Abstract
Real-time and accurate queue length information is crucial to developing effective queue management applications in modern traffic control systems to alleviate traffic congestion. A Random Forests (RF) based real-time queue length estimation method is proposed using the vehicle Global Position System (GPS) trajectory and License Plate Recognition (LPR) Data. The RF model is trained to predict the vehicle stop locations provided by the GPS data by features of traffic flow characteristics extracted from the LRP data. The predicted stop locations are further used to estimate the cyclic maximum queue length for each approach lane. The proposed method has been implemented on sixteen lanes of eight links from both major and minor arterials in Kunshan City, China. Key findings and conclusions include: 1) By feature selection, the travel time has the most significant impact on the prediction accuracy of the vehicle stop location, and the number of departed vehicles is the secondary informative feature. 2) The RF model achieves a satisfying accuracy for the stop location prediction and cyclic maximum queue length estimation, which has the best performance with a larger sample size in the training data. 3) Comparative analysis also shows the superiorities of the proposed model to have more accurate results by incorporating comprehensive features and a machine learning process.
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References
An C, Guo X, Hong R, Lu Z, Xia J (2021) Lane-based traffic arrival pattern estimation using license plate recognition data. IEEE Intelligent Transportation Systems Magazine, DOI: https://doi.org/10.1109/MITS.2021.3051489
An C, Wu Y, Xia J, Huang W (2018) Real-time queue length estimation using event-based advance detector data. Journal of Intelligent Transportation Systems 22(4):277–290, DOI: https://doi.org/10.1080/15472450.2017.1299011
Anusha SP, Sharma A, Vanajakshi L, Subramanian SC, Rilett LR (2016) Model-based approach for queue and delay estimation at signalized intersections with erroneous automated data. Journal of Transportation Engineering 142(5):1–10, DOI: https://doi.org/10.1061/(ASCE)TE.1943-5436.0000835
Ban J, Hao P, Sun Z (2011) Real time queue length estimation for signalized intersections using travel times from mobile sensors. Transportation Research Part C: Emerging Technologies 19(6): 1133–1156, DOI: https://doi.org/10.1016/j.trc.2011.01.002
Breiman L (2011) Random forests. Machine Learning 45(1):5–32, DOI: https://doi.org/10.1023/A:1010933404324
Byon YJ, Baher A, Amer S (2009) Real-time transportation mode detection via tracking global positioning system mobile devices. Journal of Intelligent Transportation Systems 13(4):161–170, DOI: https://doi.org/10.1080/15472450903287781
Byon YJ, Ha JS, Cho CS, Kim TY, Yeun CY (2017) Real-time transportation mode identification using artificial neural networks enhanced with mode availability layers: A case study in Dubai. Applied Sciences 7(9), DOI: https://doi.org/10.3390/app7090923
Byon YJ, Liang S (2014) Real-time transportation mode detection using smartphones and artificial neural networks: Performance comparisons between smartphones and conventional global positioning system sensors. Journal of Intelligent Transportation Systems 18(3):264–272, DOI: https://doi.org/10.1080/15472450.2013.824762
Cai Q, Wang Z, Zheng L, Wu B, Wang Y (2014) Shock wave approach for estimating queue length at signalized intersections by fusing data from point and mobile sensors. Transportation Research Record: Journal of the Transportation Research Board 2422:79–87, DOI: https://doi.org/10.3141/2422-09
Chang J, Talas M, Muthuswamy S (2013) Simple methodology for estimating queue lengths at signalized intersections using detector data. Transportation Research Record: Journal of the Transportation Research Board 2355:31–38, DOI: https://doi.org/10.3141/2355-04
Cheng Y, Qin X, Jin J, Ran B (2012) An exploratory shockwave approach to estimating queue length using probe trajectories. Journal of Intelligent Transportation Systems 16(1):12–23, DOI: https://doi.org/10.1080/15472450.2012.639637
Cheng Y, Qin X, Jin J, Ran B, Anderson J (2011) Cycle-by-cycle queue length estimation for signalized intersections using sampled trajectory data. Transportation Research Record: Journal of the Transportation Research Board 2257:87–94, DOI: https://doi.org/10.3141/2257-10
Comert G (2013) Simple analytical models for estimating the queue lengths from probe vehicles at traffic signals. Transportation Research Part B: Methodological 55:59–74, DOI: https://doi.org/10.1016/j.trb.2013.05.001
Comert G (2016) Queue length estimation from probe vehicles at isolated intersections: Estimators for primary parameters. European Journal of Operational Research 252(2):502–521, DOI: https://doi.org/10.1016/j.ejor.2016.01.040
Comert G, Cetin M (2009) Queue length prediction from probe vehicle location and the impacts of sample size. European Journal of Operational Research 197(1):196–202, DOI: https://doi.org/10.1016/j.ejor.2008.06.024
Comert G, Cetin M (2011) Analytical evaluation of the error in queue length estimation at traffic signals from probe vehicle data. IEEE Transactions on Intelligent Transportation Systems 12(2):563–573, DOI: https://doi.org/10.1109/TITS.2011.2113375
Hao P, Ban J (2015) Long queue estimation for signalized intersections using mobile data. Transportation Research Part B: Methodological 82:54–73, DOI: https://doi.org/10.1016/j.trb.2015.10.002
Hao P, Ban J, Yu JW (2013) Kinematic equation based vehicle queue location estimation method for signalized intersections using mobile sensor data. Journal of Intelligent Transportation Systems 19(3): 256–272, DOI: https://doi.org/10.1080/15472450.2013.857197
Hu H, Liu HX (2013) Arterial offset optimization using archived high-resolution traffic signal data. Transportation Research Part C: Emerging Technologies 37:131–144, DOI: https://doi.org/10.1016/j.trc.2013.10.001
Hu H, Wu X, Liu HX (2011) A simple forward-backward procedure for real-time signal timing adjustment on oversaturated arterial networks. 2011 14th international IEEE conference on intelligent transportation systems (ITSC), October 5–7, Washington DC, USA, 1126–1131, DOI: https://doi.org/10.1109/ITSC.2011.6083068
Lee S, Wong SC, Li YC (2015) Real-time estimation of lane-based queue lengths at isolated signalized junctions. Transportation Research Part C: Emerging Technologies 56:1–17, DOI: https://doi.org/10.1016/j.trc.2015.03.019
Li J, Zhou K, Shladover SE, Skabardonis A (2013) Estimating queue length under connected vehicle technology using probe vehicle, loop detector, and fused data. Transportation Research Record: Journal of the Transportation Research Board 2366:17–22, DOI: https://doi.org/10.3141/2356-03
Liu Z, Liu Y, Lyu C, Ye J (2020) Building personalized transportation model for online taxi-hailing demand prediction. IEEE Transactions on Cybernetics 51(9):4602–4610, DOI: https://doi.org/10.1109/TCYB.2020.3000929
Liu HX, Wu X, Ma W, Hu H (2009) Real-time queue length estimation for congested signalized intersections. Transportation Research Part C: Emerging Technologies 17(4):412–427, DOI: https://doi.org/10.1016/j.trc.2009.02.003
Luo X, Ma D, Jin S, Gong Y, Wang D (2019) Queue length estimation for signalized intersections using license plate recognition data. IEEE Intelligent Transportation Systems Magazine 11(3):209–220, DOI: https://doi.org/10.1109/MITS.2019.2919541
Mei Y, Gu W, Chung EC, Li F, Tan K (2019) A Bayesian approach for estimating vehicle queue lengths at signalized intersections using probe vehicle data. Transportation Research Part C: Emerging Technologies 109:233–249, DOI: https://doi.org/10.1016/j.trc.2019.10.006
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, DOI: https://doi.org/10.1111/mice.12095
Sharma A, Bullock D, Bonneson J (2007) Input-output and hybrid techniques for real-time prediction of delay and maximum queue length at signalized intersections. Transportation Research Record: Journal of the Transportation Research Board 2035:69–80, DOI: https://doi.org/10.3141/2035-08
Skabardonis A, Geroliminis N (2008) Real-time monitoring and control on signalized arterials. Journal of Intelligent Transportation Systems 12(2):64–74, DOI: https://doi.org/10.1080/15472450802023337
Tan C, Liu L, Wu H, Cao Y, Tang K (2020) Fuzing license plate recognition data and vehicle trajectory data for lane-based queue length estimation at signalized intersections. Journal of Intelligent Transportation Systems 24(5):449–466, DOI: https://doi.org/10.1080/15472450.2020.1732217
Tiaprasert K, Zhang Y, Wang XB, Zeng X (2015) Queue length estimation using connected vehicle technology for adaptive signal control. IEEE Transactions on Intelligent Transportation Systems 16(4):2129–2140, DOI: https://doi.org/10.1109/TITS.2015.2401007
Wang Z, Cai Q, Wu B, Zheng L, Wang Y (2017) Shockwave-based queue estimation approach for undersaturated and oversaturated signalized intersections using multi-source detection data. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 21(3):167–178, DOI: https://doi.org/10.1080/15472450.2016.1254046
Wu X, Liu HX (2011) A shockwave profile model for traffic flow on congested urban arterials. Transportation Research Part B: Methodological 45(10):1768–1786, DOI: https://doi.org/10.1016/j.trb.2011.07.013
Wu X, Liu HX, Gettman D (2010) Identification of oversaturated intersections using high-resolution traffic signal data. Transportation Research Part C: Emerging Technologies 18(4):626–638, DOI: https://doi.org/10.1016/j.trc.2010.01.003
Yang K, Menendez M (2019) Queue estimation in a connected vehicle environment: A convex approach. IEEE Transactions on Intelligent Transportation Systems 20(7):2480–2496, DOI: https://doi.org/10.1109/TITS.2018.2866936
Yu H, Liu P, Fan Y, Zhang G (2021) Developing a decentralized signal control strategy considering link storage capacity. Transportation Research Part C: Emerging Technologies 124:102971, DOI: https://doi.org/10.1016/j.trc.2021.102971
Zhan X, Li R, Ukkusuri SV (2015) Lane-based real-time queue length estimation using license plate recognition data. Transportation Research Part C: Emerging Technologies 57:85–102, DOI: https://doi.org/10.1016/j.trc.2015.06.001
Zhao Y, Zheng J, Wong W, Wang X, Meng Y, Liu HX (2019) Various methods for queue length and traffic volume estimation using probe vehicle trajectories. Transportation Research Part C: Emerging Technologies, 2019 107:70–91, DOI: https://doi.org/10.1016/j.trc.2019.07.008
Zheng J, Liu HX (2017) Estimating traffic volumes for signalized intersections using connected vehicle data. Transportation Research Part C 79:347–362, DOI: https://doi.org/10.1016/j.trc.2017.03.007
Acknowledgments
This work was supported by the National Key R&D Program of China under Grant 2018YFB1601000. The authors would like to thank the anonymous reviewers for their thorough review and constructive comments, which had led to a substantial improvement of this paper.
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Liu, D., An, C., Yasir, M. et al. A Machine Learning Based Method for Real-Time Queue Length Estimation Using License Plate Recognition and GPS Trajectory Data. KSCE J Civ Eng 26, 2408–2419 (2022). https://doi.org/10.1007/s12205-022-0451-4
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DOI: https://doi.org/10.1007/s12205-022-0451-4