A Multisource Data Approach for Estimating Vehicle Queue Length at Metered On-Ramps
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 2
Abstract
Queue length information is a critical input for ramp metering management. Based on accurate and reliable queue length, the inflow rate can be optimized to maximize the benefit of ramp metering. This paper proposes a queue length estimation method for metered on-ramps. In the proposed method, multiple data sources including INRIX data, controller event-based data, and loop detector data are used. The proposed method is based on the resilient back-propagation neural network model. In addition, the proposed method is enhanced by two techniques. The first technique is implementing the decision tree to determine whether or not the queue length is larger than zero and the second technique is checking whether or not the queue length reaches the ramp queue capacity by using the loop occupancy rate data. Three ramps along the SR-51 freeway in Phoenix, Arizona, were selected to evaluate the proposed method. The proposed method is compared with the Kalman filter (KF)-based method that has been proposed in previous research. The results show that the average improvements over the KF-based method are 46.82% and 63.08% for the estimated mean absolute error and root-mean-square error, respectively.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by the Arizona Department of Transportation (the provided data includes INRIX data, controller event-based data, loop detector data, and video data). Direct request for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
We express sincere acknowledgment to the National Natural Science Foundation of China (Serial Nos. 51578465 and 71402149), Doctoral Innovation Fund Program of Southwest Jiaotong University (DCX201826), and Chongqing Municipal Transportation Engineering Key Laboratory Open Project (2018TE04). The authors would like to thank the Arizona Department of Transportation for funding and data support. The authors confirm contribution to the paper as follows: study conception and design: Xiaoling Luo, Xiaobo Ma, Matthew Munden, Yao-Jan Wu, and Yangsheng Jiang; data collection: Xiaobo Ma, Matthew Munden; analysis and interpretation of results: Xiaoling Luo, Xiaobo Ma, Matthew Munden, Yao-Jan Wu, and Yangsheng Jiang; draft manuscript preparation: Xiaoling Luo, Xiaobo Ma, Matthew Munden, Yao-Jan Wu, and Yangsheng Jiang. All authors reviewed the results and approved the final version of the manuscript. We also want to express our acknowledgment to the Chongqing Municipal Education Commission Science and Technology Research Project (KJQN202100714).
References
An, C., Y. J. Wu, J. Xia, and W. Huang. 2018. “Real-time queue length estimation using event-based advance detector data.” J. Intell. Transp. Syst. Technol. Plann. Oper. 22 (4): 277–290. https://doi.org/10.1080/15472450.2017.1299011.
Comert, G., and M. Cetin. 2011. “Analytical evaluation of the error in queue length estimation at traffic signals from probe vehicle data.” IEEE Trans. Intell. Transp. Syst. 12 (2): 563–573. https://doi.org/10.1109/TITS.2011.2113375.
Du, B., S. Chien, J. Lee, L. Spasovic, and K. Mouskos. 2016. “Artificial neural network model for estimating temporal and spatial freeway work zone delay using probe-vehicle data.” Transp. Res. Rec. 2573 (1): 164–171. https://doi.org/10.3141/2573-20.
Hao, P., and X. Ban. 2015. “Long queue estimation for signalized intersections using mobile data.” Transp. Res. Part B Methodol. 82 (2015): 54–73. https://doi.org/10.1016/j.trb.2015.10.002.
Hao, P., X. J. Ban, and J. W. Yu. 2015. “Kinematic equation-based vehicle queue location estimation method for signalized intersections using mobile sensor data.” J. Intell. Transp. Syst. Technol. Plann. Oper. 19 (3): 256–272. https://doi.org/10.1080/15472450.2013.857197.
Kondyli, A., D. K. Hale, M. Asgharzadeh, B. Schroeder, A. Jia, and J. Bared. 2019. “Evaluating the operational effect of narrow lanes and shoulders for the highway capacity manual.” Transp. Res. Rec. 2673 (10): 558–570. https://doi.org/10.1177/0361198119849064.
Kotsialos, A. 2013. “Non-smooth optimization based on resilient backpropagation search for unconstrained and simply bounded problems.” Optim. Methods Software 28 (6): 1282–1301. https://doi.org/10.1080/10556788.2012.696262.
Kotsialos, A. 2014. “Nonlinear optimisation using directional step lengths based on RPROP.” Optim. Lett. 8 (4): 1401–1415. https://doi.org/10.1007/s11590-013-0668-8.
Lee, J., R. Jiang, and E. Chung. 2013. Traffic queue estimation for metered motorway on-ramps through use of loop detector time occupancies. Transp. Res. Rec. 2396 (1): 45–53. https://doi.org/10.3141/2396-06.
Lee, Y., C. H. Wei, and K. C. Chao. 2018. “Evaluating the effects of highway traffic accidents in the development of a vehicle accident queue length estimation model.” Int. J. Intell. Transp. Syst. Res. 16 (1): 26–38. https://doi.org/10.1007/s13177-016-0135-x.
Levinson, D., and L. Zhang. 2006. “Ramp meters on trial: Evidence from the twin cities metering holiday.” Transp. Res. Part A Policy Pract. 40 (10): 810–828. https://doi.org/10.1016/j.tra.2004.12.004.
Li, F., K. Tang, J. Yao, and K. Li. 2017. “Real-time queue length estimation for signalized intersections using vehicle trajectory data.” Transp. Res. Rec. 2623 (1): 49–59. https://doi.org/10.3141/2623-06.
Liu, H. X., X. Wu, and P. G. Michalopoulos. 2007. “Improving queue size estimation for Minnesota’s stratified zone metering strategy.” Transp. Res. Rec. 2012 (1): 38–46. https://doi.org/10.3141/2012-05.
Ma, X., A. Karimpour, and Y. Wu. 2020. “Statistical evaluation of data requirement for ramp metering performance assessment.” Transp. Res. Part A 141 (Dec): 248–261. https://doi.org/10.1016/j.tra.2020.09.011.
Papamichail, I., and M. Papageorgiou. 2011. “Balancing of queues or waiting times on metered dual-branch on-ramps.” IEEE Trans. Intell. Transp. Syst. 12 (2): 438–452. https://doi.org/10.1109/TITS.2010.2093130.
Pesti, G., and R. E. Brydia. 2017. “Work zone impact assessment methods and applications.” Transp. Res. Rec. 2617 (1): 52–59. https://doi.org/10.3141/2617-07.
Poole, A., and A. Kotsialos. 2016. “Second order macroscopic traffic flow model validation using automatic differentiation with resilient backpropagation and particle swarm optimisation algorithms.” Transp. Res. Part C Emerging Technol. 71: 356–381. https://doi.org/10.1016/j.trc.2016.07.008.
Riedmiller, M., and H. Braun. 1993. “Direct adaptive method for faster backpropagation learning: The RPROP algorithm.” In Proc., IEEE Int. Conf. on Neural Networks, 586–591. New York: IEEE. https://doi.org/10.1109/icnn.1993.298623.
Sheu, J. B., Y. H. Chou, and A. Chen. 2004. “Stochastic modeling and real-time prediction of incident effects on surface street traffic congestion.” Appl. Math. Modell. 28 (5): 445–468. https://doi.org/10.1016/j.apm.2003.10.004.
Sun, X., and R. Horowitz. 2005. Localized switching ramp-metering control with queue length estimation and regulation and microscopic simulation results. Amsterdam, Netherlands: Elsevier.
Sun, X., and R. Horowitz. 2006. “Set of new traffic-responsive ramp-metering algorithms and microscopic simulation results.” Transp. Res. Rec. 1959 (1): 9–18. https://doi.org/10.1177/0361198106195900102.
Vigos, G., and M. Papageorgiou. 2010. “A simplified estimation scheme for the number of vehicles in signalized links.” IEEE Trans. Intell. Transp. Syst. 11 (2): 312–321. https://doi.org/10.1109/TITS.2010.2042807.
Vigos, G., M. Papageorgiou, and Y. Wang. 2006. “A ramp queue length estimation algorithm.” In Proc., IEEE Conf. on Intelligent Transportation Systems, 418–425. New York: IEEE. https://doi.org/10.1109/ITSC.2006.1706777.
Weng, J., and Q. Meng. 2013. “Estimating capacity and traffic delay in work zones: An overview.” Transp. Res. Part C Emerging Technol. 35: 34–45. https://doi.org/10.1016/j.trc.2013.06.005.
Wu, J., X. Jin, and A. J. Horowitz. 2008. “Methodologies for estimating vehicle queue length at metered on-ramps.” Transp. Res. Rec. 2047 (1): 75–82. https://doi.org/10.3141/2047-09.
Wu, J., X. Jin, A. J. Horowitz, and D. Gong. 2009. “Experiment to improve estimation of vehicle queue length at metered on-ramps.” Transp. Res. Rec. 2099 (1): 30–38. https://doi.org/10.3141/2099-04.
Yang, G., Z. Tian, D. Wang, and H. Xu. 2018. “Queue length estimation for a metered on-ramp using mesoscopic simulation.” Transp. Lett. 7867 (May): 1–10. https://doi.org/10.1080/19427867.2018.1477491.
Yang, G., Z. Tian, Z. Wang, H. Xu, and R. Yue. 2019. “Impact of on-ramp traffic flow arrival profile on queue length at metered on-ramps.” J. Transp. Eng. Part A Syst. 145 (2): 1–10. https://doi.org/10.1061/JTEPBS.0000211.
Zhang, L., and D. Levinson. 2010. “Ramp metering and freeway bottleneck capacity.” Transp. Res. Part A Policy Pract. 44 (4): 218–235. https://doi.org/10.1016/j.tra.2010.01.004.
Zhao, Y., J. Zheng, W. Wong, X. Wang, Y. Meng, and H. X. Liu. 2019. “Estimation of queue lengths, probe vehicle penetration rates, and traffic volumes at signalized intersections using probe vehicle trajectories.” Transp. Res. Rec. 2673 (11): 660–670. https://doi.org/10.1177/0361198119856340.
Zhu, Z., Y. Lu, J. Zeng, and H. Chen. 2017. “Highway traffic accident influence area based on vehicle bypass decision-making index.” J. Adv. Transp. 2017 (Feb): 5270951. https://doi.org/10.1155/2017/5270951.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 2 • February 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Nov 20, 2020
Accepted: Sep 22, 2021
Published online: Dec 13, 2021
Published in print: Feb 1, 2022
Discussion open until: May 13, 2022
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