Modeling of Vehicle Left-Turn Trajectories and Exit Lane Selection at Signalized Intersections
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 3
Abstract
Within the urban road system, traffic conflicts most frequently are concentrated at road intersections, and left-turning traffic is a prominent and pivotal factor leading to intersection conflicts. The scarcity of applied models that can comprehensively elucidate the shifting trajectory dynamics and exit lane selection behavior of left-turning vehicles has posed challenges to the accurate analysis and prediction of intersection vehicle conflict patterns. This study extracted trajectories of left-turning vehicles at intersections from video data. A vehicle left-turn trajectory model was formulated, hinging on changes in trajectory curvature. Moreover, leveraging geometric intersection parameters and video data, a vehicle exit lane selection model was devised using random utility theory. This model integrates variables such as intersection turning angle, vehicle type, entrance lane position, vehicle speed, following clearance, and the lead vehicle’s position in the exit lane. The outcomes indicate that left-turn trajectories can be approximated well with a flat curve, and the trajectory’s curvature has a quadratic parabolic correlation with the intersection’s aspect ratio. The trajectory model presented in this paper adeptly characterizes left-turning vehicle movement across diverse intersections. Similarly, the exit lane selection model adeptly forecasts the distribution of left-turning vehicles in exit lane positions. This predictive efficacy stems from the recognition that turning angle, entrance lane vehicle position, and vehicle speed profoundly impact the exit lane choice for left-turning vehicles. This research sheds light on the intricacies of driving behavior and exit lane selection of left-turning vehicles at signalized intersections. By focusing on these critical aspects, this study offers valuable insights that contribute to the improvement of intersection design and overall traffic safety.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was funded by the Science and Technology Project of the Jiangxi Department of Transportation in China (Grant No. 2020H0049).
Author contributions: Conceptualization, Wuguang Lin and Hao Han; methodology, Wuguang Lin and Qifeng Yu; software, Dilinazi Tayijiang and Yu Dong; validation, Guowei Yin and Yu Dong; formal analysis, Guowei Yin, Yu Dong, and Qifeng Yu; investigation, Dilinazi Tayijiang and Yu Dong; data curation, Guowei Yin; writing—original draft preparation, Guowei Yin and Yu Dong; writing—review and editing, Wuguang Lin and Qifeng Yu; and funding acquisition, Qifeng Yu. All authors have read and agreed to the published version of the manuscript.
References
Abdeljaber, O., A. Younis, and W. Alhajyaseen. 2020. “Extraction of vehicle turning trajectories at signalized intersections using convolutional neural networks.” Arab. J. Sci. Eng. 45 (10): 8011–8025. https://doi.org/10.1007/s13369-020-04546-y.
Alhajyaseen, W. K. M., M. Asano, H. Nakamura, and D. M. Tan. 2013. “Stochastic approach for modeling the effects of intersection geometry on turning vehicle paths.” Transp. Res. Part C Emerging Technol. 32 (Jul): 179–192. https://doi.org/10.1016/j.trc.2012.09.006.
Alhajyaseen, W. K. M., M. Asano, K. Suzuki, and H. Nakamura. 2011. “Analysis on the variation of left-turning vehicle spatial trajectories inside intersections.” In Proc., Eastern Asia Society for Transportation Studies Vol. 8 (The 9th Int. Conf. of Eastern Asia Society for Transportation Studies, 2011), 309. Kawana, Japan: Eastern Asia Society for Transportation Studies. https://doi.org/10.11175/eastpro.2011.0.309.0.
Broström, M. 2020. “Real-time multi-object tracker using YOLOv5 and deep sort.” Accessed March 10, 2023. https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch.
Chen, P., W. Zeng, G. Yu, and Y. Wang. 2017. “Surrogate safety analysis of pedestrian-vehicle conflict at intersections using unmanned aerial vehicle videos.” J. Adv. Transp. 2017 (May): 5202150. https://doi.org/10.1155/2017/5202150.
Choudhury, C. F., and M. E. Ben-Akiva. 2008. “Lane selection model for urban intersections.” Transp. Res. Rec. 2088 (1): 167–176. https://doi.org/10.3141/2088-18.
Dias, C., M. Iryo-Asano, M. Abdullah, T. Oguchi, and W. Alhajyaseen. 2020. “Modeling trajectories and trajectory variation of turning vehicles at signalized intersections.” IEEE Access 8 (Jun): 109821–109834. https://doi.org/10.1109/ACCESS.2020.3002020.
Dias, C., M. Iryo-Asano, and T. Oguchi. 2017. “Predicting optimal trajectory of left-turning vehicle at signalized intersection.” Transp. Res. Procedia 21 (Jan): 240–250. https://doi.org/10.1016/j.trpro.2017.03.093.
Gipps, P. G. 1986. “A model for the structure of lane-changing decisions.” Transp. Res. Part B Methodol. 20 (5): 403–414. https://doi.org/10.1016/0191-2615(86)90012-3.
Khan, M. A., W. Ectors, T. Bellemans, D. Janssens, and G. Wets. 2018. “Unmanned aerial vehicle-based traffic analysis: A case study for shockwave identification and flow parameters estimation at signalized intersections.” Remote Sens. 10 (3): 458. https://doi.org/10.3390/rs10030458.
Ren, X., D. Wang, M. Laskey, and K. Goldberg. 2018. “Learning traffic behaviors by extracting vehicle trajectories from online video streams.” In Proc., 2018 IEEE 14th Int. Conf. on Automation Science and Engineering (CASE), 1276–1283. New York: IEEE.
Sando, T., and R. Moses. 2009. “Influence of intersection geometrics on the operation of triple left-turn lanes.” J. Transp. Eng. 135 (5): 253–259. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000005.
Sun, D., and L. Elefteriadou. 2010. “Research and implementation of lane-changing model based on driver behavior.” Transp. Res. Rec. 2161 (1): 1–10. https://doi.org/10.3141/2161-01.
Wang, X., Y. Zhang, and J. Jiao. 2019. “A state dependent mandatory lane-changing model for urban arterials with hidden Markov model method.” Int. J. Transp. Sci. Technol. 8 (2): 219–230. https://doi.org/10.1016/j.ijtst.2018.11.005.
Wei, F., W. Guo, X. Liu, C. Liang, and T. Feng. 2014. “Left-turning vehicle trajectory modeling and guide line setting at the intersection.” Discrete Dyn. Nat. Soc. 2014 (Sep): 950219. https://doi.org/10.1155/2014/950219.
Wei, F., Z. Wang, and J. Lu. 2017. “Exploring factors contributing to lane changes during left turns on quadruple left-turn lanes at signalized intersections.” Adv. Mech. Eng. 9 (5): 1687814017700062. https://doi.org/10.1177/1687814017700062.
Wolfermann, A., W. K. Alhajyaseen, and H. Nakamura. 2011. “Modeling speed profiles of turning vehicles at signalized intersections.” In Proc., 3rd Int. Conf. on Road Safety and Simulation RSS2011, Transportation Research Board TRB, Indianapolis, 1–17. Washington, DC: Transportation Research Board.
Wu, Y., M. Abdel-Aty, O. Zheng, Q. Cai, and S. Zhang. 2020. “Automated safety diagnosis based on unmanned aerial vehicle video and deep learning algorithm.” Transp. Res. Rec. 2674 (8): 350–359. https://doi.org/10.1177/0361198120925808.
Xie, D.-F., Z.-Z. Fang, B. Jia, and Z. He. 2019. “A data-driven lane-changing model based on deep learning.” Transp. Res. Part C Emerging Technol. 106 (Sep): 41–60. https://doi.org/10.1016/j.trc.2019.07.002.
Xu, Y., Z. Ma, and J. Sun. 2019. “Simulation of turning vehicles’ behaviors at mixed-flow intersections based on potential field theory.” Transportmetrica B: Transp. Dyn. 7 (1): 498–518. https://doi.org/10.1080/21680566.2018.1447407.
Yao, R., W. Zeng, Y. Chen, and Z. He. 2021. “A deep learning framework for modelling left-turning vehicle behaviour considering diagonal-crossing motorcycle conflicts at mixed-flow intersections.” Transp. Res. Part C Emerging Technol. 132 (Nov): 103415. https://doi.org/10.1016/j.trc.2021.103415.
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© 2023 American Society of Civil Engineers.
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Received: Apr 21, 2023
Accepted: Oct 11, 2023
Published online: Dec 21, 2023
Published in print: Mar 1, 2024
Discussion open until: May 21, 2024
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