A method to estimate traffic penetration rates of commercial floating car data using speed information
Abstract
Floating Car Data (FCD) are being increasingly used as an alternative traffic data source due to its lower cost and high coverage area. FCD can be obtained by tracking vehicle trajectories individually or by processing multiple tracks anonymously to produce average speed information commercially. For commercial FCD, the spatio-temporal distribution of these vehicles in actual traffic, traffic Penetration Rate (PR) is the most important factor affecting the accuracy of speed estimations, despite the high number of registered vehicles feeding to an FCD provider, denoting the market PR. This study proposes a method for assessing the traffic PR of commercial FCD by evaluating its speed estimation quality compared to Ground Truth (GT) data. GT speed data were employed to generate different levels of traffic PR using Monte Carlo (MC) simulations, which resulted in the development of Quality-PR (Q-PR) relations for Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) as selected Measures of Effectiveness (MoE). Simulation-based FCD results at an urban road segment in Ankara (Turkey) showed that a quality of FCD with traffic PR of 15% or more would improve significantly. Use of the developed Q-PR relations suggested an approximately 5% traffic PR for the commercial FCD speeds at the location.
Keyword : commercial floating car data, floating car data quality, penetration rate, probe vehicle, Monte Carlo simulations
How to Cite
Altintasi, O., Tuydes-Yaman, H., & Tuncay, K. (2022). A method to estimate traffic penetration rates of commercial floating car data using speed information. Transport, 37(3), 161–176. https://doi.org/10.3846/transport.2022.17069
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Brockfeld, E.; Lorkowski, S.; Mieth, P.; Wagner, P. 2007. Benefits and limits of recent floating car data technology – an evaluation study, in 11th World Conference on Transport Research, 24–28 June 2007, Berkeley CA, US, 1–14.
Cascetta, E. 2009. Transportation Systems Analysis: Models and Applications. Springer. 742 p. https://doi.org/10.1007/978-0-387-75857-2
Cetin, M.; List, G. F.; Zhou, Y. 2005. Factors affecting minimum number of probes required for reliable estimation of travel time, Transportation Research Record: Journal of the Transportation Research Board 1917: 37–44. https://doi.org/10.1177/0361198105191700105
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Grengs, J.; Wang, X.; Kostyniuk, L. 2008. Using GPS data to understand driving behavior, Journal of Urban Technology 15(2): 33–53. https://doi.org/10.1080/10630730802401942
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Hu, J.; Fontaine, M. D.; Ma, J. 2016. Quality of private sector travel-time data on arterials, Journal of Transportation Engineering 142(4): 04016010. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000815
INRIX. 2018. I-95 Vehicle Probe Project II: Interface Guide. 119 p. Available from Internet: https://tetcoalition.org/wp-content/uploads/2015/02/I-95_VPP_II_Inteface_Guide-March_2018.pdf
Jenelius, E.; Koutsopoulos, H. N. 2015. Probe vehicle data sampled by time or space: consistent travel time allocation and estimation, Transportation Research Part B: Methodological 71: 120–137. https://doi.org/10.1016/j.trb.2014.10.008
Jenelius, E.; Koutsopoulos, H. N. 2013. Travel time estimation for urban road networks using low frequency probe vehicle data, Transportation Research Part B: Methodological 53: 64–81. https://doi.org/10.1016/j.trb.2013.03.008
Kerner, B. S.; Demir, C.; Herrtwich, R. G.; Klenov, S. L.; Rehborn, H.; Aleksic, M.; Haug, A. 2005. Traffic state detection with floating car data in road networks, in Proceedings: 2005 IEEE Intelligent Transportation Systems, 13–16 September 2005, Vienna, Austria, 44–49. https://doi.org/10.1109/ITSC.2005.1520133
Kessler, L.; Huber, G.; Kesting, A.; Bogenberger, K. 2018. Comparing speed data from stationary detectors against floating-car data, IFAC-PapersOnLine 51(9): 299–304. https://doi.org/10.1016/j.ifacol.2018.07.049
Kim, S.; Coifman, B. 2014. Comparing INRIX speed data against concurrent loop detector stations over several months, Transportation Research Part C: Emerging Technologies 49: 59–72. https://doi.org/10.1016/j.trc.2014.10.002
Klunder, G. A.; Taale, H.; Kester, L.; Hoogendoorn, S. 2017. Improvement of network performance by in-vehicle routing using floating car data, Journal of Advanced Transportation 2017: 8483750. https://doi.org/10.1155/2017/8483750
Nigro, M.; Cipriani, E.; Del Giudice, A. 2018. Exploiting floating car data for time-dependent origin–destination matrices estimation, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 22(2): 159–174. https://doi.org/10.1080/15472450.2017.1421462
Ramezani, M.; Geroliminis, N. 2015. Queue profile estimation in congested urban networks with probe data, Computer-Aided Civil and Infrastructure Engineering 30(6): 414–432. https://doi.org/10.1111/mice.12095
Ribeiro, M. D.; Larrañaga, A. M.; Arellana, J.; Cybis, H. B. B. 2014. Influence of GPS and self-reported data in travel demand models, Procedia – Social and Behavioral Sciences 162: 467–476. https://doi.org/10.1016/j.sbspro.2014.12.228
Sunderrajan, A.; Viswanathan, V.; Cai, W.; Knoll, A. 2016. Traffic state estimation using floating car data, Procedia Computer Science 80: 2008–2018. https://doi.org/10.1016/j.procs.2016.05.521
Talebpour, A.; Mahmassani, H. S. 2016. Influence of connected and autonomous vehicles on traffic flow stability and throughput, Transportation Research Part C: Emerging Technologies 71: 143–163. https://doi.org/10.1016/j.trc.2016.07.007
Vandenberghe, W.; Vanhauwaert, E.; Verbrugge, S.; Moerman, I.; Demeester, P. 2012. Feasibility of expanding traffic monitoring systems with floating car data technology, IET Intelligent Transport Systems 6(4): 347–354. https://doi.org/10.1049/iet-its.2011.0221
Wang, X.; Liu, H.; Yu, R.; Deng, B.; Chen, X.; Wu, B. 2014. Exploring operating speeds on urban arterials using floating car data: case study in Shanghai, Journal of Transportation Engineering 140(9): 04014044. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000685
Waterfield, B. 2011. Tom Tom sold driver’s GPS details to be used by police for speed traps, The Telegraph, 28 April 2011. Available from Internet: https://www.telegraph.co.uk/technology/news/8480702/Tom-Tom-sold-drivers-GPS-details-to-be-used-by-police-for-speed-traps.html
Yun, M.; Qin, W. 2019. Minimum sampling size of floating cars for urban link travel time distribution estimation, Transportation Research Record: Journal of the Transportation Research Board 2673(3): 24–43. https://doi.org/10.1177/0361198119834297
Zhang, Z.; Xu, X.; Gao, L. 2015. Minimum sample size determination of floating cars in an urban hybrid network, in CICTP 2015: Efficient, Safe, and Green Multimodal Transportation – Proceedings of the 15th COTA International Conference of Transportation Professionals, 24–27 July 2015, Beijing, China, 443–453. https://doi.org/10.1061/9780784479292.040
Zhao, N.; Yu, L.; Zhao, H.; Guo, J.; Wen, H. 2009. Analysis of traffic flow characteristics on ring road expressways in Beijing: using floating car data and remote traffic microwave sensor data, Transportation Research Record: Journal of the Transportation Research Board 2124: 178–185. https://doi.org/10.3141/2124-17
Altintasi, O.; Tuydes-Yaman, H.; Tuncay, K. 2019a. Monitoring urban traffic from floating car data (FCD): using speed or a los-based state measure, Lecture Notes in Networks and Systems 51: 163–173. https://doi.org/10.1007/978-3-319-98615-9_15
Altintasi, O.; Tuydes-Yaman, H.; Tuncay, K. 2019b. Quality of floating car data (FCD) as a surrogate measure for urban arterial speed, Canadian Journal of Civil Engineering 46(12): 1187–1198. https://doi.org/10.1139/cjce-2018-0422
Ambühl, L.; Menendez, M. 2016. Data fusion algorithm for macroscopic fundamental diagram estimation, Transportation Research Part C: Emerging Technologies 71: 184–197. https://doi.org/10.1016/j.trc.2016.07.013
Beibei, J. Y.; Van Zuylen, H. J.; Shoufeng, L. 2016. Determining the macroscopic fundamental diagram on the basis of mixed and incomplete traffic data, in TRB 95th Annual Meeting Compendium of Papers, 10–14 January 2016, Washington, DC, US, 1–13.
Brockfeld, E.; Lorkowski, S.; Mieth, P.; Wagner, P. 2007. Benefits and limits of recent floating car data technology – an evaluation study, in 11th World Conference on Transport Research, 24–28 June 2007, Berkeley CA, US, 1–14.
Cascetta, E. 2009. Transportation Systems Analysis: Models and Applications. Springer. 742 p. https://doi.org/10.1007/978-0-387-75857-2
Cetin, M.; List, G. F.; Zhou, Y. 2005. Factors affecting minimum number of probes required for reliable estimation of travel time, Transportation Research Record: Journal of the Transportation Research Board 1917: 37–44. https://doi.org/10.1177/0361198105191700105
Chase, R. T.; Williams, B. M.; Rouphail, N. M.; Kim, S. 2012. Comparative evaluation of reported speeds from corresponding fixed-point and probe-based detection systems, Transportation Research Record: Journal of the Transportation Research Board 2308: 110–119. https://doi.org/10.3141/2308-12
Chen, M.; Chien, S. I. J. 2000. Determining the number of probe vehicles for freeway travel time estimation by microscopic simulation, Transportation Research Record: Journal of the Transportation Research Board 1719: 61–68. https://doi.org/10.3141/1719-08
Cheu, R. L.; Xie, C.; Lee, D.-H. 2002. Probe vehicle population and sample size for arterial speed estimation, Computer-Aided Civil and Infrastructure Engineering 17(1): 53–60. https://doi.org/10.1111/1467-8667.00252
Croce, A. I.; Musolino, G.; Rindone, C.; Vitetta, A. 2019. Transport system models and big data: zoning and graph building with traditional surveys, FCD and GIS, ISPRS International Journal of Geo-Information 8(4): 187. https://doi.org/10.3390/ijgi8040187
De Fabritiis, C.; Ragona, R.; Valenti, G. 2008. Traffic estimation and prediction based on real time floating car data, in 2008 11th International IEEE Conference on Intelligent Transportation Systems, 12–15 October 2008, Beijing, China, 197–203. https://doi.org/10.1109/ITSC.2008.4732534
Grengs, J.; Wang, X.; Kostyniuk, L. 2008. Using GPS data to understand driving behavior, Journal of Urban Technology 15(2): 33–53. https://doi.org/10.1080/10630730802401942
He, Z.; Lv, Y.; Lu, L.; Guan, W. 2019a. Constructing spatiotemporal speed contour diagrams: using rectangular or non-rectangular parallelogram cells?, Transportmetrica B: Transport Dynamics 7(1): 44–60. https://doi.org/10.1080/21680566.2017.1320774
He, Z.; Qi, G.; Lu, L.; Chen, Y. 2019b. Network-wide identification of turn-level intersection congestion using only low-frequency probe vehicle data, Transportation Research Part C: Emerging Technologies 108: 320–339. https://doi.org/10.1016/j.trc.2019.10.001
Hong, J.; Zhang, X.; Wei, Z.; Li, L.; Ren, Y. 2007. Spatial and temporal analysis of probe vehicle-based sampling for real-time traffic information system, in 2007 IEEE Intelligent Vehicles Symposium, 13–15 June 2007, Istanbul, Turkey, 1234–1239. https://doi.org/10.1109/IVS.2007.4290287
Houbraken, M.; Logghe, S.; Audenaert, P.; Colle, D.; Pickavet, M. 2018. Examining the potential of floating car data for dynamic traffic management, IET Intelligent Transport Systems 12(5): 335–344. https://doi.org/10.1049/iet-its.2016.0230
Hu, J.; Fontaine, M. D.; Ma, J. 2016. Quality of private sector travel-time data on arterials, Journal of Transportation Engineering 142(4): 04016010. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000815
INRIX. 2018. I-95 Vehicle Probe Project II: Interface Guide. 119 p. Available from Internet: https://tetcoalition.org/wp-content/uploads/2015/02/I-95_VPP_II_Inteface_Guide-March_2018.pdf
Jenelius, E.; Koutsopoulos, H. N. 2015. Probe vehicle data sampled by time or space: consistent travel time allocation and estimation, Transportation Research Part B: Methodological 71: 120–137. https://doi.org/10.1016/j.trb.2014.10.008
Jenelius, E.; Koutsopoulos, H. N. 2013. Travel time estimation for urban road networks using low frequency probe vehicle data, Transportation Research Part B: Methodological 53: 64–81. https://doi.org/10.1016/j.trb.2013.03.008
Kerner, B. S.; Demir, C.; Herrtwich, R. G.; Klenov, S. L.; Rehborn, H.; Aleksic, M.; Haug, A. 2005. Traffic state detection with floating car data in road networks, in Proceedings: 2005 IEEE Intelligent Transportation Systems, 13–16 September 2005, Vienna, Austria, 44–49. https://doi.org/10.1109/ITSC.2005.1520133
Kessler, L.; Huber, G.; Kesting, A.; Bogenberger, K. 2018. Comparing speed data from stationary detectors against floating-car data, IFAC-PapersOnLine 51(9): 299–304. https://doi.org/10.1016/j.ifacol.2018.07.049
Kim, S.; Coifman, B. 2014. Comparing INRIX speed data against concurrent loop detector stations over several months, Transportation Research Part C: Emerging Technologies 49: 59–72. https://doi.org/10.1016/j.trc.2014.10.002
Klunder, G. A.; Taale, H.; Kester, L.; Hoogendoorn, S. 2017. Improvement of network performance by in-vehicle routing using floating car data, Journal of Advanced Transportation 2017: 8483750. https://doi.org/10.1155/2017/8483750
Nigro, M.; Cipriani, E.; Del Giudice, A. 2018. Exploiting floating car data for time-dependent origin–destination matrices estimation, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 22(2): 159–174. https://doi.org/10.1080/15472450.2017.1421462
Ramezani, M.; Geroliminis, N. 2015. Queue profile estimation in congested urban networks with probe data, Computer-Aided Civil and Infrastructure Engineering 30(6): 414–432. https://doi.org/10.1111/mice.12095
Ribeiro, M. D.; Larrañaga, A. M.; Arellana, J.; Cybis, H. B. B. 2014. Influence of GPS and self-reported data in travel demand models, Procedia – Social and Behavioral Sciences 162: 467–476. https://doi.org/10.1016/j.sbspro.2014.12.228
Sunderrajan, A.; Viswanathan, V.; Cai, W.; Knoll, A. 2016. Traffic state estimation using floating car data, Procedia Computer Science 80: 2008–2018. https://doi.org/10.1016/j.procs.2016.05.521
Talebpour, A.; Mahmassani, H. S. 2016. Influence of connected and autonomous vehicles on traffic flow stability and throughput, Transportation Research Part C: Emerging Technologies 71: 143–163. https://doi.org/10.1016/j.trc.2016.07.007
Vandenberghe, W.; Vanhauwaert, E.; Verbrugge, S.; Moerman, I.; Demeester, P. 2012. Feasibility of expanding traffic monitoring systems with floating car data technology, IET Intelligent Transport Systems 6(4): 347–354. https://doi.org/10.1049/iet-its.2011.0221
Wang, X.; Liu, H.; Yu, R.; Deng, B.; Chen, X.; Wu, B. 2014. Exploring operating speeds on urban arterials using floating car data: case study in Shanghai, Journal of Transportation Engineering 140(9): 04014044. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000685
Waterfield, B. 2011. Tom Tom sold driver’s GPS details to be used by police for speed traps, The Telegraph, 28 April 2011. Available from Internet: https://www.telegraph.co.uk/technology/news/8480702/Tom-Tom-sold-drivers-GPS-details-to-be-used-by-police-for-speed-traps.html
Yun, M.; Qin, W. 2019. Minimum sampling size of floating cars for urban link travel time distribution estimation, Transportation Research Record: Journal of the Transportation Research Board 2673(3): 24–43. https://doi.org/10.1177/0361198119834297
Zhang, Z.; Xu, X.; Gao, L. 2015. Minimum sample size determination of floating cars in an urban hybrid network, in CICTP 2015: Efficient, Safe, and Green Multimodal Transportation – Proceedings of the 15th COTA International Conference of Transportation Professionals, 24–27 July 2015, Beijing, China, 443–453. https://doi.org/10.1061/9780784479292.040
Zhao, N.; Yu, L.; Zhao, H.; Guo, J.; Wen, H. 2009. Analysis of traffic flow characteristics on ring road expressways in Beijing: using floating car data and remote traffic microwave sensor data, Transportation Research Record: Journal of the Transportation Research Board 2124: 178–185. https://doi.org/10.3141/2124-17