take it either to the centre of the city or any other location on the other extreme. At the same time is visible in the east-south quarter of the old city a major concentration of

63 4-Model Construction and Output 63 high traffic, while in the north-west there are high counts but quickly are distributed on the surroundings Cyclists Gates Afternoon Block As mapped in Figure 47 Cyclists Gates Afternoon Block, 28 locations are having the highest values from all reading locations. Distribution is very similar to the morning block. The histogram presents the same distribution, with a higher peak on the lowest rank of values; a minor share of them is in top. The count locations in the external ring of the research area have lower values, collecting all traffic in a few routes, on the same streets as in the morning block, therefore they are already well known and hard to avoid or substitute at first glance. At the same time there are many bottle necks, located along the Isar river, the bridges connecting east and west, similar situation is found over the train lines, they generate a major separation between north and south on the west side. For this counting block is important to remark, on the south-eastern side of the old city there is not having such a high concentration of counts like in the morning block. There is heavier concentration on the southwest coming from Lindwurmstr, taking Sonnenstr or Oberangerstr in direction to Residenzstr, which again is the highest among all counts. Patterns are quite similar but in this block, movements from south to north and forth, are the heaviest. On the northwest, there is movement along the ring closing the research area; traffic upon this section of the perimeter is high, yet there is no connection to city centre, which indicates that trips are in between surrounding areas, neglecting the centre as it was before on the morning block Cyclists Gates Projections By using all the values from the automated and manual counts for its corresponding date (most of the cases July ) the weather conditions were confirmed based on historical data from Deutscher Wetterdienst, DWD (BMVBS, 2012). Along with information collected on Table 12 Details on Count Locations in Research Area, were input into the TU-Dresden tool (please refer to section above), therefore all values for a Normal Day, Cycling Season Day and Annual Average were standardized. The process is explained in detail by Schiller (2011), the input was described just before and the output is similar to the Table 13, Table 14, and Table 15 below. At the same time, the tool generates a graphic output like the sample in Figure 48, Figure 49 or Figure 50 below, which has graphs for the hourly, daily and monthly distribution of cycling traffic based on the model developed by Schiller (2011).

64 4-Model Construction and Output 64 The results for these three different projections are mapped in Appendix G-Projected Traffic Values on Count Locations, for a normal day in Figure 51, for a day during cycling season in Figure 52 and for annual average in Figure 53. All of them have similar behaviour since they are the same output mapped for different scenarios, in that sense the distribution of values, as it is seen on the histogram for each figure, follow the same distribution seen on the real manual an automated counts. Moreover, the spatial distribution of the values distributes the traffic not only on the main routes described for the manual counts on morning block, but evidences the existence of ring routes, all converging on the centre of the city, more precisely on Residenzstr. However, it is visible that there is not a strong connection between the southeast and southwest side of the city, same situation occurs between southwest and northwest. Since all of the results for Normal Day, Cycling Season Day and Annual Average come from the same model, they will have the same distribution, therefore any of them are expected to have the same correlation with the real measured counts; fact to be proven on the statistical model Urban Network Model On Sections and were introduced the elements necessary to create a network that will allow to make an assessment of the spatial qualities of a specific network for cycling. Later on section was developed the data requirement for achieving that network in certain manner that will agree the theoretical requirements explained on Chapter 2. The process of editing, attributing and arranging the network structure gave as result a set of cycling junctions and street links that will feed the NDS through the feature classes that defines street links (roads) and control points (barriers). Based on the attribute CycleLane, the elements that will be part of the SSX analysis were selected. The result of the selected street links and the calculation of the SSX Indicators are shown on the Appendix I-Urban Network Map for Betweenness 2500 mts. The results for this indicator and radius were selected between all the analysis cases shown on Figure 28. Based on the process to be described on section 4.4.2, which means that for all street links on the cycling network, including the entire infrastructure and emulating the street link conditions on the urban environment, the SSX indicators were calculated by using sdna (Chiaradia, 2012). sdna (Chiaradia, 2012) allows the user to take a street link layer from ArcGIS and run two different scripts. The first one Prepare Network, this is intended to review

65 4-Model Construction and Output 65 the structure of the network and warranty that there is a unique planar graph, erasing any duplicate, overlapping, wrongly conformed links. This was the final quality check for the street links. Once this was completed Run Analysis was executed, following the Analysis Options from Figure 28, this script was used 32 times. The results will be confronted (on section 4.4.2) with the statistical data and included as result. The mapping follows a colour coding, from dark blue going through green, yellow and finally red, which represents in the same order the lower values for this indicator to the highest values. The scale was defined in ten different breaks following a uniform deciles separation between them in order to cover all range of values and provide a fair combination Transport Network Model In the same manner that the Urban Network Model was created. The Transport Network Model includes all street elements that can be used for cycling. Following rules explained in sections 3.3.3, 3.3.4, and along with logic for building the NDS with its spatial structure shown on Appendix H. This structure is by far more complex than the one on the Urban Model. The planar graph defined for transport has a very wide number of attributes that defines several qualities of the network. Therefore, as many parameters are defined to qualify the network will consequently mean that such attributes must be collected and properly assigned to each street link on the network. This kind of assignment and the logic defined for the NDS are the tools to be used by the Network Analyst tool from ArcGIS to solve transport problems to be proposed Model Benchmarking Since this exercise intends to transfer the success of SSX from pedestrian movement into Cyclists Movement, it is necessary to implement a model of this nature and then this must be probed against empirical measurements. In this exercise was suggested to get a quantitative and qualitative assessment. How those were performed and their results will be explained below Experts Interview Output On Table 10 is displayed some basic data regarding the Cycling Experts who took part on the interviews, all details and maps are collected on Appendix K. Members of ADFC Munich, Bike Sharing Companies operating in the city, Local Cycling Traffic Planning Office, Traffic Company performing cycling traffic counts, among others

66 Date Duration (min) Expert Name Cycling Radius: Commuting distance Under Performing Links Over Performing Links 4-Model Construction and Output 66 were approached and contacted to take part. From those, eleven experts took the survey, analysed the map and completed the proposed exercise. Experts were able to identify easily underperforming street links, while agreeing on the over performing was a complex task, this as there was not a clear and standardized measure for defining what should be high cycling traffic, because this concept was different for each expert. Each expert showed some expertise in a specific city area, therefore comments, and deeper insight on the research area was acquired. These were used as main input to review and improve model s performance. It is important to highlight that all participants agreed that prevailing colour patterns agreed with their expectations, straight and main long roads having high potentials of movement. Some facts about the interviews indicates that the average time for filling the map was around 50 minutes, average cycling radius is 10 km, and the average commuting distance about 8 km. The interviews were done as explained on section 3.4.1, records are part of Appendix K-Cycling Experts Interviews. Based on their input the Urban Model was improved. However, there are several street links and their performance cannot be explained based on merely spatial patterns. Since the model assumes more activity when there is a higher link and node density, areas like green open parks, pedestrianized street links, open squares, or major blocks, may lower such densities, and therefore produce low potential levels of activity Rainald Laurer 9km 15km Tal, Arnulfstr., Hohenzollernstr., Briennerstr Dachauer Str, Isarring, Frankfurter Ring, Heidemannstr, Feldmochinger Str, Blutenburgstr. Landwehrstr Martin Glas 11 to 20km 7km Isarradweg, Schleißheimer Str., Franz- Joseph-Str., Elisabethstr., Barerstr., Scheinerstr., Possartstr., Sternwartstr. Mittler Ring, Innsbruker Ring, Heinrich-Wieland Str., Berg-am-Laim Str., Wasserburger Landstr Peter Schorer Robert Zach 8 to 11km Westpark, Olympiapark, Wiesn, Oberanger, Rindermarkt, Sonnenstr., Corneliusstr. Paul-Heyse-Str. Dachauer Str.

67 Date Duration (min) Expert Name Cycling Radius: Commuting distance Under Performing Links Over Performing Links 4-Model Construction and Output Wigand Von Sassen Karin Hoffmann Rainer Heer, Hans-Dieter Wöppel Dietmar Imhof Elisabeth Zorn Patrick Kolesa 9km 5,2 to 7 km 6 to 20km 6 to 10km 6 to 15km 9km 6km 4km 12km 7km Residenzstr. Zweibrückenstr., Kirchenstr., Klenzesstr., Thalkirchener Str., Wiesn, Radlkoferstr., Ganghoferstr., Pfeuferstr. Westpark with Hansastr, Isarradweg Arnulfstr, Margaretenstr. Residenzstr. Garmischer Str. and Heckenstallerstr., Isarradweg, Harlachingerstr. Sonnenstr. Donnersberger Str.and Paul-Heyse-Str., Isarradweg, Sonnenstr. Odeonsplatz Table 10 Resume Experts Interviews Bridges over S- Bahn-Stammstrecke Nympenburger Str, Lindwurmstr, Leopoldstr, Montgelasstr. On the other hand, paths along the Isar River had low potential values, something also reflected on Sonnenstr. and Residenzstr., a systematic blunder on the model, this can be the result of complex spatial patterns that hinders the performance of the indicators, thus cannot answer for the assumptions made on section Statistical Review As explained in section 3.4.2, urban network model will be tested against experimental statistical data namely cyclists counts collected in gates. This was obtained from the City of Munich. The process will take all the possible analysis cases and by matching them with traffic counts and generating a correlation matrix. The result of the matrix will serve as argument to select the best analysis cases and indicators. In other words, higher correlation values between two variables are an indication that one can explain through a mathematical model the second variable. Correlation With Analysis Case Indicator B_morn_Ra w B_aftn_ Raw T_DTV_MO _FR Angular Discrete 2500 Avg_BtA Angular Discrete 2500 Avg_TPBtA Angular Discrete 2500 Avg_Jnc

68 4-Model Construction and Output 68 Correlation With Analysis Case Indicator B_morn_Ra w B_aftn_ Raw T_DTV_MO _FR Angular Discrete 2500 Avg_Lnk Angular Continuos 2500 Avg_BtA2500c Angular Continuos 2500 Avg_TPBtA2500c Euclidean Continuos n Min_NQPDEnc Euclidean Continuos 2500 Min_Lnk2500c Euclidean Continuos 2500 Min_Jnc2500c Euclidean Continuos 2500 Min_Lnk7500c Euclidean Discrete n Max_MGLEn Euclidean Discrete 2500 Min_Jnc Euclidean Discrete 7500 Min_Lnk Euclidean Discrete 7500 Min_Jnc Angular Continuos 2500 Length Weight Angular Continuos 2500 Length Weight Angular Discrete 2500 Length Weight Angular Discrete 2500 Length Weight Euclidean Continuos n Length Weight Euclidean Continuos 7500 Length Weight Euclidean Discrete 2500 Length Weight Euclidean Discrete 7500 Length Weight Avg_BtAWl2500c Avg_TPBtAWl c Avg_BtAWl Avg_TPBtAWl Min_NQPDEWlnc Min_NQPDEWl c Min_Lnk Min_NQPDEWl Table 11 Correlation Results for SSX Indicators and Cyclists Gates Therefore, correlation between cyclists gates and indicators on all analysis cases were calculated, from all the results the top ten were selected and manually confirmed. On Table 11 is shown the highest correlation values found from all analysis cases. The top value is for the analysis case Angular-Continuous with 2500 radius with r = Since correlation is a first moment measure of a following regression model, it is possible to say that any of the indicators calculated with the present model and its considerations is not able to explain the Cyclists counts gates used as statistical contrast. Due to the distribution of the indicators as was seen on their histograms, a data conversion was considered using Logarithmic - Logarithmic and Semi Logarithmic, trying to achieve a better fit with a normal distribution. However, values had a more likely Beta distribution. Nevertheless, this was carried there was no impact on r 2 result, which means that chances for these indicators calculated with this urban network model to explain satisfactory the cyclists gate counts are very low.

69 4-Model Construction and Output 69 Correlation values obtained are very low and non-representative; Figure 35 shows the scatter plot of Betweenness indicator for a radius of 2500 meters. The cyclists gates plotted against this indicator presents a high level of variation and spread, there is not a clear tendency, even more there are readings outlying, which in this case corresponds to specific cases highlighted before, for Instance Residenzstr., Margarentenstr. and counts over the bridges. Since these are obligatory crossing points or bottle necks the traffic is pushed through those. In the same figure green line represents a normal Linear Regression model, the obtained r 2 = Figure 35 Scatter Plot Betweenness 2500 Vs. Cyclists Gates In Figure 36, it is plotted same data set after taking out nine outlying values, identified by their position on Figure 35, those lie far away from the spread lines (red dotted lines). By removing outliers is possible to reduce partly error caused by factors that cannot be explained or were not considered to be explained by the model of this research. Yet, obtained r 2 = 0.23 after this treatment.

70 4-Model Construction and Output 70 Figure 36 Scatter Plot Betweenness 2500 Vs. Cyclists Gates after Outliers

71 5-Output Analysis and Discussion Output Analysis and Discussion One of the objectives of this research was to measure how spatial indicators are able to explain aggregate cycling traffic through an urban cycling network. On this process, the infrastructure used by cyclists on the study area was considered and digitalised. This created a significant challenge. Street segments allowed to cyclists are many and they can be parallel, therefore urban space is fractioned, a specific space for each mode of transport; on the other hand, this is not physical barrier for sight or to hinder the orientation process that allows navigation. The model created and used to calculate the spatial indicators was not able to explain a significant share of cyclists count gates satisfactory. Thus, this section will try to point out and explain the causes for such performance. Spatial Data Sources: Since OSM was used as a supplier for spatial data and hence to create a spatial network and it has to be highlighted that multi user input of the data, means different judgments for: Joining street links at intersections: It is supposed that streets segments are drawn following their axis lines, hence meeting at geometric centres on intersections; however this was not all the case on all intersections across the model. This always decreases models performance as it adds unnecessary turnings, which do not take place on reality. These were not completely purged, as they needed to be done one by one, manually by visual inspection. Branching for starting infrastructure or Merging ending infrastructure when separating from other street segments: There are no rules for branching and connecting consistently new infrastructure with street segments upstream, these were not standardized completely across the model, inducing in many cases to unnecessary turnings. Link End nodes: Manual digitalization of streets produces arbitrary nodes and follows user s visual perception; consequently, more nodes will be constructed than the minimum required. Street Segment Classification: After a street segment is digitalized, this must be classified, thus accurate assignment of attributes will depend on users judgment, and this may be biased. These problems can be stated into two different categories, Added Angular Change and Corrupted Classification. Both of them hinder the calculations, first by adding unnecessary changes of direction and secondly leading to a wrong selection of street segments for creating an adequate urban network for this transport mode. Statistical Data Sources: Empirical data for this exercise comes from traffic counts. On section the nature and diversity of this data was explained, either from

72 5-Output Analysis and Discussion 72 automated or manual origin, all of them have different dates, temperature, weather, location, and riding season. For automatic counters fake readings and malfunctioning may occur, while for manual counts from mistaken perception and registry, visibility obstacles, and hidden commuters may corrupt the final figure. However, these are minimal compared to the first listed parameters. Within the sample of 31 count locations, variations on those primary parameters are major, thus the sample is not homogeneous and seasonal variations are important, not only through the year but between years are strong reasons to believe that cycling in one year was not the same in a second one. A supposition that is supported by continuous reports on statistics in which cycling traffic has a growing trend for the period of these counts. Standardizing these readings was an option; however, so much variability cannot be reduced with a synthetic method that is not adjusted for Munich urban area exclusively. Urban and Transport Network Model Philosophy: Every city has different habits and approaches to implement rules and spaces to each actor on traffic. Once this has been analysed it is possible to understand what street segments can be used for cycling and how cyclists will make use of them honouring those rules. This is a crucial indication to select which street segments will be part of the network. At the same time, this will give an indication of how to resolve conflicts while depurating and editing the network. From a transport and cadastral point of view, every infrastructure element can be represented by a link, since they carry along diverse attributes will be appropriated for analysis, assignments, and classifications. This generates a number of parallel street segments, as many as different types of infrastructure. This precept was followed during the creation of the present model. However, Hillier (1988) considers urban space as all visible space between physical barriers, namely buildings for our study area, thus this space is shared by all. In other words, there is no segregation or separation of functions, while this space serves for movement and consequently mobility, there is only need for accounting a unique street segment for each one of these spaces. The conceptions just before presented are in contradiction. On one side a transport network requires specified links, segregated by which mode will make use of them; on the other hand, an urban network requires a minimum of street segments to model the same space. As consequence, more or less parallel street segments will converge in intersections, therefore creating a significant different number of nodes, a unique one in the urban network, or the squared number of parallel street

73 5-Output Analysis and Discussion 73 segments present in the intersection. This clearly modifies the number of turning points and the angularity of both cases. This issue was understood from the beginning of the creation of the model, yet not addressed in the most efficient manner. Suppositions made to model the cycling infrastructure were not enough to ensure a proper model performance. This can be seen on the remarks made by the cycling experts, under and over performing links, areas that could not be properly accounted with cyclists gate counts. Alongside, the number of parallel street segments while dealing with major roads and broad areas was not adequate enough. Similar situation can be seen on intersections and roundabouts, they produce an angular change, but how severe this is supposed to be? Without hampering the model yet representing the difficulty experienced by cyclists or pedestrians. Clearly, after decomposing the elements of the network and how these are selected and connected are the major challenges to be assumed in a future if attempts to recreate a cycling network are to be taken. Turner (2007) explained that centre lines and axes are good enough to assume the calculations that Hillier (1988) laid for analysing urban spaces with Axial Lines. This is a very important argument to consider while creating a network model of this type. There is need to find a common point in which the segregation of functions can meet the human perception of space, in this case measured by visibility. The segregation of modes as different parallel street segments needs to be questioned on the basis of hierarchy, infrastructure classification and visibility if one is pursuing to model human behaviour while mobilizing and navigating. Probes of this can be found on Radford (2007) and McCahill (2008).

74 6-Conclusions and Recommendations Conclusions and Recommendations This thesis showed that it is possible to create an urban network from open data. Nevertheless, this requires further protocols to standardize intersections and link creation. Moreover, this can be used to develop a cyclist movement model of Munich City and urban area. However, model performance for explaining recorded cyclist flows is still poor, due to the accuracy of the model and shortcomings of the survey method. As a result, we propose to improve the model by taking into account visibility of the urban space and standardise the survey data, and covering more factors that influence the route choice for cyclists. For Data Acquisition: Government and Official offices should become the natural sources of data for these exercises. This ensures data congruence and reliability, reducing bias on judgment for classification and representation of the urban space. Spatial Data shall be able to represent all physical characteristics of infrastructure; this will help to define a methodology to model more precisely intersections and branching or merging of those elements. Open Street Map can be used as a replacement for government data. However, data retrieval and selection is a complex process and requires development of verification protocols for the data available on the Internet. Empirical Data needs to come from a tighter time frame, same year same season. Empirical data cannot be confined to merely cyclist count gates, Origin and Destination surveys must be considered to describe more accurate behaviours, and allow definition of diverse profiles for cyclist s users. Data sampling for this kind of exercises should be highly denser to traditional transport models. Aggregation to District levels in a city is not enough to represent patterns of movement that are smaller than those districts. Therefore, if cycling trips are most frequent in a specific radius, data sampling must be denser than this radius. In this exercise, the radius was 2.5km, therefore demographic, census and transport data cannot be aggregated to areas bigger than this radius.

75 6-Conclusions and Recommendations 75 For Model Definition: Cycling as daily mode of transport can be easily confused with sport and recreation activities. Therefore, defining a unique cyclist user is not recommended; there is no agreement on this sense, as there is a wide range of definitions. Defining a set of diverse cycling users in terms of experience and confidence will give arguments to define which street segments should be considered for modelling, and how their usage can be weighted based on safety and friendliness. Defining a Cycling Network sounds very trivial, since every street link can be used for cycling purposes. However, since transport-modelling techniques suggest a segregated street segment for every mode, selection of street segments for network analysis must be oriented to acknowledge not this specific infrastructure as separate element but as an attribute for that space. If the source for spatial data to be used is Open Street Map, protocols for Intersection Normalization must be build and hence standardize every node and the street segments converging in them. Independent from the source for spatial data, Intersections, and relations between nodes and street segments must be carefully reviewed. Connections on intersections must be representative of the difficulties for crossings and turnings on reality; no extra effort should be induced on the simplifications done to build the network model. Since SSX and indicators care of the structure of the network, details on infrastructure, and detailed modelling should be simplified. In other words, parallel street segments should be avoided and number of nodes at intersections should be minimized. For Model Benchmarking: Empirical data cannot be considered as the only medium of contrast. Census Data and Transportation surveys needs to be considered as more appropriate sources for unveiling the connections between factors that shapes cycling as commuting option and urban structure. Since Network Analysis is a part of Spatial Analysis, the indicators calculated make use of the network itself, therefore must be tested with other spatial based models. This means that data for doing such contrast must be denser and less aggregated compared to the sample used for this exercise. Geographic Weight Regression (GWR) should be considered as a method to test spatially distributed variables with empirical data that depends on the location and the structure of the urban space.

76 6-Conclusions and Recommendations 76 For Model Output: Details on change of direction along street segment are unnecessary for a network analysis. By linking two different nodes is more than enough information for the model. For instance, details like bus stops, curves on corners and zebra crossings, add unnecessary angular change. The network structure used for the analysed model was closer to a transport model definition. Thus, street segments for each type of infrastructure were made part of the network. Resulting in several parallel street segments that were used for this analysis. This is a redundancy for graph analysis, a misconception that hindered the performance of the model.

77 7-Future Developments Future Developments Cycling is a very complex topic, mostly because it has been underestimated as a powerful solution for urban commuting. On the same sense, Spatial and Network Analysis have not play until now a major role on analysing transport patterns. Since the major focus has been from Economics and inherited flow theories, there is need for looking different approaches that allows the analysis of specific factors that have been neglected until now. An interesting analysis of the factors influencing cycling was shown on Figure 18 Relations between factors influencing NMT travel (FHWA 1999). Many of them have not been properly studied. One reason for this is the huge amount of data required to find an experimental proof of them and many of them requires acquiring data that can be easily sensitive to privacy issues. In this regard, demographic data and transport surveys must be collected, and as explained in Chapter 6 aggregation levels for this data should be smaller compared to the most common radius distance for cycling. GPS traces offer a very wide range of options for this purpose. They not only show geographical position but time stamps, moreover they can be linked to user data and from this is possible to derive demographic data. Same output was suggested by Radford (2007) and McCahill (2008). This data is becoming every day more available with the availability of devices that allows the collection of this data from Smartphone, devices that are basically on everyone s possession nowadays. Examples on the usage of voluntary GPS traces and their donation for planning usage can be found on the USA, specifically on the planning process of the Bike Sharing System of New York City ( and more recently on data collection for similar purposes on Austin Texas ( Along with census data, transportation surveys are resources of great value for understanding below in the next section there is a small exploration of Mobilität in Deutschland dataset for 2008 (BMVBS, 2010). All this experimental data can shed lights on the arguments that already FHWA have explored and presented a logical relation to cyclists behaviour. Therefore, considering alone a factor, in this case Network Structure will contribute in a timid manner to explain a behaviour that is much more complex.

78 7-Future Developments 78 Finally, in order to build a model that can be used more extensively to analysis aggregated cycling traffic, it is necessary to join experimental probe of traffic and demographic factors that are able to explain generators and attractors of traffic. In this case, in section 7.2 below a methodology for traffic assignment is considered and briefly explored. This in order to support needs for further and more detailed data collection and to carry on with the transition of transport theories to a different and more complex environment Census Data BMVBS, since 2002 has released results from massive activity interviews that shed lights on movement and mobility patterns on Germany. In 2008 the second version of Mobilität in Deutschland, was released, along with it there is a subset for Munich, this contains demographic data on a sample from 3561 households on 1170 districts. Based on an activity diary survey and the demographic data from the people and vehicles in every household, all connected by the trips done by the inhabitants on the household, as it is shown on Figure 37, all data is placed in a database for integrity and proper selection of the data based on households. Figure 37 Relations on MID 2008 Data Set By choosing the first trip for every single different Household Id, the appropriate location of each one of them was determined and assigned accordingly to each district, from these the spatial distribution of the data set was confirmed and localized the test sample for the whole data set. This is necessary not only to check the spatiality distribution of the data but also to generate an OD matrix that will feed the transport assignment later on. Figure 38 Allocation of Household Sample to Districts After processing the survey data on a database, a selection of all trips having bicycle as main mode was made, from those, their intersection with origin (SPoint Table) and destination (Zpoint Table) points was made in order to allocate each origin and destination to the districts and consequently to get a primitive OD Matrix.

79 7-Future Developments 79 Figure 39 Selection Process of Surveyed Trips for OD 7.2. Traffic Assignment Since SSX estimates the potential movement through the network for a single mode based on the arrangement of the street network, can be compared to a two-step transportation model, in this case there will be trip generation and trip assignment. In other words, as every single segment becomes an OD which means all elements of the network are potentially generators and attractors then based on the centrality measures trips are distributed. Figure 40 Classic Four-Step Model (Ortuzar 2011) In order to assess the performance of the model versus a traditional gravitational model would be necessary to do something comparable. Bearing in mind that most popular transport model is the four steps model (Trip Generation, Trip Distribution, Modal Split and Assignment), the first and last steps will be considered for this benchmarking. As discussed before in Section a TN is a valued graph full of attributes, once the spatial data has been arranged and the network built, it is necessary to feed it with data. For building, a transport model a survey would suffice in which OD, along with census data will allow to generate all the indicators necessary to reflect the nature and extension of the traffic patterns, yet it is possible to do something quite similar with traffic counts in selected locations across the network. Ortuzar (2011), suggest as series of methods to convert traffic counts into OD matrices, these are very simplified compared to surveys and activity based diaries. Simplification allows quick estimation of travel patterns, based on the supposition that

80 7-Future Developments 80 they represent a middle point between route choice and trip matrix. Therefore, following the techniques to calculate the internal values on the OD matrix and multiply them by a factor that will estimate the trip assignment. This calculation is done by joining the members of the OD matrix T ij and the proportion of trips from one zone to another that will cross that link p a ij as shown on Equation 1. Equation 1 Flow in a Particular Link as contribution of all trips (Ortuzar, 2011) This will cause a N 2 of T ij to be estimated from L simultaneous linear equations like Equation 1, which will be equivalent to the number of traffic counts available, however L might be much less than N, leading to an infinite number of solutions. In order to establish border conditions that will reduce the number of solutions Ortuzar (2011) suggest two approaches, structured and unstructured methods. The first one means that a pre-existing demand model will constrain the current assignment, while the second approach relies on stochastic methods to set the same constraints without requiring further data or previous models. In order to do the traffic assignment and calculating trip distribution and the members of the OD matrix, several methods can be used. Yet simplification can be undertake even further, since this research work focuses on urban cycling, assuming congestion is highly improbable, therefore a proportional assignment like All or Nothing method will comply the needs of this procedure as specified in Equation 2, therefore p a ij can vary between 0 and 1. Moreover, independence between p a ij and the matrix to be estimated can be assumed. Equation 2 Definition for All or Nothing Assignment (Ortuzar, 2011) Along with assignment, estimation of all T ij can be done using a gravity model, something that leaded to a calibration problems, experienced in Denmark by planner on the late 70 s when doing assignment in inter-urban networks. Calibration alternatives like intervening opportunities (OP) or flexible gravity-opportunity (GO) model, which to use will depend on the calibration against empirical data. Finally, Ortuzar suggest using a Gravity model along with a calibration by using Non-Linear Least Squares (NLLS) method. Calibration and frontier conditions are a major problem for these methods, due to the fact that infinite solutions may lead to a significant number of options that perhaps are correct for a punctual case, because traffic counts can be taken at specific moment in which conditions may lead to a particular traffic load on the network.

81 7-Future Developments 81 For this Ortuzar suggest several methods result of experimentation with stochastic methods that allows the addition of an error variable that can absorb the error inherited from data collection techniques, assumptions and simplifications undertaken. In conclusion, the methods to be used are still under review as computational power has been increasing and allows refining the current techniques to reduce the error and set more feasible constraints.

82 Glossary of terms 82 Glossary of terms Accessibility: describes the degree to which people can access and/or reach different transport modes, and particularly bicycle infrastructure available in any given area. Average Daily Traffic (ADT): is equal to the total traffic volume during a given period (from 1 to 364 days) divided by the number of days in that period. ADT volume can be established by applying to correction factors such as for season, day of week or direction if required. Bike or Bicycle, Cycle, Path or Way: refers to physically segregated from the main motorway by a verge, grass, planting line or gravel shoulder, a difference in level, and/or other physical barriers that prevent passage by motorized vehicles. Thus includes all facilities for cyclists, while in other places, it refers to visually segregated facilities, and it may include signposted routes, which can be found with both types of segregation. Should preserve visibility, particularly at intersections, following follow motorized traffic flows, and specific cases allow counter flow. Infrastructure can be placed on one or both sides of the road. Capacity: typically refers to the maximum number of vehicles or individuals that can pass through a given cross section of a lane or street in one direction (or in both directions for a two-lane or three-lane road or highway) for a given time period Cycle or Bicycle-Pedestrian Path (Shared or Multi-Use Path):A path designated with preference to pedestrian and cyclists users, requiring that the faster users yields right of way and protect the most vulnerable users. Cycle, bicycle, bike Lane: a ground signalized lane for cycling, and therefore must be used by cyclists only and must follow the direction of the main traffic. In some specific and signalized cases, such lanes may be shared with other vehicles in similar speed conditions. Cycle, bicycle, bike Route: a subsequent path or route conformed by several street links with signposting and different types of infrastructure, intended to provide a connection between distant places within the city; can be exclusive or shared not only with pedestrians but motorized traffic. Hard measures: physical factors and infrastructure that directly is affected by policy changes. For instance development patterns, street layout, bicycle lanes, footpaths, intersections, bicycle parking, etc. Level of Service (LOS): For any type traffic, mainly assesses interruptions to free traffic flow or conditions that may lead to a congestion and reduction on the comfortable use of the commuting infrastructure. Can be rated with a fixed scale and graded based on factors. Soft measures: Non-physical factors that can be directly affected by policy changes without implying any kind of civil structure. For instance, ticket pricing, education, awareness campaigns, marketing events etc.

83 Glossary of terms 83 Mode: Any means of moving people or goods by using vehicles like airplane, bicycle, and automobile or by own locomotion like pedestrian through land, space and water. Mode Choice Model: Mathematical model used to determine in which proportion a mode of transport is selected by users to carry the process of moving between two locations, within a study area. Mode or Modal split: Figure expressed as percentage related to every mode in terms of their share within the total usage of transport within a study area. Traffic Calming: A combination of measures generally hard measures, aimed at altering driver behaviour, by reducing speeds and improving conditions for pedestrians and cyclists, especially in residential areas to increase safety and give preference to local and floating non-motorized traffic. Transitability: Is the ability for a commuter to cross or over take a barrier, based on rights of legal or ownership nature. Transportation Modelling: Involves a model and a computerised procedure to predict future trip making and a replication of the current situation in a mathematical fashion. The traditional model has four steps: trip generation, trip distribution, mode choice, and assignment to a simplified transportation, within a study area. Trip Distribution: Process by which the number of trips between traffic areas zones is allocated in a travel demand model. Trip Generation Process by which the number of trips within each traffic analysis zone is estimated in a transportation model, this based on demographic and economic variables and are given in the form of attractions and productions.

84 Abbreviations 84 Abbreviations ADFC : Allgemeiner Deutscher Fahrrad-Club (German Bicycle Club) BCI : Bicycle Compatibility Index BMVBS : Bundesministeriums Für Verkehr, Bau Und Stadtentwicklung (Federal Ministry of Transport, Building and Urban Development) CBD : Central Business District FHWA : U.S. Department of Transportation, Federal Highway Administration GIS : Geographic Information System GIZ : Gesellschaft für Internationale Zusammenarbeit GPS : Global Positioning System LOS : Levels of Service MVV :Münchner Verkehrs- und Tarifverbund GmbH (Munich Transport and Tariff Association) NDS : Network Data Set NMT : Non-Motorized Transport OSM : Open Street Map SSX : Space Syntax S-Bahn : Stadtschnellbahn (City centre and suburban railways) TAZ : Traffic Analysis Zone U-Bahn : Untergrundbahn (Underground Rapid Transit or Metro) VEP : Verkehrsentwicklungspläne Radverkehr (Cycling Transport Development Plan)

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89 Source of Reference 89 ZUERICH.CH/TED/DE/INDEX/TAZ/MOBILITAET/MOBILITAETSSTRATEGIE.HTML, ACCESSED ON LAST CYCLISTS TOURING CLUB (CTC), CYCLE FOR TRANSPORT AND HELP THE ENVIRONMENT, LAST ACCESSED ON THE CITY OF COPENHAGEN, TECHNICAL AND ENVIRONMENTAL ADMINISTRATION TRAFFIC DEPARTMENT, GOOD, BETTER, BEST (2011), NTPAGE/LIVINGINCOPENHAGEN/~/MEDIA/A6581E08C2EF4275BD3CA1DB951215C 3.ASHX, LAST ACCESSED ON List of other sources CHIARADIA ALAIN, WEBSTER CHRIS AND COOPER CRISPIN, (2012), SDNA, CARDIFF SCHOOL OF PLANNING & GEOGRAPHY AND THE SUSTAINABLE PLACES RESEARCH INSTITUTE, CARDIFF CF10 3WA, UNITED KINGDOM ( SCHILLER CHRISTIAN, ZIMMERMANN FRANK, (2011), HOCHRECHNUNGSMODELL VON STICHPROBENZÄHLUNGEN FÜR DEN RADVERKEHR, FACHBEREICH THEORIE DER VERKEHRSPLANUNG - TECHNISCHE UNIVERSITÄT DRESDEN, ( DRESDEN.DE/DIE_TU_DRESDEN/FAKULTAETEN/VKW/IVS/TVP/HRV/INDEX.HTML) TURNER A (2001), DEPTHMAP: A PROGRAM TO PERFORM VISIBILITY GRAPH ANALYSIS. 3RD INTERNATIONAL SPACE SYNTAX SYMPOSIUM, GEORGIA INSTITUTE OF TECHNOLOGY, ATLANTA, GA. ( ZIMMERMANN, EVA. (2010) OSM2NETWORKDATASET, HOCHSCHULE KARLSRUHE TECHNIK UND WIRTSCHAFT FAKULTÄT FÜR GEOMATIK FACHBEREICH KARTOGRAPHIE UND GEOMATIK, (

90 List of Figures 90 List of Figures Figure 1 Sign posting in Munich's main routes (Landeshauptstadt München, June 2007)... 9 Figure 2 Cycling Network in Munich's Old City (Landeshauptstadt München, June 2007) Figure 3 Main Cycling Signposted Routes in Munich's Metropolitan Area (Landeshauptstadt München, June 2007) Figure 4 Cycling Network Map for Munich (Landeshauptstadt München, June 2007) Figure 5 Accumulative Travel distances for each main mode of transport (BMVBS 2010) Figure 6 Network Concept for 2015, Munich's Traffic Development Plan (Landeshauptstadt München, March 2006) Figure 7 Basic Node Link representation Figure 8 Basic Axial Line representation Figure 9 Basic Centre Line representation Figure 10 Evolution of Elements in Network Analysis Figure 11 Closeness Centrality (C C ) in a general street network (Schwander, Law 2012) Figure 12 Betweenness Centrality (C B ) in a General Street Network (Schwander, Law 2012) Figure 13 Interaction Models (Rodrigue 2009) Figure 14 SSX symbolization of Nodes and Links Figure 15 Street Mobility Functions (Godefrooij, April 2009) Figure 16 Street Mobility Shapes (Godefrooij, April 2009) Figure 17 Street Mobility Use (Godefrooij, April 2009) Figure 18 Relations between factors influencing NMT travel (FHWA 1999) Figure 19Main Steps in Spatial Network Analysis Figure 20 Detail Steps on Network Analysis Figure 21 Cycling Route Planner München.de Figure 22 Access Hierarchy based on mean of transport (OSM) Figure 23 Legal Access Restrictions by vehicle and street link type (OSM) Figure 24 Sensor for Bicycles (Source: Schuh & Co.) Figure 25 GPS Communication Device for Bicycle Sensor (Source: Schuh & Co.).. 43 Figure 26 Münchner Radlstadtplan (Radlhaupstadt 2012) Figure 27 Topology Rules on the NDS Figure 28 Analysis Options and Cases Figure 29 Steps for Statistical Assessment Figure 30 Location of Munich City as in Open Street Map web site, with limits 48º232N, 48º022N, 11º763E and 11º390E

91 List of Figures 91 Figure 31 OSM2NDS processing a dump OSM file for Munich Figure 32 Geodatabase for Munich Cycling Model Figure 33 Automatic Counter Comparison Figure 34 Sample of Manual Count Isarparallele (Schuh & Co 2011) Figure 35 Scatter Plot Betweenness 2500 Vs. Cyclists Gates Figure 36 Scatter Plot Betweenness 2500 Vs. Cyclists Gates after Outliers Figure 37 Relations on MID 2008 Data Set Figure 38 Allocation of Household Sample to Districts Figure 39 Selection Process of Surveyed Trips for OD Figure 40 Classic Four-Step Model (Ortuzar 2011) Figure 41 Road Hierarchy OSM Figure 42 Lane Classification Figure 43 Urban Network Classification Figure 44 Location of Counters in Munich Figure 45 Cyclists Gates Dawn Block Figure 46 Cyclists Gates Morning Block Figure 47 Cyclists Gates Afternoon Block Figure 48 Day, Week and Annual Traffic Average Hirschgarten Figure 49 Day, Week and Annual Traffic Average VZ Isarparallele-a Figure 50 Day, Week and Annual Traffic Average VZ Isarparallele-b Figure 51 Projected Cyclists Gates for a Normal Day Figure 52 Projected Cyclists Gates for a Cycling Season Day Figure 53 Projected Cyclists Gates for an Annual Average Figure 54 NDS Map for Research Area Figure 55 Angular Betweenness Figure 56 Map for Qualitative Assessment Figure 57 Interview Map Herrn Laurer Figure 58 Interview Map Herrn Glas Figure 59 Interview Map Herrn Zach Figure 60 Interview Map Herrn Von Sassen Figure 61 Interview Map Frau Hoffmann Figure 62 Interview Map Herrn Imhof Figure 63 Interview Map Frau Zorn Figure 64 Interview Map Herrn Kolesa

92 List of Tables 92 List of Tables Table 1 Main Differences between Cyclists and Pedestrian in USA (LéGaré 2009). 29 Table 2 Demand Estimation (FHWA 1999) Table 3 Relative Demand Potential (FHWA 1999) Table 4 Supply Quality Analysis (FHWA 1999) Table 5 Supporting Tools and Techniques (FHWA 1999) Table 6 Data Requirements for Model Building Table 7 Cycle Lane Classification Table 8 Indicators to be assessed Table 9 Data Verification for Automatic Counters Table 10 Resume Experts Interviews Table 11 Correlation Results for SSX Indicators and Cyclists Gates Table 12 Details on Count Locations in Research Area Table 13 Result Traffic Projection Hirschgarten Table 14 Result Traffic Projection Isarparallele-a Table 15 Result Traffic Projection Isarparallele-b Table 16 Fields in the Feature Class Roads (Zimmermann, 2010) Table 17 Fields in the Feature Class Barriers (Zimmermann, 2010) Table 18 Usage of Parameters on NDS (Zimmermann, 2010) Table 19 Network Attributes Evaluators and their sources (Zimmermann, 2010)

93 Appendix A-Data Requirements Contact Communication 93 Appendix A-Data Requirements Contact Communication Sample Mail sent to Contact Person on Data Sources Institutions: Details on Data Requirements: MASTER S THESIS DATA REQUIREMENTS for Mr. Cesar Ricardo Criollo Preciado Title: Spatial Network Analysis for Urban Cycling Networks Introduction: As Student of the Masters of Transportation Systems at Technische Universität München, the last and most important milestone is to write a Master s Thesis. As one of my research interests is cycling, I will analyse current modelling and planning techniques for this mode of transport focusing on Space Syntax modelling techniques. Detailed Data Requirements:

94 Appendix A-Data Requirements Contact Communication 94 For the First Part: Since Space Syntax relies on a spatial model of Natural Movement, understanding the rules that shapes that human movement is crucial. On urban spaces, those rules are given by: Cycling and Pedestrian Master Development Plan, at Nation and City level, if available. Cycling Network Design Guide, with road classification and selection methodology. Maps with dedicated signposted cycling network Cycling and Pedestrian Infrastructure Designing Guide Traffic Rules for Motorized and Non-Motorized Vehicles For the Second Part: Building the Space Syntax model requires spatial or geo-referenced data, on Shape files, GML, KMZ, or any other digital Format that is compatible with any GIS (Geographical Information System) and allows geodesic manipulation. Which describes the built environment, such as: Road Network as lines, with attributes describing right of way, allowed way, speed limits, physical, geometrical, hierarchical, and geographical characteristics of each segment. Cycling Network as lines, with attributes describing right of way, allowed way, speed limits, physical, geometrical, hierarchical, and geographical characteristics of each segment. Public Transport Network as lines, with attributes describing right of way, allowed way, speed limits, physical, geometrical, hierarchical, and geographical characteristics of each segment. Public Transport Facilities (Stations, Park and Ride, e.g.) as points or polygons, with attributes describing hierarchical, functional and geographical characteristics of each element. Street Environment (Sidewalks, Squares, Ramps, Stairs, e.g.) as Polygons, with attributes describing physical, geometrical, hierarchical, right of way and geographical characteristics of each element. Buildings and Constructions as Polygons, with attributes describing geometrical and geographical characteristics of each element. Natural Environment (Lakes, Parks, Forest, Swamps, e.g.) as polygons, with attributes describing geometrical and geographical characteristics of each element. Rivers as lines, with attributes describing physical, geometrical and geographical characteristics of each segment. For the Third Part: Since this is a transport research project, transport data is required to validate the proposed model, on several sets of data covering last 5 to 10 years, therefore statistics regarding transport and mobility as: Traffic counts, covering several, several locations across the city, with geolocalization, time stamp, aggregation time and modal split. Records from Transportation Survey as trips for the city area with, trip length as time and distance, purpose, starting and finishing date or time. Records from Census Data as records describing main demographic indicators for the smallest planning unit possible (City District, Block, e.g.) with land use, density use, age, gender and vehicle ownership. Origin Destination Matrix, with the trips for each mode of transportation including cycling for the smallest possible planning unit.

95 Appendix B-Official Form Requesting Data 95 Appendix B-Official Form Requesting Data

96 Appendix B-Official Form Requesting Data 96

97 Appendix B-Official Form Requesting Data 97

98 Appendix B-Official Form Requesting Data 98

99 Appendix C-Road Hierarchy and Lane Classification Maps 99 Appendix C-Road Hierarchy and Lane Classification Maps Figure 41 Road Hierarchy OSM

100 Appendix C-Road Hierarchy and Lane Classification Maps 100 Figure 42 Lane Classification

101 Appendix C-Road Hierarchy and Lane Classification Maps 101 Figure 43 Urban Network Classification

102 Appendix D-Detail of Count Locations 102 Appendix D-Detail of Count Locations Location: Arnulfstr Südseite Counting Direction: To South Location: Bad-Kreuther-Str. (Joseph-Hörwick- Weg) Counting Direction: To East / To West Location: Olympia Park (Rudolf-Harbig-Weg) Counting Direction: To South / To North

103 Appendix D-Detail of Count Locations 103 Location: Residenzstr. Counting Direction: To South / To North Location: Erhardtstr. (Deutsches Museum) Counting Direction: To South / To North Location: Hirschgarten HLP (Birketweg) Counting Direction: To East / To West

104 Appendix D-Detail of Count Locations 104 Location: Margaretenstr. (Harras) Counting Direction: ToWest / To East General Location around Munich: Source Aerial images: Google Earth Figure 44 Location of Counters in Munich Source Render Maps: Open Street Map

105 Counter Name Type # Arms Direction 1 Direction 2 Direction 3 Direction 4 Location Structure Date Day Type Holiday Season Weather Hours Appendix D-Detail of Count Locations 105 Aut- Mar Aut- BaK Margaretenstr. (Harras) Bad-Kreuther-Str. (Joseph-Hörwick- Weg) Erhardtstr. (Deutsches Museum) Aut- Erh Aut- Res Aut- Arn Aut- Hir Aut- Oly Man- Asr Man- Ipr Man- Asr1 Man- Lws Man- Kps Gate 2 To West To East Gate 2 To West To East Gate 2 To South To North Residenzstr Gate 2 To North To South Arnulfstr Südseite Hirschgarten HLP (Birketweg) Olympia Park (Rudolf-Harbig- Weg) VZ Altstadtring Int 4 VZ Isarparallele Int 4 VZ Altstadtring01 Int 4 Gate 1 To East Gate 2 To West To East Gate 2 To North To South Maximilian str. Isartorplat z Karl-Scharnagl- Ring Stern- /Widenmayers tr. Thomas- Wimmer-Ring Thomas- Wimmer-Ring Steinsdorfstr. Frauenstr. VZ Lindwurmstr. Int 4 Sonnenstr. Oberanger Blumenstr. Int 4 Karlsplatz Sonnenstr. Maximilianst r. Maximilianst r. Tal Lindwurmstr. Prielmayerst r. Stadtran d oder Stadtran d oder Stadtzent rum Stadtzent rum Stadtzent rum Stadtran d oder sonstiger Stadt Stadtzent rum sonstiger Stadt Stadtzent rum Stadtzent rum Stadtzent rum 12/07/ 11 12/07/ 11 26/07/ 11 12/07/ 11 12/07/ 11 12/07/ 11 12/07/ 11 22/07/ 09 09/11/ 11 22/07/ 09 17/05/ 11 19/07/ 11 Werktag No Rain Werktag No Rain Werktag No Dry Werktag No Rain Werktag No Rain Werktag No Rain Werktag No Rain Werktag No Dry Werktag No Dry Werktag No Dry Werktag No Dry Werktag No Rain 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 00:00-06:00, 06:00-10:00 06:00-10:00, 15:00-19:00 00:00-06:00, 06:00-10:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00

106 Counter Name Type # Arms Direction 1 Direction 2 Direction 3 Direction 4 Location Structure Date Day Type Holiday Season Weather Hours Appendix D-Detail of Count Locations 106 Man- Ipr1 VZ Isarparallele01 Int 4 ifflandstr. - - Widenmayerstr. Tivolistr. sonstiger Stadt 09/11/ 11 Werktag No Dry 06:00-10:00, 15:00-19:00 Man- Oly VZ Lindwurmstr01 Int 4 Man- Lws1 Man- Smp Man- Hbf Man- Hzp Herzog- Heinrich-Str. VZ Stiglmaierplatz Int 3 Dachauer Str. Lindwurms tr. Dachauer Str. Kapuzinerstr. Seidlstr. Lindwurmstr. VZ Hbf Int 4 Seidlstr. Marsstr. Seidlstr. Marsstr. VZ Hohenzollernplatz Int 3 VZ Olympia Int 4 VZ Parkstadt Bogenhausen VZ Rosenheimer Str VZ 2009 Int 4 Hohenzollernst r. Schleißheimer Str. Leuchtenbergri ng Int 4 Steinstr. Tengstr. Karl- Theodor- Str. Einsteinstr. Rosenheim er Str. Gate 2 To East To West Int 3 VZ Mittl. Ring Int 4 Landshuter Allee VZ Olympia01 Int 4 Dachauer Str. VZ Mittl. Ring01 Int 4 Landshuter Allee Trappentre utunnel Arnulfstr. Man- Psb Man- Rst Man- Cb9 Man- Dbb Man- Mtr Man- Oly1 Man- Mtr1 Man- Swg Schwere- Reiter-Str. Leonrodstr Hohenzollernstr. Schleißheimer Str. Leuchtenbergri ng Franziskanerstr. Landsberger Str. Dachauer Str. Landshuter Allee VZ Schwabing Int 3 Leopoldstr. Ungererstr Leopoldstr. Ackermanns tr. Einsteinstr. Rosenheime r Str. Arnulfstr. Leonrodstr. Leonrodstr. Stadtran d oder Stadtzent rum Stadtzent rum sonstiger Stadt Stadtran d oder sonstiger Stadt Stadtran d oder sonstiger Stadt Stadtran d oder sonstiger Stadt sonstiger Stadt Stadtran d oder sonstiger Stadt 17/05/ 11 25/11/ 10 21/07/ 11 19/05/ 11 14/07/ 09 06/10/ 11 03/05/ 11 15/10/ 09 23/07/ 08 13/10/ 09 15/07/ 09 13/10/ 09 25/10/ 11 Werktag No Dry Werktag No Dry Werktag No Rain Werktag No Rain Werktag No Rain Werktag No Rain Werktag No Dry Werktag No Dry Werktag No Rain Werktag No Rain Werktag No Dry Werktag No Rain Werktag No Dry 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 00:00-06:00, 06:00-10:00 00:00-06:00, 06:00-10:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00

107 Counter Name Type # Arms Direction 1 Direction 2 Direction 3 Direction 4 Location Structure Date Day Type Holiday Season Weather Hours Appendix D-Detail of Count Locations 107 Man- Swg1 Man- Psb1 Man- Mro Prinzregen tenstr. Richard- Strauss- Str. Rosenheim er Str. Man- Ros Man- Scr VZ Schwabing01 Int 4 Leopoldstr. Nikolaistr. Leopoldstr. VZ Parkstadt Bogenhausen1 VZ Mittlerer Ring Ost Int 4 Richard- Strauss-Str. Int 4 Effnerplatz VZ ROST Int 4 Anzinger Str. VZ Schyrenstr. Int 3 Humboldtstr. Schyrenstr. Leuchtenbergri ng St.-Martin-Str. Hohenzoller nstr. Prinzregente nstr.. Isarring Rosenheime r Str. Stadtran d oder sonstiger Stadt Stadtran d oder Stadtran d oder sonstiger Stadt 25/10/ 11 06/10/ 11 10/11/ 11 24/04/ 12 28/07/ 11 Werktag No Dry Werktag No Rain Werktag No Dry Werktag No Rain Werktag No Rain 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 06:00-10:00, 15:00-19:00 Table 12 Details on Count Locations in Research Area

108 Appendix E-Traffic Values on Count Locations 108 Appendix E-Traffic Values on Count Locations Figure 45 Cyclists Gates Dawn Block

109 Appendix E-Traffic Values on Count Locations 109 Figure 46 Cyclists Gates Morning Block

110 Appendix E-Traffic Values on Count Locations 110 Figure 47 Cyclists Gates Afternoon Block

111 Appendix F-Day, Season, Annual Cycling Traffic Averages 111 Appendix F-Day, Season, Annual Cycling Traffic Averages Results for Hirschgarten Count Location: Figure 48 Day, Week and Annual Traffic Average Hirschgarten ausgewählte Kennwerte für die Zählstelle Zähltag Tagtyp Montag - Freitag gewählter Querschnitt gezählte Stunden 14h R1 R2 Q gezählte Verkehrsstärke Hochrechnungsfaktor Verkehrsstärke am Zähltag 1, ,112 Radverkehrssaison, trocken DTV 2, ,030 2,017 DTV MO-FR 2,126 1,044 1,082 2,126 DTV SA 1, ,866 DTV SOFT 1, ,682 Radverkehrssaison, alle Tage DTV 1, ,498 DTV MO-FR 1, ,584 DTV SA 1, ,378 DTV SOFT 1, ,233 gesamtes Jahr DTV

112 Appendix F-Day, Season, Annual Cycling Traffic Averages 112 ausgewählte Kennwerte für die Zählstelle DTV MO-FR 1, ,019 DTV SA DTV SOFT gewählter Querschnitt 12 zwischen To West und To East R1 To West R2 To East Table 13 Result Traffic Projection Hirschgarten Results for VZ Isarparallele-a Count Location: Figure 49 Day, Week and Annual Traffic Average VZ Isarparallele-a ausgewählte Kennwerte für die Zählstelle Zähltag Tagtyp Montag - Freitag gewählter Querschnitt gezählte Stunden 8h R1 R2 Q gezählte Verkehrsstärke 2,201 1,180 1,021 2,201 Hochrechnungsfaktor Verkehrsstärke am Zähltag 4,059 2,176 1,883 4,059 Radverkehrssaison, trocken DTV 6,744 3,615 3,128 6,744 DTV MO-FR 7,457 3,997 3,459 7,457 DTV SA 5,887 3,156 2,731 5,887 DTV SOFT 4,459 2,390 2,068 4,459