Quality of Real-Time Travel Time Information
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QUALITY OF REAL-TIME TRAVEL TIME INFORMATION A research to travel times presented to drivers in the Netherlands 19 September 2012 Master thesis, final report A.J. de Jong Quality of real-time travel time information Title Quality of real-time travel time information A research to travel times presented to drivers in the Netherlands Author A.J. (Joost) de Jong BSc student Civil Engineering and Management Supervisors ARCADIS ir. P.T.W. (Patrick) Broeren ir. M.E.J. (Martijn) Loot Supervisors University of Twente prof. dr. ir. E.C. (Eric) van Berkum dr. ir. J. (Jing) Bie Status Master thesis, final report Date 19 September 2012 Master thesis ARCADIS 1 Quality of real-time travel time information 2 ARCADIS :Master thesis Quality of real-time travel time information Summary Real-time travel time information becomes more and more important in nowadays’ busy traffic situations. For a driver to choose the shortest route, for a traffic manager to improve the traffic flow. Therefore both public and private information providers offer traffic information services. This research compares travel time information presented by Rijkswaterstaat (RWS), TomTom and Bluetooth services in the Netherlands. To measure the quality of the presented travel times the research contains a qualitative and a quantitative analysis. The first part creates insight in the operations of RWS and TomTom and describes the strengths and weaknesses related to accuracy, variety of data sources, relevancy, timeliness and accessibility. This second part contains a data-analysis to compare travel times of both providers at free flow and congested conditions. The analysis contains a description of trends in performances and measurements of accuracy by using the Mean Absolute Percentage Error (MAPE) and a two and one tailed paired student’s t-test. Rijkswaterstaat, the national traffic manager of the Dutch freeways, presents travel times on Variable Messages Signs (VMS) and uses loop detector data to calculate a travel time. Loop detectors however only measure speeds and intensities at certain points and a linear speed distribution between those measurement points is assumed to calculate an instantaneous travel time. Hereby the presented information could contain inaccuracies since changes of traffic flow between two loops are difficult to monitor, especially in congested conditions (for example with stop and go movements). The information is presented on VMS, which is freely accessible for all drivers on the road, but will only be relevant to the freeway part of a route. Next to that the used communication channel has some weaknesses; information should be read while driving at high speeds and after having passed the VMS the driver does not receive any updates. TomTom, the leading producer of navigation systems, also provides real-time travel time information, which is presented at in-car navigation systems. This navigation product is called TomTom HD Traffic. The company uses both historical and real-time traffic data, combined by data fusion, to calculate a travel time. Real-time travel times are directly measured by floating car data of GPS receivers in their navigation systems and location data of cell phones using the Vodafone network. This latter data source of GSM location data requires extensive filtering to use only active cell phones located in a vehicle. A weakness of the measurement methods is the penetration rate of the equipped vehicles. Both GPS and GSM data sources only measure a selection of the total traffic flow. Furthermore, due to the limited bandwidth only real-time data is used in case of real delays. If the measurements exceed the historical values and the real- time data source gets priority in de data fusion. Strengths of TomTom’s system are the travel time information is applied to the whole route including all type of roads, and up-to-date information is continuously accessible at the in-car device. Bluetooth travel times are measured by monitoring active Bluetooth devices. Matching MAC-addresses at different locations leads to a direct measurement of travel time. However the method is dependent on a penetration rate and only measures a travel time at the end of a section. This results in delayed detection of increasing travel times during congestion, which is a major drawback for accurate real-time information. In short, the qualitative analysis shows the travel time of TomTom is based on travel time measurements, relevant for the user-specified route and always accessible. In contrast, the indirect travel time calculation method of RWS uses average speeds at measurements points and assumes a linear speed distribution between those points to produce a travel time. Due to the characteristics of the communication channel the travel time information of RWS is only relevant to freeways and not accessible after passing the VMS. Master thesis ARCADIS 3 Quality of real-time travel time information The second part of this research include five scenarios to evaluate the accuracy of presented travel times during different traffic conditions. The scenarios contain a variation of different types of roads and monitoring systems to show the influence of the quality of the data collection on the presented travel time. Scenario 0: Nijkerk - Nunspeet (via A28 both directions) . Scenario 1: Hoevelaken to Utrecht (via A28 and A1/A27) . Scenario 2: Gouda to Den Haag and Rotterdam (via A12 and A20) . Scenario 3: Leiderdorp to Bodengraven (via A4/A12 and N11) . Scenario 4: Ede to Lunetten (via A12) Based on an users’ perspective, the travel times of Rijkswaterstaat are compared to TomTom HD Traffic travel times (in scenario 1, 2, 3 and 4) and/or to Bluetooth travel times (in scenario 0 and 4). In this way the available real-time information services are compared to determine whether differences appear. In scenario 0 and 1 also experienced travel times of individual TomTom users are added to check whether the presented information matches the experiences of drivers. In the data-analysis three types of days (free flow, medium congested and heavy congested days) are distinguished to check whether the performances of the travel time information systems are depending on the degree of delay. Furthermore the congested days are further divided in periods of free flow, increasing and decreasing congestion to determine the quality of the systems in encountering changes of traffic conditions. The data-analysis shows that despite the theoretically weaknesses of indirect travel time calculation based on loop data, the travel times of RWS compete with the travel times of TomTom regarding accuracy. The travel times are comparable for road sections with high density of good performing loop detectors. For other sections differences are found between travel times of RWS and TomTom during both free flow and congested conditions. Firstly, during free flow conditions the travel time of TomTom is smaller than RWS in all scenarios. The difference varies from less than 1 minutes to more than 3 minutes, depending on the specific route. The deviating travel times could be caused by an incorrect alignment of the routes, by the various performances of loop detectors or by ignoring or including slow trucks. Remarkable, the free flow values of TomTom does not match the experienced travel times of TomTom users. Next to that, for the non- freeway N11 the free flow travel time of TomTom is larger than the loop based travel time and does not confirm the results at freeways. Secondly, during increasing congestion the difference between TomTom and RWS is bigger than during decreasing congestion. Several trends are observed in the comparisons of the travel time peaks during all scenarios. For example, because a threshold is use to switch between historical and real-time traffic data in the data fusion of TomTom, the travel time of RWS starts showing delay slightly earlier than TomTom. Next to that, the increase of RWS’ travel time is smaller than TomTom whereby at high delays TomTom’s travel time become comparable or even exceeds the values of RWS. During decreasing congestion the performance could be divided in two trends. The travel times decline comparable or the peak of TomTom decreases faster than RWS. Next to these comparisons of RWS and TomTom travel times, this research shows among others the weaknesses of delayed detection of changes in travel time by Bluetooth detectors. This result suggest timeliness is a clear problem for these type of traffic data, whereby it is less useful for real-time travel time information provision. Finally, although the results of the qualitative analysis indicate clear advantages of the TomTom system, the data-analysis refines this conclusion. The RWS system is able to deliver accurate travel time information if a high density of loop detectors is available and no disturbing factors (like roadworks) are present. 4 ARCADIS :Master thesis Quality of real-time travel time information Preface This report is the result of my graduation project to the quality of travel time information. This research studied the operation of the travel time systems of Rijkswaterstaat and TomTom, by analysing the strengths and weaknesses. And compares the travel times in five scenarios, measuring the accuracy of the presented information. The research is the final assignment of my education Civil Engineering and Management, track Traffic Engineering at the University of Twente. The past six months I have worked at ARCADIS in Arnhem and learn a lot about the topic. Studying the theoretical strengths and weaknesses of the data sources and recognizing these characteristics is the real data was a nice job. Next to that contacting the related companies and convincing them to join this research was instructive. At the start of this report I like to thank all the people who have supported this research. Firstly my supervisors at ARCADIS for the very useful feedback during the whole project.