EFFECTIVE ROAD SAFETY MANAGEMENT USING NETWORK-WIDE HISTORICAL SPEED DATA COMMERCIAL SPEED DATA AS BIG DATA SOURCE TO IMPROVE ROAD SAFETY INTELLIGENCE

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1 EFFECTIVE ROAD SAFETY MANAGEMENT USING NETWORK-WIDE HISTORICAL SPEED DATA COMMERCIAL SPEED DATA AS BIG DATA SOURCE TO IMPROVE ROAD SAFETY INTELLIGENCE Timo Hoffmann Karin Hitscherich PTV Group 1. INTRODUCTION Black Spot Management (BSM) and Network Safety Management (NSM) techniques offer a structured approach to the management and treatment of parts of a transport network where road traffic collisions have historically been concentrated. These methods are described as guidelines in EU directive 2008/96/EC and are therefore in use in many European countries. Since about 2010, commercially available historical speed data from GPS measurements collected by navigation devices or mobile phones can provide a detailed picture of the driven speeds throughout the whole network. The providers are collecting, storing and processing the data, and are offering this big data for analysis purposes. This data creates a great opportunity for faster, cheaper and more detailed observed speed analysis and road safety evaluations. To effectively increase road safety, road authorities need to set up tools and processes for a comprehensive road infrastructure safety management. According to EU directive 2008/96/EC on road infrastructure safety management the identification of high risk locations and sections and their appropriate remedial measures is one of the most important processes in this regard. Historical crash data is a main input for these assessments. This paper shows that network wide, commercially available speed data can help to further increase road safety intelligence for the whole network. Several different use cases are described here, some of them exemplified by screenshots of real world applications using this sort of data. Rather than this being an academic paper, this is meant to be a guide for road safety experts and transportation planners alike who want to learn about the processes and possibilities of road safety infrastructure management using historical crash data and empirical speed data. 1

2 2. COMMERCIAL SPEED DATA AND ITS USE FOR ROAD SAFETY Commercial map data providers are offering custom speed data in selected countries. As opposed to speed data coming from measurement devices, this data was gathered by analysing a large number of vehicle GPS trajectories. A major advantage of this big data approach is the general availability of the data on the whole road network instead of just punctual information or for predefined routes like when travel time data is collected by Bluetooth device tracking. Another advantage is the effortless and fast availability of the data. The main caveat of this data source is that it does not account for all vehicles, but only those in which an enabled device is present (such as a navigation device, or smartphone). The devices do not report the type of vehicle the data is coming from, so speeds could be calculated using not only cars but potentially also trucks, buses or others. There are algorithms in place that filter some of those vehicles to get a representative speed reading for typical cars. Several providers offer network wide empirical speed data commercially. Among them are TomTom, INRIX and HERE (Nokia). In this paper, several exemplary applications of TomTom s product Custom Area Analysis are used. The data available allows for a custom definition of date period (data is generally available from 2008 depending on region/country in the case of TomTom data), weekly hour groups within this period (e.g. weekday morning peak, Friday & Saturday night) and for freely definable routes within the road network. For this kind of data, the per segment speed distribution in 5-percentile steps is also provided, allowing analysis of V85 values (85th percentile) and more. The importance of having information about the speed distribution as opposed to only average speed data can be seen in Figure 1. At an example road segment, a median speed is 45 km/h, v85 (85% of drivers are below this speed) is 55 km/h, the topmost 5% were driving 59 km/h and over. On the other side of the spectrum, 5% of the speeds are even below 32 km/h. In this example only 45 % of the vehicles less than half were going between 40 and 50 km/h (a speed one could have assumed the majority of cars going if the median speed is 45 km /h), 30% above 50 km/h, 25% below 40 km/h. 2

3 Figure 1: Median speed and speed distribution in 5-percentile steps on a random link segment We have tested this data and identified the following use cases, benefits and opportunities for road safety researchers and practitioners: 1. Compare design speeds with actual driving speeds 2. Assess the effect of road infrastructure changes or speed enforcement tactics on the driving speeds at the site and in its vicinity 3. Analyse the general speeding behaviour at crash sites where speed is a possible contributing factor 4. Check necessity for posting a general speed limit as opposed to time dependent speed limit changes 5. Improve the quality of transportation model outputs: travel times, emission, crash prediction 6. Calibrate the driving behaviour in microsimulations 1. COMPARE DESIGN SPEEDS WITH ACTUAL DRIVING SPEEDS Roads are generally designed to safely handle a predefined speed and a maximum expected speed. In most cases a corresponding speed limit is explicitly posted or implicitly given, but this is not always the case. Also, over time, changes in maximum speed limits can occur or the speeding likelihood may change due to different factors. In turn, a mismatch between initial design 3

4 speed and the speeds generally driven might arise. Speed data can now be used to look for network elements where such discrepancies occur. The idea of so-called self-explanatory roads is proposed by road safety experts, meaning that sustainable, successful roads are able to convey necessary messages of themselves to road users, for example, their safe driving speed. If the general speeding behaviour of certain parts of the road network as observed through speed data is not in line with the assumed safe driving speed, speed-related high risk sections can be singled out. Since the location of these segments within a road network is not known to begin with, a network wide approach to find these sections is necessary. This means, traditional sources of speed data cannot be used for this use case, because they deliver speed information either on individual point locations only (speed cameras, loop detectors etc.) or of routes (Bluetooth measurements, automatic number plate recognition based systems etc.). The absolute speeds driven on a road network varies naturally according to road class and given speed limits among other influencing factors. This is why an analysis of actual absolute speeds driven on a road network is per se not of high interest when aiming for a network wide analysis with a focus on representative user behaviour. Rather the use of a ratio of actual speeds driven versus design speed seems most appropriate for analytical purposes. For both of these values ( speeds driven and design speed ), different definitions or ways of representing them are possible. Speeds driven could be the median, the 85 th percentile of the speed distribution or another derived value from the speed data set and each of these values could be evaluated for different time periods: night times, weekends, rush hours, winter time, vacation time, etc. The design speed of roads is a value which might not be available for the entire road network. An appropriate proxy value is the speed limit, since there is a strong linkage between those two variables. The speed limit itself can either be the posted speed limit or the implicitly given legal speed limit in the given environment (e.g. urban roads). Another advantage of using this value is the better availability of this data set in digital form for the whole road network. A third option for the denominator of this ratio could also be a calculated free flow speed which is available as a base attribute for commercial digital road data used for navigation. 4

5 Which combination(s) of the values and time periods described above for the calculation of the ratio yield the best results in terms of identifying critical sections in the road network in terms of speeding behaviour is an area of either further research or best practice analysis. In practical terms, there will be different approaches depending on the objectives and data availability. The introduction of a new speed limit regime or the upgrading of those parts of the network where the speeding behaviour does not match the intended speeds can be focus of a Systemic Safety Approach to improve the inherent safety performance of this part of the network. Figure 1: Digital road network of Basel showing ratio of night time (free flow) speeds vs. morning peak speed in different colours Figure 1 shows for the network of main roads in Basel, Switzerland the ratio between the measured median speed during night hours ( free flow speed ) and during the morning peak. By displaying the speeds in a planning system it is possible to run a great variety of analyses and identify locations of potential risks. Figure 2 displays for one part of the road network of London the free flow speed and the empirical speed for early Sunday morning hours. It shows areas where driven speeds are higher than free flow speeds. 5

6 Figure 2: Digital road network of London showing free flow speeds and Sunday night/early morning speeds Both Figure 1 and Figure 2 are examples of how using software analysis and visualization tools together with speed data can give road safety professionals an additional layer of information toward their road safety situation. 2. ASSESS THE EFFECT OF ROAD INFRASTRUCTURE CHANGES OR SPEED ENFORCEMENT TACTICS ON THE DRIVING SPEEDS AT THE SITE AND IN ITS VICINITY For many crash black spot treatments, speed reduction is a main goal. However it is hard to evaluate the long term effect on speeds of any measure at the treatment site and its surroundings. There may not have been a local speed measuring device at the site of the planned intervention for a long enough time to get enough historical speed data to observe trends and variations in the before period. Typically in order to get this necessary data to evaluate this, the road administration would need to go through the following steps: 1. Speed measuring device acquisition 2. Device installation on the road 3. Ideally several months wait during data collection 4. Subsequent data analysis Some speed measurements (e.g. by visible speed cameras) can influence speeding behaviour, thus rendering the observation faulty to begin with. 6

7 Commercially available speed data can act as an alternative to this long and costly process. Another advantage of the commercially available speed data is its general availability throughout the network, making area wide or multiple site analysis possible. The necessity for this kind of evaluation can easily be demonstrated with an example of an implemented traffic calming measure on an urban road with high traffic volume. If the chicane in place is encouraging a small percentage of drivers to take a quick detour route through side streets meant for low speeds, there could be a serious road safety problem in the vicinity of the original black spot that was intended to be treated. In order to estimate the effect of capacity reducing measures on traffic distribution, microscopic simulations can be use, to assess then subsequently the effect of these capacity reductions on driver route choice (to get an idea on where to the traffic diverts because of this) macroscopic models can be used (see chapter 5). Speed data can now be used to get an indication of the historical speed distribution as well as the changes in speeds after implementation of the treatment without influencing the behaviour during the measurement period in any way. This is in addition to what is possible with traditional single location speed camera measurements also available in adjacent parts of the road network. With this information then it is possible to evaluate, how well any treatment a simple change in signage or road markings, a rebuilt road design, a new traffic signalling, temporary or fixed speed cameras etc. is able to reduce the speeds at the location of the treatment and in its vicinity and for how long. Figure 3 shows a comparison of speeds driven on Thursday mornings (between 7am and 10am) on London s Super Cyclehighway 2 before it was build (2010; top) and after (2012; bottom). In this case some slight changes in the speeds before and after the introduction of the cycle lane can be observed. Overall there was a slight decrease in travel speeds after the introduction of the Super Cyclehighway 2, best seen around the intersection of High Street with the A12. This reduction of speed can of course also be due to some other factors which changed in between the times of both observations. 7

8 Figure 3: Comparison of speeds before and after implementation of a Super Cyclehighway 3. ANALYSE THE GENERAL SPEEDING BEHAVIOUR AT CRASH SITES WHERE SPEED IS A POSSIBLE CONTRIBUTING FACTOR If speeding was a contributing factor of a crash is not always easy to assess. In turn, the quality of this attribute in the crash data if available is mediocre at best. Commercial speed data can help to identify if speeding is generally a problem at a high risk section and at what times. 8

9 The police officer called to the site of a road crash needs to assess the evidence on site as well as witness testimony in order to find out if speeding was a contributing factor in the crash. Both sources are not easy to interpret. If not properly trained, it will be guesswork in regards to the value of the speeding attribute in the official forms. This is why an objective and quantitative statement of the speeding situation at a crash black spot is hard to come by with traditional means. For Black Spot Management purposes it is important to get an idea of the common contributing factors within a certain location with a high crash frequency after it has been identified. These common contributing factors generally have to be derived by analysing the attributes of the crashes provided in the data sets. Since speeding information is not available in the crash data set or quality of this data attribute is low, additional data sources can be considered to get an indication of the speeding behaviour at those locations. Speed data information is generally only available on the road sections and not within an intersection. Thus a workflow for this use case can be: 1. Find black spot locations with suspected speeding problems 2. Pre-screen for black spots on link (filter out intersections) 3. Select a black spot for further investigation 4. Check in what time periods within a day, what weekdays and what time of the year crashes occur at this site 5. Define time periods for the speed data with the results of step 4 in mind 6. Define route along which speed data should be bought 7. Acquire the data 8. Analyse the speed data The analysis can be based on several speed data attributes. The most promising are: Median speed v85 (85th percentile of the speeds driven) v95 (top 5 % of speed distribution) variance/differences of speeds (vmax - vmin) 9

10 Figure 4: Visualization of crash locations surrounding London s Cycle Superhighway 2 with speed information In Figure 4 empirical speeds of a section of London s Super Cyclehighway 2 during morning peak hours are displayed alongside crashes in that region where cyclists have been involved. Further analyses of the driven speeds can be based on comparison of different types of weekdays and hours of the day as seen in Figure 5. Figure 5: More detailed view of median speeds driven at various times along London s Cycle Superhighway 2 10

11 4. CHECK NECESSITY FOR POSTING A GENERAL SPEED LIMIT AS OPPOSED TO TIME DEPENDENT SPEED LIMIT CHANGES Building on top of the previous use case, if knowledge is available on when critical speeding occurs, the road administration has the option to consider temporal speed limits (e.g. only at night times), which might be in line with local or regional policy to not impose unnecessary speed limitations while at the same time consider road safety aspects. Limiting speeds with speed limits is often a controversial issue, with both sides having fierce advocates. On the one hand there are residents, local action groups, etc. calling for speed reductions, on the other hand motorists demand a certain allowed speed at least on main and major roads. One possible way of meeting demands from both sides can be to introduce temporary speed limits. In order to find out for what times speed limits and their enforcement are most needed, both historical crash data as well as speed data can be evaluated. With this approach, it is possible to selectively reduce speeds at times when in the past crashes occurred and when speeding is the most prevalent problem while at the same time allow certain travel times when they are needed and they don t pose a road safety issue. With the advances in technology and equipment in the ITS (Intelligent Transportation System) field, dynamic speed limits even for non-motorway type roads are possible from a technological as well as financial point of view. Even if not used within a larger traffic management solution for a larger area, a fixed program perhaps with some sensor-based adaptivity (weather sensors to detect rain, chance of ice, wind gusts, fog etc.) are possible. This way, various pre-defined speed limits according to time of the day, day of the week, holiday, vacation or event times etc. can be configured and automatically activated. Due to heavily formalized procedures in the UK, Europe in general, North America and other predominantly high income countries, this approach might be more feasible in lower and middle income countries where incidentally also a higher modal share of vulnerable road users especially pedestrians are on the road at certain times. Rather than setting up more or less arbitrary speed limits and time periods for them, the analysis of speeds driven at various times in the past can help to define a customized schedule for time-dependent speed limits. Setting up 11

12 speed limits at particular places and during certain times only will likely improve acceptance levels while at the same time reduce the effort for enforcement. 5. IMPROVE THE QUALITY OF TRANSPORTATION MODEL OUTPUTS: TRAVEL TIMES, EMISSION, CRASH PREDICTION Macroscopic transportation models can help analyse whether a high number of crashes on a road or a junction is related to high or small volumes of traffic and how the volume of traffic at a site will react on changes in the network. The latter is needed to evaluate the road safety impact (Road Safety Impact Assessment RIA), which is described in more detail later in this paper. A more direct application of transportation models in the context of road safety is the generation of crash rate information. The crash rate of an intersection is usually calculated using the following function: C = with: A V 365 C A V Crash rate of the intersection Average number of crashes at the intersection per year Average Daily Traffic volume (ADT) of the intersection Likewise the crash rate of a road segment is calculated using the following function: C = with: A L V 365 C A V L Crash rate of the road segment Average number of crashes on the segment per year Average Daily Traffic (ADT) of the road Length So the crash rates are given as crashes per million vehicles per year at intersections and with crashes per million vehicle kilometres (or miles) per year at road sections. For both of these rates ADT (Average Daily Traffic) information is necessary. For individual locations, this data set can be measured using 12

13 counting devices. For Network Safety Management (NSM) purposes, a network-wide approach is necessary to be able to calculate crash rates on all parts of the given road network. To generate approximate values for average daily traffic data, transportation models have been used for several decades already this is state of technology. To calculate the volumes in a transport model the modelled origin-destination matrix has to be assigned to the network. One main input for the assignment is the impedance of a route which is directly related to the travel time on the route and thus is depending on the number of vehicles using it. This relationship is described in so called capacity-restraint-functions or volume-delayfunctions. These functions can be different for several road types and have to be calibrated. Figure 6 shows some examples of volume delay functions indicating the amount of travel time increase for more traffic on a road. For the calibration both volumes and speeds are necessary. The first are only available from loop detectors whereas the second type of data is not always available. Also the quality of speed data at loop detectors in cities is dependent from the type, age and location of the measurement (influence of signalized junctions). Here again commercial speed data can increase the quality. Figure 6: Examples of volume delay functions used in macroscopic planning models for traffic assignment In conclusion, this use case uses network-wide speed data to calibrate a transportation model which estimates traffic volumes to then calculate more realistic crash rates throughout the network. A better calibrated transportation model is also able to calculate future scenarios with a higher quality. So called 13

14 Road Safety Impact Assessments (RIA) examine the road safety implications of a certain infrastructure measure. RIA has become more important in several countries, where they become obligatory as part of the cost-benefit analysis for infrastructure investments (e.g. Swiss official guideline SNR ). A prevalent approach is to compare the costs of crashes in two or alternative road routes and design characteristics on a strategic level. One major input data for examinations like this are the traffic volumes on all links of the base scenario and all alternatives. It is recommended to derive the volumes from a transportation model. Figure 7 shows an example of a scenario evaluation which shows the expected shift in traffic volume numbers after the opening of a new bypass. Road segments where more traffic can be expected are shown in red, the thickness of the bar corresponds with the amount of extra traffic volume. Roads where a decrease in traffic volume is expected are shown in green. Figure 7: Modelled shifts in traffic flow after a new bypass When adding relevant safety indicators to the model a potential change in the number of crashes and the crash costs can be calculated by using the volumes identified for the different scenarios which in turn were calculated using the official guideline. The scenarios can then be evaluated under safety aspects. The evaluation result will support decision makers to find the best alternative. 6. USAGE AS PARAMETER FOR CRASH PREDICTION MODELS (CPMS) CPMs are used by RIA use case above for future scenarios, but also to understand the expected number of crashes at a specific location. This is 14

15 helpful to understand if an accumulation of crashes at a certain location is unusually high or if a number close to the observed number of crashes is expected because of the amount of traffic and other circumstances at this location. CPMs consist of one or several functions which derive the number of crashes (of a certain kind or severity) using various parameters. These parameters can, if the data is available, also contain characteristic speed related values like the median speed, v85 or others. If historical crash data and network related data is available, a CPM can be derived making use of a selection of parameters, speed being an obvious one, since especially the severity of crashes is directly and strongly related to speeds driven. When a CPM has been defined for a region in such a way, it is possible to quantify the model-based influence of speed. Proposed speed reductions can then be evaluated in regards to their effect on crash occurrence. CPMs never can predict the exact number of crashes, as these will fluctuate depending on various factors Nevertheless, model based approaches using speed data as parameters derived from observed historical crash data have been created and are in use in several countries. 7. CALIBRATION OF MICROSIMULATIONS TO BETTER MODEL THE REAL DRIVING BEHAVIOUR The use of microsimulation software to assess safety levels is a growing trend among researchers and consultancies specialized in road safety evaluations. The most commonly used toolset for this is PTV Vissim as microsimulations software and SSAM ( Safety Surrogate Analysis Module ) as analytical engine for surrogate measures to assess safety. Typical safety surrogate measures used for safety evaluations from either real life video observations or microsimulations are Time To Collision (TTC) or Post Encroachment Time (PET). While PTV Vissim offers a default driving behaviour and speed distribution functions, the quality of the model (i.e. how well the model simulates "real" driving behaviour) can be increased by using local speed distributions from the commercially available speed data as input. Also, total travel times can be compared between the model output and the measured data to calibrate iteratively until speeds are modelled very realistically by the simulation. 15

16 Figure 8: Desired speed distribution setting dialog in microsimulation software PTV Vissim Models and their resulting simulations which have been calibrated in such a way will also result in safety surrogate measures of a higher quality. In turn the safety-focussed comparison of e.g. different intersection layouts using this methodology yields better results. 3. CONCLUSION The example use cases above describe some interesting approaches of adding speed related road safety intelligence to standard tool based and data driven road safety processes. Currently available speed data can mostly in addition to historical crash data and network related infrastructure parameters provide a deeper insight for crash risk evaluation and measure effectiveness analysis because for some of these evaluations it is essential to have speed data for a large area instead of individual locations only. 16