BIKEPRINT IN DEPTH ANALYSIS OF CYCLIST BEHAVIOUR AND CYCLE NETWORK PERFORMANCE USING EXTENSIVE GPS-TRACK DATA

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1 BIKEPRINT IN DEPTH ANALYSIS OF CYCLIST BEHAVIOUR AND CYCLE NETWORK PERFORMANCE USING EXTENSIVE GPS-TRACK DATA Dirk Bussche DAT.Mobility and NHTV University of Applied Sciences Paul van de Coevering NHTV University of Applied Sciences 1. INTRODUCTION: TRANSLATING GPS DATA INTO POLICY ENHANCING INFORMATION Bicycle use is on the rise in most western cities. For reasons of sustainability, liveability, health and accessibility many local governments support and try to strengthen this trend. Governments have invested in infrastructure and promotion campaigns with different degrees of success. To make these investments more effective, thorough insight into bicycle behaviour and network performance is needed in addition to the currently available traffic counts and qualitative information. While this kind of quantitative information about network performance has been available for car traffic for some time, developments for bicycle traffic has been slow. To reduce this knowledge gap, we developed an analysis framework which is based on extensive GPS-tracking. The EU-sponsored 1) BikePRINT 2) application, provides policy information about network performances such as: heat maps, delays at junctions, route choice (and detours), average speeds and the number of cyclists per day. These kinds of indicators were already available for car traffic. However, naively applying these car-based indicators to bicycle traffic creates paradoxical results. For instance, travel delays for car traffic are often calculated by comparing the free flow travel times with the actual travel times. As car speed is to a large extent determined by infrastructure and speed restrictions, this provides a good indication of travel time delays. However, the maximum speeds of cyclists are heterogeneous. For instance, higher cycling speeds are often detected in peak hours presumably because people are in a hurry. Furthermore, while routing of car trips is quite predictable with modern traffic assignment tools, we are just beginning to understand the route choice decision-making process of cyclists. These route choices may tell us more about the importance of design characteristics such as the attractiveness of certain road segments. 1

2 To assess what policy information, maps and analyses were needed we have conducted interviews with policy-makers. In addition we have carried out stated preference research on mode choice behaviour for short distances. Furthermore, distance decay functions have been derived from traffic surveys. This paper describes the main modules of BikePRINT and the underlying assumptions. The next section describes the first core module that provides information about the current network performances. Section three describes the module that is used to assess the effects of future infrastructure scenarios. The last section provides an outlook regarding the further development of the BikePRINT framework. 2. NETWORK PERFORMANCE AND BICYCLING ACCESSIBILITY 2.1 Main cycle network Many cities define a core cycle network with higher standards of infrastructure and maintenance (for instance early winter service). This is efficient only if most cyclists make use of this network instead of other routes. Comprehensive information about the distribution of bicycle traffic over the network is often not available since cycle counts are usually performed on a number of specific locations on the main cycle network only. GPS-tracks are well-suited to identify important routes outside the main cycle network. If the distribution of bicycle traffic deviates from the intended core bicycle network, policy makers can try to identify the rationale behind these differences and improve the core cycle network, or they may consider incorporating the often used routes in their core cycle network. Figure 1: Example of bicycle use outside of main cycle network 2

3 2.2 Cycle congestion With increased bicycle use, infrastructure capacity will not always be sufficient and cycle congestion may occur at certain locations. For car traffic, the ratio of peak hour speed and free flow (=night) speed is a good indicator for congestion. For cycle traffic this indicator would not make any sense. Figure 2 shows that notably at night people cycle slowly; they may not feel rushed to reach their destination or may be tired. Figure 2: Speed of inner-city car (blue) and bicycle (green) trips per hour of day To increase our understanding of bicycle travel speeds and the influence of particular network elements, we have analysed the speed profile of single trips. We came to the conclusion that speed variance is more important than absolute speed. Figure 3, shows the speed profile of a cyclist who cycles about 3 km with a nearly constant speed of 10 km/h. Traditionally, we would mark such a track red in the map to indicate slow speed. However the question is if this lower speed is related to obstacles in the network or if this is the result of someone who prefers to cycle at a lower speed or has restricted capabilities. Fig 3: slow cyclist Fig. 4: average cyclist Fig. 5: 20 km/h, high variance Figure 4 shows a speed profile of a cyclist who cycles with a relatively constant speed of 20 km/h for about 2,5 km. Traditionally, we would mark this as yellow or green for medium/high speed. But what if the tracks in figure 3 and figure 4 are on the same bicycle path? Is it sufficient to calculate the 3

4 average speed of both cyclists? This would be the case if we were interested in the actual use of that path. But as our main interest is in determining the network performance, another approach is needed. Figure 5 displays the speed profile of a cyclist on a (different) bicycle path who presumably wanted to cycle 30 km/h. This is indicated by the fact that he often accelerates to 30 but is delayed repeatedly to speeds around 10 km/h. If we aim to provide indicators for network performance, we can use the cyclists as human sensors to show us where significant delays are present. In figures 3 and 4, it seems that cyclists can cycle their desired speed, whereas in figure 5, there is a clear delay in the sense that the cyclist cannot cycle at its desired speed. These delays could be attributed to the moments the cyclist gets a text message on his phone or has to check the map. But if such a pattern is repeatedly observed across individuals, we can assume that there is something on the bicycle network that causes these delays. To examine the nature of these delays and provide potential solutions, policy makers can examine these specific links. Our aim is to find a relative speed definition that fulfils these requirements: independent of sensor (type of cyclist); we check this by splitting the whole dataset into the 50% fastest cyclists and 50% slowest cyclists. The network segment level values should be as similar possible for both datasets; values have to be interpretable from a content view; values on a string of segments have to sum up to the total travel time on that route. To achieve that goal, we tested different indicators and identified the relative difference between actual travel time and desired travel time (derived from local percentile of speeds and distance) as the best relative speed indicator (figure 6). 4

5 Figure 6: Bicycle network delays in the city of Breda, Netherlands 2.3 Selected link and routes Once a certain network element with considerable delays has been identified, measures should be considered to reduce the underlying problems. If a solution on the bottleneck itself is impossible or expensive, one can consider offering alternative routes. But then, the planner has to know the origindestination relations of users of the bottleneck. BikePRINT provides such a map (figure 7). Figure 7: selected link: all routes via selected link underpass The figure shows that most cyclists of a railway underpass in the Dutch municipality of Breda have a destination in the eastern part of the city centre. If this tunnel would be a bottleneck it may require large investments to increase its capacity. Instead, cyclists could also use another underpass further east. This would require a cycle connection north of the railway but this may be much cheaper compared to increasing the capacity of the underpass. 5

6 2.4 Informal routes GPS-tracks also record informal routes that are not part of the official network and that are automatically identified when connecting the tracks to the network (mapmatching), in our case OpenStreetMap. Except map- and GPS-errors which are processed elsewhere, we find informal trails; crossing of parking spaces, pedestrian areas or garage yards; crossing of lane separation lines on locations where this is not appropriate. Figure 8: Informal cycleway If the policy maker identifies such informal connections, he or she can: Officially facilitate the informal path. Figure 8 shows a dead end road (Cluysenaerstraat) which is deliberately not connected with the crossing because the car traffic would be difficult to handle. But there is no reason to block cyclists too. Block the informal path. In some cases there can be reasons not to facilitate such a track, for instance because of safety or property status. In such cases, one can actively block the track. Facilitating an alternative connection should be considered in this case as the informal track shows a need for this connection. 2.5 Waiting times on crossings Many cities monitor waiting times for cars at controlled intersections. Much less attention is given to waiting times for bicycle traffic. If available, cities mostly use the average waiting time for traffic lights, derived from signalprogram. This traditional approach does not incorporate: 6

7 crossings without traffic signals; breaking earlier, after seeing a red signal; time to accelerate to desired speed again; delays by other cyclists already waiting at the crossing. BikePRINT calculates the waiting time in three steps. First, the realised, empirical passage time between 50 meters before and after a crossing is determined based on GPS tracking. In the second step a fictive passage time is calculated for this same stretch of infrastructure, assuming the cyclist could cycle at his desired speed. In the third step, the waiting time is calculated by taking the difference between both passage times. Figure 9 shows the resulting map. The colour of the circles represents the amount of delay while the size represents the number of cyclists that are affected. Figure 9: Delays on crossings. Colour: delay, Thickness: number of cyclists This map enables planners to identify significant delays in the network and to monitor their development over time. In many cases, red crossings with very small circles are irrelevant: very few cyclists experience these problems. Exceptions to this rule are crossings where the delay is the very reason for cyclists to avoid it. 2.6 Detours and potential cyclists When the quality of a certain part of the cycle network is very low, most cyclists will probably avoid these routes. In a simple demand approach, these routes will not be improved on short notice because of the low number of cyclists. BikePRINT identifies these routes by comparing the actual observed cyclists on each segment with the fictive number of cyclists we would have if everyone would cycle the shortest route (figure 10). 7

8 Figure 10: Avoided and preferred routes, identified by detour analysis Network segments where more actual cyclists are observed than would be expected from the shortest routes are coloured blue and interpreted as routes that are attractive enough that cyclists are willing to take a detour to use them. Conversely, the red coloured segment can be interpreted as less attractive : cyclists appear to take detours in order to avoid these routes. 2.7 Isochrones Extensive GPS tracking enables the derivation of empirical travel times between nodes over the cycle network. Sets of geographical areas, for instance zip-codes, neighbourhoods transportation model zones, can be used to calculate travel times matrices. These matrices can be shown on a map by clicking on one specific area (figure 11). The map will show the travel times from the selected area to the surrounding areas. Figure 11: Isochrones 8

9 To make these isochrones meaningful, we also add synthetically calculated isochrones (circles) as typical travel times for the given distance. Yellow areas outside the yellow circle indicate few delays. Orange areas inside the yellow circle indicate longer than expected travel times. Planners can use this information to assess cycling travel times to important destinations such as city centres, schools and shopping areas. 2.8 Potential bicycle accessibility The map of potential bicycle accessibility shows per area the accumulative number of destinations which are currently within cycling distance (figure 12). Higher levels of accessibility indicate that people have many destinations within reach. BikePRINT is able to distinguish between different types of destinations including workplaces, retail stores, schools and residents. These maps can be used for decisions regarding the location of new urban developments such as retail facilities, schools and housing. For example, areas with a relatively high number of residents within cycling distance may be interesting for the development of small scale retail facilities, enabling more bicycle-oriented urban developments. Figure 12: Potential bicycle accessibility The measures of travel time between origins and destinations are derived from the empirical GPS data. This ensures an accurate measurement of the potential bicycle accessibility based on the current bicycle network performance and speeds. BikePRINT uses the potential accessibility measure to estimate changes in accessibility. This measure ensures that destinations at greater distances have a diminishing influence on the overall accessibility. It can be described by the following equation: 9

10 [Equation 1] Thus, the potential accessibility of origin zone i (Ai) depends on the population the number of destinations in the other zones (Dj) which are weighted by their distance to zone i by F(Cij). The term F(Cij) is the distance decay function. Different forms of distance decay function are available and the appropriate function depends on the nature of the data. In this case, the log-logistic S curve provided the best fit to the data. The curve is represented by: [Equation 2] The parameters MAX, a and b were modelled to provide the best fit to the dataset. The travel data were derived from the Dutch National Travel Survey (NTS) (CBS, 2015). Several decay functions were estimated for different transport modes and trip purposes (table 1). Transport modes Bicycle Car Public transport Walking Trip purposes Home-work Home-business Business trips Home-school Home-shopping Social-recreational Other Table 1. Transport modes and trip purposes To ensure that the estimates of all distance decay curves are based on a sufficient number of observations, the NTS data from the years are combined into one dataset which comprises more than observations (trips). In our estimations X represents the travel time in minutes. Observations of travel time from the NTS data were clustered in 5 minute time intervals. The midpoint of each time interval (e.g. 2,5 and 7,5 minutes) represents the observation of travel time. The dependent variable is the percentage of trips in these separate travel time intervals. This is computed by dividing the number of trips in each interval by the total number of trips by a given mode for a given purpose. The decay functions have been corrected for known issues in the NTS data and slightly adapted to maximize usability. Figure 13 shows the overall distance decay functions for commuting trips for bicycling and the other transport modes (on the left) and for bicycle trips for different trip purposes (on the right). 10

11 Figure 13: distance decay functions BikePRINT 3. CHANGES IN CYCLE BEHAVIOUR IN RESPONSE TO POLICY MEASURES 3.1 Policy support for future investments The second core module of BikePRINT provides policy support for future investments in bicycle infrastructure. The effects of infrastructure investments and better network performances can be estimated by simulating fewer delays on junctions, higher specific cycling speeds (overall network or link specific) and the construction of additional infrastructure. The baseline situation is based on empirical data such as travel times and delays derived from GPS tracking encompassing the whole bicycle network. The potential effects of infrastructure investments are shown by: 1. Interactive maps, which enable visual comparisons of the baseline situation and the new situation after infrastructural changes. 2. The estimated changes in potential bicycle accessibility for different trip purposes. 3. The number of extra bicycle trips and overall travel time gains for cyclists. The interactive map in figure 14 shows the effects of a new high speed cycle route in the Netherlands. BikePRINT users can add new infrastructure developments to the network which enables the comparison of a multitude of scenarios. On the left side of the map, current travel times are displayed from their origin to all destinations. The right side shows the changes in travel time due to the new bicycle infrastructure. As anticipated, the map shows that travel time gains are larger for longer distances. 11

12 Figure 14: Interactive map scenario module In addition to the interactive map, BikePRINT provides graphs that display the changes in overall potential bicycle accessibility, the potential number of additional bicycling trips and overall travel time gains for cyclists (figure 15). This graph also includes the length of the new bicycle routes. The rationale behind this is that more extensive infrastructure investments (i.e. longer routes) will, ceteris paribus, exert stronger effects on bicycle use. Inclusion of the length provides an indication of the development costs although it is acknowledged that bridges, viaducts and the like significantly influence the total construction costs. Figure 15: Typical output of scenario module 12

13 3.2 Changes in potential bicycle accessibility The changes in the overall bicycle potential accessibility are measured by the number of different type of destinations within reach. By default, these destinations include: workplaces, retail stores, schools and residents. Depending on the available spatial data, the number and type of destination types can be extended (or decreased). Calculation of the changes in potential bicycle accessibility is based on equation 1. The difference is that the potential accessibility for each zone is weighted by the number of persons living in origin zone I (Pi). This ensures that origin zones with a larger population have more weight in the calculation of changes in the potential accessibility compared to zones with smaller populations. This leads to the following equation (for each destination type): [Equation 3] The total weighted potential accessibility per person in the study area is calculated by adding up the weighted potential accessibility of all zones and dividing this by the total number of persons in the study area. Or: [Equation 4] This bicycle accessibility is calculated for the baseline situation and for the other scenarios; in figure 15 the difference between both is presented. Hence, the figures for the potential accessibility can be interpreted as the average number of destinations (per subtype) that is within bicycle distance of the residents of the whole study area. 3.3 Extra cyclists and travel time gains In addition to changes in potential bicycle accessibility the ambition was to provide estimates for the number of extra cyclists and the overall travel time gains for cyclists after improvements in the bicycle infrastructure. Here we were confronted with a dilemma. On the one hand, BikePRINT is designed as a quick scan which limits the amount of time for data processing and model complexity. On the other hand, a precise and accurate estimate of these effects would ask for a much more complex multimodal traffic model. We decided to keep the modelling structure straightforward and provide global but robust estimations of effects. This provides sufficient accuracy to enable the comparison of effects of different scenarios. 13

14 To estimate the number of extra cyclists and travel time gains a distribution of cyclists over the pairs of origins and destinations is required. This distribution is based on a single (production) constrained gravity model: Tij = k (Oi Dj) / F(cij) [Equation 5] where Tij is the number of trips from origin i to destination j, Oi is the number of trips originating from zone i and Dj is the number of trips terminating at destination j. The scalar, k, is simply the ratio of the maximum possible trips (total production) to the calculated number of trips using the unconstrained gravity model. The function F(Cij) is estimated to allow the model results to match the earlier mentioned distance decay function. The production and attraction factors are derived from the NTS dataset ( ), described earlier in this section. Depending on available data sources, these factors can be calculated for different trip purposes (e.g. commuting, shopping, attending school/education, visiting friends and family). The production factors are calculated by dividing the total number of trips for a certain purpose by the size of the related subpopulation in the study area. For instance, the production factor for commuting is calculated by dividing the total number of commuting trips by the size of the working population. The attraction factor for shopping is calculated by dividing the total number of shopping trips by the total number of jobs related to this branch. Based on these factors, the overall production and attraction of all zones is calculated for all travel modes together. In the second step of the modelling process, the share of bicycle trips in overall estimations for production and attraction is determined. In reality, the share of bicycle trips is determined by a multitude of factors including population characteristics, travel related attitudes and built environment characteristics such as densities and the availability and the quality of bicycle infrastructure (Bohte, 2010; Ogilvie et al., 2011). However, data availability often limits the number of determinants that can be used. Therefore, the relative share of bicycle trips is based on the level of urbanisation at the four-digit postal-code area level (overall density measure including inhabitants and work, schools, retail shops, leisure, etc). The bicycle shares are for each also derived from the NTS dataset by calculating the production and attraction specifically for the bicycle. The overall production and attraction of all zones is multiplied by the share of bicycle trips for the corresponding level of urbanisation. This results in the (unimodal) production and attraction of bicycle trips for each zone. Even 14

15 though this single determinant seems to provide a relatively crude estimation of the bicycle share, it should be noted that many other determinants such as attitudes and population characteristics are also related to the level of urbanisation. To a certain extent, the level of urbanisation also acts as a proxy for these determinants. Next, the gravity model is used to estimate the origins and destinations of the trips (Tij) per purpose. This results in a origin-destination (OD-)matrix that links each bicycle trip from an origin to a specific destination zone. This matrix is used to calculate the overall travel time gains for the existing bicycle trips. For each OD pair, the differences in travel time are multiplied by the number of bicycle trips. The total travel time gains are calculated by adding up the travel time gains on all OD pairs. These travel times are incorporated in the graph in figure 15. Note that we do not consider potential distribution effects in this calculation. In other words, we assume that cyclists will not change their destinations due to the improvements in the bicycle infrastructure. However, a certain number of travellers may decide to switch travel mode following the bicycle infrastructure improvements, resulting in a higher number of bicycle trips on the related links. As a full-blown multimodal traffic model is not possible in this quick scan framework, these potential mode shifts are estimated by applying competition curves. The bicycle competition curve describes the share of bicycle trips in the total amount of travel depending on the travel time. Figure 16 shows the overall competition curve and specific curves for trips with education and commuting purposes. The competitive position of the bicycle decreases as time intervals become larger. Furthermore, comparison of the individual curves shows that they significantly differ per trip purpose. In line with the calculation of the production and attraction, these curves are estimated for different levels of urbanisation at the four-digit postal-code area level. 15

16 Figure 16: Bicycle competition curve for different trip purposes The potential number of modal shift is calculated for each OD pair by dividing the current number of bicycle trips by the bicycle share from the competition curve that is related to the specific travel time on that link. Next, this outcome is multiplied by the (higher) bicycle share that is related to the new (shorter) travel time on this link after infrastructure improvements. This result provides an indication for the number of bicycle trips in the scenario situation. The resulting modal shift is calculated by subtracting the number of bicycle trips in the baseline situation from the number of trips in the scenario situation. Finally, these results for each OD pair are added up to provide the total number of additional bicycle trips. 4. CONCLUSIONS Extensive GPS tracking of cyclists can give thorough insight into travel behaviour and bicycle network performance. Information such as preferred (and avoided) routes, speeds, delays and accessibility can make bicycle planning and policy more targeted and efficient. When developing BikePRINT, we learned that many current road network performance Indicators make no sense for cyclists. To reduce this knowledge gap, dedicated indicators for cycling have been developed. Although extensive GPS tracking provides a rich additional source of information, it is neither intended nor suitable to replace traffic models or count data. We consider both sources of information as complementary. GPS tracking will provide detailed insight into cycle behaviour and route choices but they are less suited to estimate the total number of cyclists due to the limited 16

17 sample. Therefore, we aim to combine both data sources in the near future which would provide better estimates of the distribution of cyclists over the entire network. Nevertheless, we hope that the current version of BikePRINT already contributes to better bicycle planning and policy and will be used to increase cycle use in many cities. REFERENCES Bohte, W. (2010) Residential Self-selection and travel: The relationship between travel-related attitudes, BE characteristics and TB, Doctoral dissertation, Sustainable Urban Areas 35 (Delft: IOS Press). CBS (2015), Onderzoek Verplaatsingen in Nederland (2013) Onderzoeksbeschrijving OViN Data Archiving and Networked Services (DANS). Accessed 1st June NL/menu/themas/verkeervervoer/methoden/dataverzameling/korteonderzoeks beschrijvingen/ovinbeschrijving-art.htm. Ogilvie D, Bull, F., Cooper, A., Rutter, H., Adams, E., Brand, C., Ghali, K., Jones, T., Mutrie, N., Powell, J., Preston, J., Sahlqvist, S. and Song Y. (2011) Evaluating the travel, physical activity and carbon impacts of a natural experiment in the provision of new walking and cycling infrastructure: methods for the core module of the iconnect study, BMJ Open, 2012, 2:e ) BikePRINT is part of the Nisto project, which received EU Interreg IV B funding 2) 17