ANALYSIS OF TRAVEL TIMES AND ROUTES ON URBAN ROADS BY MEANS OF FLOATING-CAR DATA 1. SUMMARY Ralf-Peter Schäfer Kai-Uwe Thiessenhusen Elmar Brockfeld Peter Wagner German Aerospace Center (DLR) Institute of Transport Research The paper outlines an approach analyzing travel time and routes by GPS floating car data. GPS data from a Taxi client disposition system are used for real-time traffic information services. For our analysis, the frequently recorded positions of 300 cars of a Berlin Taxi company are exploited. Since May 2001 several millions of position data from the associated Taxi fleet have been collected. These data are an excellent basis for data mining analyzing the daily variation of travel time on almost all of the major roads of Berlin. Various commercial applications just as real-time traffic monitoring, timedynamic routing or fleet management can benefit from the results. 2. INTRODUCTION Heavy traffic including congestion can be observed on urban roads all over the world. In view of limited space for new roads, it is necessary to improve the exploitation of the existing network. In order to solve this problem the number of telematics applications has been rapidly grown over the last couple of years. Mainly new wireless communication and location-sensing technology pushed this development. Consequently, traffic management and mobility services providing online travel information for road users were established over the last years. Traffic information is usually broadcasted via radio or TV as well as via modern channels like the Internet or cellular phone networks. Conventional data sources for traffic information are based on traffic volume measurements by inductive loops or infrared sensors. The major problem of this approach is the reconstruction of travel time and the routes of the vehicles in the road network. In this paper a more reliable method is discussed analyzing the travel time and routes of vehicles on urban roads. The approach is based on floating car data (FCD) using the global positioning system (GPS). Nowadays an increasing number of commercial vehicles are equipped with such GPS devices. In that context, we established a project with a Berlin Taxi company. 300 Taxis equipped with a GPS receiver and a wireless communication device are used as FCD data supplier. Once per minute each Taxi sends its current position to the Taxi headquarters, where the data are processed for the online Taxi client disposition. Using synergies from this Taxi disposition system, we developed a
real-time FCD traffic information system. In May 2001 the real time operation of our FCD system was started, exploiting the GPS data of the Taxi disposition software. Since May 2001 several millions of position data from the associated taxi fleet have been collected. These data are an excellent basis for data mining and for analyzing the daily variation of travel time on almost all of the major roads of Berlin. In the paper, we analyze the traffic situation on different road types. Additionally, the reasons for fluctuation patterns are discussed. There are several factors causing the travel time to vary from day to day. The general traffic demand, weather condition, seasonal effects, events or roadwork influence the behavior of the vehicles. With this input we are able to generate travel time maps for the Berlin road network and a routing algorithm using these maps. The results of our research are the basis for real-time traffic monitoring and management as well as for real-time travel time forecast in urban networks. Furthermore, our analyses are used for fleet management of a Berlin waste disposal company. Our routing algorithms are implemented for the optimization of routes of the vehicles as well as for the reduction of fuel consumption. 3. FCD SYSTEM ARCHITECTURE For the travel time and route analysis GPS positioning data from a taxi fleet in Berlin were used. In contrast to stationary traffic sensors, where only the local velocity of the vehicles can be measured, floating car data cover continuously the route and speed of the vehicles by frequent detection of the positions using the GPS. Meanwhile, the GPS is widely used in various commercial transportation companies. There are several advantages for doing the fleet disposition with the aid of GPS, especially the optimization of route choice and route scheduling. Furthermore, the dispatcher can contemporary change the disposition scheme, even when the execution of orders is delayed by congestion or other troubles. In that context a traffic information system using GPS FCD was designed and implemented. The back-end part of the environment consists of the up and running taxi fleet disposition system. Each taxi is equipped with a GPS receiver and a wireless communication device. Taxi companies usually use own communication infrastructure and frequencies. This has the advantage that no communication expenses are incurred. Additionally, the hardware and software on-board of the vehicles and the head quarters have not to be changed. When the taxi is not waiting each vehicle transfers twice per minutes the GPS position, the timestamp, the vehicle identifier and status information (occupied, searching, etc.) via the communication network to the taxi headquarter, where
the data are stored in a data base system. In case of a client order the dispatcher has information about the nearest taxi to the client location. In order to exploit the GPS data for traffic monitoring we only had to establish a communication link to the data base server at the taxi headquarters via an ISDN phone line. The data transfer between the server at the DLR and the taxi company is done in real-time using an XML-protocol. At the DLR server site the data are preprocessed extracting the velocity and driving direction of each vehicle and separating non-relevant data e.g. waiting taxis, as well. The preprocessed data are stored in a data base management system, where an easy access from the client user interfaces is guaranteed. Furthermore, other data analysis modules like map matching have access to the data via a uniform and fast interface. The algorithms and results of the travel time and route analysis are discussed in the next chapters. The user interface of the traffic monitoring system is designed as client/server architecture and implemented in JAVA. The visualization tool consists of a digital map and various graphical layers, as well. The bottom one is a digital road map visualizing the major road network, the city border or lakes and rivers of the underlying urban area. Optionally, several other layers can be overlaid. One example is the positioning layer showing the GPS location data of all vehicles, other layers give travel time and velocity information in a grid or edge representation. For the visualization of the edge velocities a mapmatching algorithm has to be applied. In Fig. 1 the GPS positions of 144 taxis on May 24, 2002, between 1pm and 2pm are shown.
Figure 1: Visualization of Taxi positions and velocities in Berlin; GPS data of one-hour timeframe, selected jams are marked The GPS positioning data on the above map are classified by different velocity classes. The used colors are seen in the legend of the map. Positions in red or orange color show vehicles in slow driving mode with a mean velocity below 20 km/h since the last position. Following this rule jam trajectories are easy to detect by red colored position chains. The start and end of a congested road could also be detected including the travel time to pass the traffic jam. 4. ANALYSIS OF GPS POSITIONING DATA - GLOBAL FEATURES Beside the characteristics of travel times on a certain road (see next section), the GPS positioning data also deliver constraints on general properties of the Berlin traffic. Figure 2: Daily speed profiles of all GPS-taxi data. The different colors denote the different days of week. All plots have been averaged over all weeks in the first half of the year 2002. The size of a time sample is 5 minutes. Fig. 2 shows the averaged daily speed profiles for each day of week averaged over the time from beginning of January until the end of June 2002 (German public holidays on Mondays--Fridays are not included). The velocities are calculated from data of all taxis averaged over sample ranges of 5 minutes. Totally, about 7 million data have been taken into consideration for this plot.
The working day profiles are very similar for all days from Monday up to Thursday. The speed continuously decreases in the morning hours. The reason for the lack of characteristic morning rush hour depletion is that the data considered for this plot are dominated by taxis concentrated in the city area. The roads usually used by commuters to move into the city are used to a smaller amount only. Later in the morning, around noon, and in the early afternoon the average speed is relatively low and nearly constant. After a minimum at the afternoon rush hour, the speed increases again. Fridays are only slightly different. The daytime velocity is slightly lower, and the minimum occurs one hour earlier than at other days. Averaged velocities on Saturdays and Sundays are, as can be expected, significantly larger. Figure 3: Daily speed averages at working days (red), Saturdays (green), Sundays (blue), and public holidays (purple) from January until June 2002. Beside these effects, the traffic situation is influenced by a large variety of externalities. While several of them, e.g. road construction works, in the most case influence the traffic in a local area only others will show features in larger parts of the city or even the whole area. Such effects, of course, are visible also in the studied taxi data, and vice versa, the taxi data give indication for the existences of such effects. Typical examples are weather conditions or other seasonal effects (holidays). Also, some cultural or political events may influence the traffic situation in larger parts of the city.
Fig. 3 displays the daily speed average of all taxis for the days from January up to end of May 2002. Days from Monday through Friday are marked red, Saturdays, Sundays, and public holidays are marked green, purple, and red, respectively. Each average has been calculated from approximately 50000 GPS data values. Of course, the daily averages on weekend days are larger than on working days, and on Sundays and public holidays also larger than on Saturdays. While this is a rather obvious effect, several more interesting features are visible in the data: On certain days, the average speed was significantly small than in average. The strongest depletions at days 43 and 53 (February 12, and 22) are related to days with bad weather conditions, typically snow and ices. The winter 2002 was rather mild in Berlin, and only very few days had for a few hours of snowy conditions. Around day 90, a slight increase in working day speed can be seen. This is due to a Berlin school holiday time. At these periods, the traffic is generally lower than during school seasons, and, therefore, the averaged speeds are higher. Figure 4: Averaged speed (in km/h) as a function of time of the day (given in hours) in Berlin-Kreuzberg and Neukoeln, an area with a large Turkish community. The green solid line is the averaged Saturday profile, the red crosses are the data from Saturday, June 22 nd, 2002 with the world championship football game Turkey vs. Senegal between 13.30 and 15.30.
One example for the influence of a special event is shown in Figure 4. Here, the averaged speed in a region including the Berlin districts Kreuzberg and Neukoeln (with a large Turkish community) is plotted. The crosses show the averaged speed, on Saturday, June 22 nd, 2002, the solid red line the speed profile in this part of Berlin averaged over all Saturdays from January until the end of June 2002. On June 22 nd, there was the football world championship match Turkey vs. Senegal. Two significant features can be observed: during the match, from 13.30 until about 15.30, the observed speed values are significantly larger than in Saturday average. The reason, it can be assumed, is that many people watched the game in TV, and the streets were less crowded than on average. After the end of the game, the averaged speed drops rapidly by more than 10 km/h, significantly below Saturday average. Turkish people like to celebrate the winning of their team by cruising in their cars through the streets. This leads to a traffic-breakdown in several parts of the studied region, which is expressed in the decrease in average speed. 5. TRAVEL TIME AND ROUTE ANALYSIS USING MAP MATCHING Since May 2001 more than 20 million of GPS positioning data of the taxi fleet were collected in Berlin. The database was used for travel time and route analysis on major roads. In order to receive the right reference of each GPS position to the underlying road network it is necessary to implement a mapmatching algorithm. The used digital map of Berlin consists of approximately 50000 edges covering all major roads. From each GPS data set the nearest edge was calculated. Positions where the distance to the nearest edge was larger than a certain threshold value had been neglected as well as data from vehicles with unreliable driving behavior (e.g. waiting taxi). As has been mentioned already, the classification of the driving behavior can be evaluated by eight different status informations from the taxi. The matching principle is shown in Figure 5. GPS positions road net reliable band banddistance Figure 5: Matching principle of GPS position data to a digital road map
In view of the high accuracy of the GPS signal the reliable band was chosen to be 15 meters. However, in case of a position near an intersection (overlapping area of the reliable bands) a mismatch to the right edge reference can occur. With respect to the high number of positioning data in our test site this effect can be neglected, too. In order to eliminate further inconsistencies a Dijkstra routing algorithm can be applied for each vehicle trajectory. The algorithm returns all nodes and edges of a route with respect to the underlying digital network. Matched road edges from the GPS positions, which are not included in the returned node list are misinterpretations and therefore have to be substituted or eliminated. After the successful matching procedure, the velocities computed for any edge can be separated by weekday and hourly variation for each day. Furthermore, special traffic variation in case of events like demonstrations, soccer games, unusual weather conditions or vacations can be separately processed. In the next picture the results for hourly variations of the edge velocities in Berlin are shown. The plot is a result from GPS data recorded between Monday and Friday. Figure 6: Road velocities in Berlin between 5pm and 6pm (weekday) congested areas are marked This knowledge of the mean velocity on the major roads of Berlin is an excellent basis for a realistic travel time calculation. In contrast to existing navigation systems the calculated travel time for a given route is quite more
reliable because the system uses measured velocity from of the associated taxi vehicles. Additionally, optimal routes with respect to a chosen start time could be proposed. In the next figure a screenshot of our implemented router system is shown. The user has access to the router via an Internet site and receives for given start and destination points the shortest and fastest route information including the relevant distances and travel times. The knowledge of optimal route for a specific weekday and time of the day including the expected travel time has strong effects on the disposition of the fleet in commercial transport companies. This can lead to a strong reduction of travel time executing scheduled work orders. Blue: Fastest path red: shortest path Distance Destination Travel time Shortest Fastest path Start Figure 7: Output of the router system for a trip between the DLR and the Berlin Tegel Airport; start time 1 p.m. on a Thursday 6. CONCLUSION The floating car data (FCD) are a very good supplement to conventional stationary traffic sensors. In contrast to conventional approaches the travel time and routes of vehicle can be detected more reliable. The data collection of FCD using GPS data from fleet management system is a very cheap traffic sensor because synergies could be used. In a pilot real-time FCD system for the German capital Berlin the GPS data of a taxi fleet with 300 vehicles were analyzed in terms of travel time and routes of the associated vehicles. The
results have shown that the mean travel times behavior in urban area can be good reconstructed. Meanwhile, at the DLR further application in Vienna and Nuremberg with a higher density of vehicles were established.
7. BIBLIOGRAPHY Lewis, H. (1992) The Consultants Complete Proposal Manual, PTRC, London. Etches, A. (2000) A Temporal Geo-Spatial Database in support of an Integrated Urban Transportation System, Zagel, B. (Ed.), GIS in Transport und Verkehr, 33-44. Gordillo, S., F. Balaguer, C. Mostaccio, and F. des Neves (1999) Developing GIS Applications with Objects: A Design Pattern Approach, GeoInformatica 3(1), 7-32. Guptill, S.C., and J.L. Morrison (1995) Elements of spatial data quality, Elsevier, Oxford. Helbing, D. (1997), Verkehrsdynamik, Springer, Berlin, Heidelberg, New York, 308 pp. Klein, L.A. (1997), Vehicle Detector Technologies for Traffic Management Applications, Part 1. Streit, U. (1995) Statistical Analysis of Spatial Data in Geographic Information Systems, Bock, H.H., and W. Polnsek (Eds.), Data Analysis and Information Systems. Statistical and Conceptual Approaches, Vol. 7, 208-216.