Improved DTTE Method for Route-Level Travel Time Estimation on Freeways
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 2
Abstract
Travel time estimation plays an important role in advanced traveler information systems (ATIS) for dynamic traffic management. Static travel time estimation (STTE) and dynamic travel time estimation (DTTE) are two of the major methods widely explored for travel time measurement. To analyze their performance on route-level travel time estimation on freeways where congestion may occur, this study developed a framework consisting of four steps: traffic state prediction, travel time estimation, results evaluation, and performance comparison. A METANET-based macroscopic traffic model was developed and employed to predict traffic states based on loop detector data. Then, a novel DTTE method was developed and is proposed herein that combines the piece-wise linear speed-based (PLSB) method and the trajectory assumption algorithm. The indices of the mean absolute relative error (MARE) and the root mean squared error (RMSE) were employed to analyze estimation accuracy by the traditional STTE method and the proposed DTTE method. The comparison results illustrate that during high-demand periods, the proposed DTTE method outperforms the traditional STTE method by producing results that better match reference travel times, which were obtained from video sensors installed along the urban freeway corridor.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data used in this study were provided by a third party. Direct request for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
The authors would like to thank the Centre for Smart Transportation at the University of Alberta and the City of Edmonton for providing data used in this paper. This research work was jointly supported by the Innovation Program of Shanghai Municipal Education Commission (2021-01-07-00-07-E00092), Shanghai Engineering Research Center of Urban Infrastructure Renewal (No. 20DZ2251900), Key Research and Development Program of Shandong Province (2020CXGC010117), and Shandong Transportation Technology Plan (2021B60).
References
Abdulhai, B., and S. M. Tabib. 2003. “Spatio-temporal inductance-pattern recognition for vehicle re-identification.” Transp. Res. Part C: Emerging Technol. 11 (3–4): 223–239. https://doi.org/10.1016/S0968-090X(03)00024-X.
Allström, A., J. Ekström, D. Gundlegård, R. Ringdahl, C. Rydergren, A. M. Bayen, and A. D. Patire. 2016. “Hybrid approach for short-term traffic state and travel time prediction on highways.” Transp. Res. Rec. 2554 (1): 60–68. https://doi.org/10.3141/2554-07.
Bansal, P. 2020. “Biogeography-based optimization and support vector regression for freeway travel time prediction and feature selection.” Preprint, submitted July 30, 2020. https://arxiv.org/abs/2007.15212.
Barcelö, J., L. Montero, L. Marqués, and C. Carmona. 2010. “Travel time forecasting and dynamic origin-destination estimation for freeways based on Bluetooth traffic monitoring.” Transp. Res. Rec. 2175 (1): 19–27. https://doi.org/10.3141/2175-03.
Berkow, M., C. M. Monsere, P. Koonce, R. L. Bertini, and M. Wolfe. 2009. “Prototype for data fusion using stationary and mobile data: Sources for improved arterial performance measurement.” Transp. Res. Rec. 2099 (1): 102–112. https://doi.org/10.3141/2099-12.
Bhaskar, A., Q. Ming, and E. Chung. 2014. “Hybrid model for motorway travel time estimation considering increased detector spacing.” Transp. Res. Rec. 2442 (1): 71–84. https://doi.org/10.3141/2442-09.
Buisson, C. 2006. “Simple traffic model for a simple problem: Sizing travel time measurement devices.” Transp. Res. Rec. 1965 (1): 210–218. https://doi.org/10.1177/0361198106196500122.
Chepuri, A., J. Ramakrishnan, S. Arkatkar, G. Joshi, and S. Pulugurtha. 2018. “Examining travel time reliability-based performance indicators for bus routes using GPS-based bus trajectory data in India.” J. Transp. Eng. Part A: Syst. 144 (5): 04018012. https://doi.org/10.1061/JTEPBS.0000109.
Chiabaut, N., and R. Faitout. 2021. “Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days.” Transp. Res. Part C: Emerging Technol. 124 (Mar): 102920. https://doi.org/10.1016/j.trc.2020.102920.
Chien, S. I.-J., and C. M. Kuchipudi. 2003. “Dynamic travel time prediction with real-time and historic data.” J. Transp. Eng. 129 (6): 608–616. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(608).
Chung, E.-H., and A. Shalaby. 2007. “Expected time of arrival model for school bus transit using real-time global positioning system-based automatic vehicle location data.” J. Intell. Transp. Syst. 11 (4): 157–167. https://doi.org/10.1080/15472450701649398.
Coifman, B. 2002. “Estimating travel times and vehicle trajectories on freeways using dual loop detectors.” Transp. Res. Part A: Policy Pract. 36 (4): 351–364. https://doi.org/10.1016/S0965-8564(01)00007-6.
Edara, P., B. Smith, J. Guo, S. Babiceanu, and C. J. McGhee. 2011. “Methodology to identify optimal placement of point detectors for travel time estimation.” J. Transp. Eng. 137 (3): 155–173. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000205.
Fakharian Qom, S., M. Hadi, Y. Xiao, and H. Al-Deek. 2017. “Queue length estimation for freeway facilities: Based on combination of point traffic detector and automatic vehicle identification data.” Transp. Res. Rec. 2616 (1): 19–26. https://doi.org/10.3141/2616-03.
Fei, X., C.-C. Lu, and K. Liu. 2011. “A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction.” Transp. Res. Part C: Emerging Technol. 19 (6): 1306–1318. https://doi.org/10.1016/j.trc.2010.10.005.
Gonçalves, J., J. S. Goncalves, R. J. Rossetti, and C. Olaverri-Monreal. 2014. “Smartphone sensor platform to study traffic conditions and assess driving performance.” In Proc., 17th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), 2596–2601. New York: IEEE.
Gong, Y., M. Abdel-Aty, and J. Park. 2019. “Evaluation and augmentation of traffic data including Bluetooth detection system on arterials.” J. Intell. Transp. Syst. 2019 (Jun): 1–13. https://doi.org/10.1080/15472450.2019.1632707.
Herrera, J. C., D. B. Work, R. Herring, X. J. Ban, Q. Jacobson, and A. M. Bayen. 2010. “Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment.” Transp. Res. Part C: Emerging Technol. 18 (4): 568–583. https://doi.org/10.1016/j.trc.2009.10.006.
Hoffmann, G., and J. Janko. 1990. “Travel times as a basic part of the LISB guidance strategy.” In Proc., 3rd Int. Conf. on Road Traffic Control, 6–10. London: Institution of Electrical Engineers.
Hofleitner, A., R. Herring, and A. Bayen. 2012. “Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning.” Transp. Res. Part B: Methodol. 46 (9): 1097–1122. https://doi.org/10.1016/j.trb.2012.03.006.
Hu, T.-Y., and W.-M. Ho. 2010. “Travel time prediction for urban networks: The comparisons of simulation-based and time-series models.” In Proc., 17th ITS World Congress Automated Vehicles Symp. Washington, DC: National Academy of Sciences.
Kamga, C. N., K. C. Mouskos, and R. E. Paaswell. 2011. “A methodology to estimate travel time using dynamic traffic assignment (DTA) under incident conditions.” Transp. Res. Part C: Emerging Technol. 19 (6): 1215–1224. https://doi.org/10.1016/j.trc.2011.02.004.
Kaufmann, S., B. S. Kerner, H. Rehborn, M. Koller, and S. L. Klenov. 2018. “Aerial observations of moving synchronized flow patterns in over-saturated city traffic.” Transp. Res. Part C: Emerging Technol. 86 (Jan): 393–406. https://doi.org/10.1016/j.trc.2017.11.024.
Kumar, B. A., R. Jairam, S. S. Arkatkar, and L. Vanajakshi. 2019. “Real time bus travel time prediction using k-NN classifier.” Transp. Lett. 11 (7): 362–372. https://doi.org/10.1080/19427867.2017.1366120.
Kwon, T. M. 2006. “Blind deconvolution processing of loop inductance signals for vehicle reidentification.” In Proc., 85th Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Ladino, A., A. Y. Kibangou, C. C. De Wit, and H. Fourati. 2017. “A real time forecasting tool for dynamic travel time from clustered time series.” Transp. Res. Part C: Emerging Technol. 80 (Jul): 216–238. https://doi.org/10.1016/j.trc.2017.05.002.
Li, R., and G. Rose. 2011. “Incorporating uncertainty into short-term travel time predictions.” Transp. Res. Part C: Emerging Technol. 19 (6): 1006–1018. https://doi.org/10.1016/j.trc.2011.05.014.
Li, R., G. Rose, and M. Sarvi. 2006. “Evaluation of speed-based travel time estimation models.” J. Transp. Eng. 132 (7): 540–547. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:7(540).
Li, Y., and M. McDonald. 2002. “Link travel time estimation using single GPS equipped probe vehicle.” In Proc., IEEE 5th Int. Conf. on Intelligent Transportation Systems, 932–937. New York: IEEE.
Li, Z., R. Kluger, X. Hu, Y.-J. Wu, and X. Zhu. 2018. “Reconstructing vehicle trajectories to support travel time estimation.” Transp. Res. Rec. 2672 (42): 148–158. https://doi.org/10.1177/0361198118772956.
Liu, X.-F., L.-M. Gao, C.-F. Liu, H.-X. Du, and X.-T. Zhang. 2018. “Using long-short term memory model to predict the short-term traffic volume based on loop detector big data.” In CICTP 2018: Intelligence, connectivity, and mobility, 1256–1262. Reston, VA: ASCE.
Lu, L., J. Wang, Z. He, and C.-Y. Chan. 2018a. “Real-time estimation of freeway travel time with recurrent congestion based on sparse detector data.” IET Intell. Transp. Syst. 12 (1): 2–11. https://doi.org/10.1049/iet-its.2016.0356.
Lu, X., P. Xu, and K. Jiang. 2018b. “A lane-based dynamic network loading model for a congested urban network with application in a typical network in China.” In CICTP 2018: Intelligence, connectivity, and mobility, 2477–2486. Reston, VA: ASCE.
Ma, X., Z. Tao, Y. Wang, H. Yu, and Y. Wang. 2015. “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data.” Transp. Res. Part C: Emerging Technol. 54 (May): 187–197. https://doi.org/10.1016/j.trc.2015.03.014.
Martchouk, M., F. Mannering, and D. Bullock. 2011. “Analysis of freeway travel time variability using Bluetooth detection.” J. Transp. Eng. 137 (10): 697–704. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000253.
Meng, M., A. Rau, and H. Mahardhika. 2018. “Public transport travel time perception: Effects of socioeconomic characteristics, trip characteristics and facility usage.” Transp. Res. Part A: Policy Pract. 114 (Aug): 24–37. https://doi.org/10.1016/j.tra.2018.01.015.
Moonam, H. M. 2016. Developing sampling strategies and predicting freeway travel time using Bluetooth data. Milwaukee, WI: Univ. of Wisconsin-Milwaukee.
Mori, U., A. Mendiburu, M. Álvarez, and J. A. Lozano. 2015. “A review of travel time estimation and forecasting for advanced traveller information systems.” Transp. A: Transp. Sci. 11 (2): 119–157. https://doi.org/10.1080/23249935.2014.932469.
Ni, D., and H. J. Wang. 2008. “Trajectory reconstruction for travel time estimation.” J. Intell. Transp. Syst. 12 (3): 113–125. https://doi.org/10.1080/15472450802262307.
Nikovski, D., N. Nishiuma, Y. Goto, and H. Kumazawa. 2005. “Univariate short-term prediction of road travel times.” In Proc., 2005 IEEE Intelligent Transportation Systems, 1074–1079. New York: IEEE.
Oh, S., Y.-J. Byon, K. Jang, and H. Yeo. 2015. “Short-term travel-time prediction on highway: A review of the data-driven approach.” Transp. Rev. 35 (1): 4–32. https://doi.org/10.1080/01441647.2014.992496.
Papageorgiou, M. 1990. “Dynamic modeling, assignment, and route guidance in traffic networks.” Transp. Res. Part B: Methodol. 24 (6): 471–495. https://doi.org/10.1016/0191-2615(90)90041-V.
Pirc, J., G. Turk, and M. Žura. 2016. “Highway travel time estimation using multiple data sources.” IET Intel. Transport Syst. 10 (10): 649–657. https://doi.org/10.1049/iet-its.2015.0211.
Reddy, K. K., B. A. Kumar, and L. Vanajakshi. 2016. “Bus travel time prediction under high variability conditions.” Curr. Sci. 111 (4): 700–711.
Sharmila, R., N. R. Velaga, and A. Kumar. 2019. “SVM-based hybrid approach for corridor-level travel-time estimation.” IET Intel. Transport Syst. 13 (9): 1429–1439. https://doi.org/10.1049/iet-its.2018.5069.
Soriguera, F., and F. Robuste. 2010. “Requiem for freeway travel time estimation methods based on blind speed interpolations between point measurements.” IEEE Trans. Intell. Transp. Syst. 12 (1): 291–297. https://doi.org/10.1109/TITS.2010.2095007.
Soriguera, F., and F. Robusté. 2013. “Freeway travel-time information: Design and real-time performance using spot-speed methods.” IEEE Trans. Intell. Transp. Syst. 14 (2): 731–742. https://doi.org/10.1109/TITS.2012.2234454.
Spiliopoulou, A., M. Kontorinaki, M. Papageorgiou, and P. Kopelias. 2014. “Macroscopic traffic flow model validation at congested freeway off-ramp areas.” Transp. Res. Part C: Emerging Technol. 41 (Apr): 18–29. https://doi.org/10.1016/j.trc.2014.01.009.
Tang, K., S. Chen, Z. Liu, and A. J. Khattak. 2018. “A tensor-based Bayesian probabilistic model for citywide personalized travel time estimation.” Transp. Res. Part C: Emerging Technol. 90 (May): 260–280. https://doi.org/10.1016/j.trc.2018.03.004.
Tatomir, B., L. J. Rothkrantz, and A. C. Suson. 2009. “Travel time prediction for dynamic routing using ant based control.” In Proc., 2009 Winter Simulation Conf. (WSC), 1069–1078. New York: IEEE.
Van Lint, J. 2008. “Online learning solutions for freeway travel time prediction.” IEEE Trans. Intell. Transp. Syst. 9 (1): 38–47. https://doi.org/10.1109/TITS.2008.915649.
Van Lint, J., and N. Van der Zijpp. 2003. “Improving a travel-time estimation algorithm by using dual loop detectors.” Transp. Res. Rec. 1855 (1): 41–48. https://doi.org/10.3141/1855-05.
Van Lint, J., and C. Van Hinsbergen. 2012. “Short-term traffic and travel time prediction models.” Arti. Intell. Appl. Criti. Transp. Issues 22 (1): 22–41.
Van Lint, J. W. C. 2018. “Empirical evaluation of new robust travel time estimation algorithms.” Transp. Res. Rec. 2160 (1): 50–59. https://doi.org/10.3141/2160-06.
Xiao, Y., S. F. Qom, M. Hadi, and H. Al-Deek. 2014. “Use of data from point detectors and automatic vehicle identification to compare instantaneous and experienced travel times.” Transp. Res. Rec. 2470 (1): 95–104. https://doi.org/10.3141/2470-10.
Yeon, J., L. Elefteriadou, and S. Lawphongpanich. 2008. “Travel time estimation on a freeway using discrete time Markov chains.” Transp. Res. Part B: Methodol. 42 (4): 325–338. https://doi.org/10.1016/j.trb.2007.08.005.
Yildirimoglu, M., and N. Geroliminis. 2013. “Experienced travel time prediction for congested freeways.” Transp. Res. Part B: Methodol. 53 (Jan): 45–63. https://doi.org/10.1016/j.trb.2013.03.006.
Yim, Y. 2003. The state of cellular probes. Berkeley, CA: Univ. of California.
Yu, H., Z. Wu, S. Wang, Y. Wang, and X. Ma. 2017. “Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks.” Sensors 17 (7): 1501. https://doi.org/10.3390/s17071501.
Zeng, X., and Y. Zhang. 2013. “Development of recurrent neural network considering temporal-spatial input dynamics for freeway travel time modeling.” Comput.-Aided Civ. Infrastruct. Eng. 28 (5): 359–371. https://doi.org/10.1111/mice.12000.
Zhan, X., R. Li, and S. V. Ukkusuri. 2020. “Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data.” Transp. Res. Part C: Emerging Technol. 117 (Aug): 102660. https://doi.org/10.1016/j.trc.2020.102660.
Zhang, H., H. Wu, W. Sun, and B. Zheng. 2018. “Deeptravel: A neural network based travel time estimation model with auxiliary supervision.” In Proc., Int. Joint Conf. on Artificial Intelligence, 3655–3661. Ithaca, NY: Cornell Univ.
Zhang, K., N. Jia, L. Zheng, and Z. Liu. 2019. “A novel generative adversarial network for estimation of trip travel time distribution with trajectory data.” Transp. Res. Part C: Emerging Technol. 108 (Nov): 223–244. https://doi.org/10.1016/j.trc.2019.09.019.
Zhang, W. 2006. Freeway travel time estimation based on spot speed measurements. Blacksburg, VA: Virginia Tech.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 2 • February 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 24, 2021
Accepted: Oct 21, 2021
Published online: Dec 7, 2021
Published in print: Feb 1, 2022
Discussion open until: May 7, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Mohammad Ali Arman, Chris M.J. Tampére, Accurate Segment Travel Time Estimation Based on Individual Vehicle Data, 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 10.1109/ITSC57777.2023.10421955, (1616-1621), (2023).
- Guangchuan Yang, Daniel Coble, Chris Vaughan, Catherine Peele, Atefeh Morsali, George F. List, Daniel J. Findley, Waiting Time Estimation at Ferry Terminals Based on License Plate Recognition, Journal of Transportation Engineering, Part A: Systems, 10.1061/JTEPBS.0000722, 148, 9, (2022).