Optimal Differential Variable Speed Limit Control in a Connected and Autonomous Vehicle Environment for Freeway Off-Ramp Bottlenecks
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 4
Abstract
When traffic congestion occurs on freeway off-ramp bottlenecks, the traffic state becomes complicated and changeable, which leads to increased vehicle travel time and decreased traffic safety and traffic efficiency. Variable speed limit (VSL) control is an effective method to improve traffic conditions, increase bottleneck throughput, improve traffic efficiency, and reduce emissions. Currently, there is an emerging trend of using connected and autonomous vehicle (CAV) technology to develop VSL control. This paper proposes an optimal differential variable speed limit (DVSL) control strategy under mixed CAVs and human-driven vehicles (HVs) environment for freeway off-ramp bottlenecks. The proposed DVSL control considers the characteristics of on-ramp, off-ramp, and mixed traffic flow (i.e., CAVs coexist with HVs). The proposed optimal DVSL control can describe and forecast the dynamics of traffic flow, and can set different speed limits across each lane with a multiple-objective function of total travel time (TTT) and total travel distance (TTD). A model predictive control (MPC) approach was utilized to optimize the DVSL control algorithm. The designed DVSL control was tested on a real-word freeway section with a simulated off-ramp bottleneck. The simulation results show that the proposed control strategy outperforms other existing methods in terms of improving the mobility of a freeway off-ramp bottleneck and maximizing the environmental benefits. Sensitivity analysis shows that the proposed control strategy can improve performance with the increase of the penetration rate (PR) of CAVs. The proposed methods form the basis of VSL control at off-ramp sections under mixed traffic environment.
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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 work was jointly supported by the National Natural Science Foundation of China (U20A20330, 62273263, 52078356 and 71771176), Natural Science Foundation of Shanghai (19ZR1479000, 20692191200), Shanghai Municipal Science and Technology Major Project (2022-5-YB-09), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100), and the Fundamental Research Funds for the Central Universities.
References
Allaby, P., B. Hellinga, and M. Bullock. 2007. “Variable speed limits: Safety and operational impacts of a candidate control strategy for freeway applications.” IEEE Trans. Intell. Transp. Syst. 8 (4): 671–680. https://doi.org/10.1109/TITS.2007.908562.
Bansal, P., and K. M. Kockelman. 2017. “Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies.” Transp. Res. Part C 95 (Jan): 49–63. https://doi.org/10.1016/j.tra.2016.10.013.
Camacho, E. F., and C. Bordons. 2004. Model predictive control. London: Springer-Verlag.
Carlson, R. C., I. Papamichail, and M. Papageorgiou. 2011. “Local feedback-based mainstream traffic flow control on motorways using variable speed limits.” IEEE Trans. Intell. Transp. Syst. 12 (4): 1261–1276. https://doi.org/10.1109/TITS.2011.2156792.
Carlson, R. C., I. Papamichail, M. Papageorgiou, and A. Messmer. 2010. “Optimal mainstream traffic flow control of large-scale motorway networks.” Transp. Res. Part C 18 (2): 193–212. https://doi.org/10.1016/j.trc.2009.05.014.
Chang, G.-L., S. Y. Park, and J. Paracha. 2011. “Intelligent transportation system field demonstration: Integration of variable speed limit control and travel time estimation for a recurrently congested highway.” Transp. Res. Rec. 2243 (1): 55–66. https://doi.org/10.3141/2243-07.
Chu, T., J. Wang, L. Codecà, and Z. Li. 2020. “Multi-agent deep reinforcement learning for large-scale traffic signal control.” IEEE Trans. Intell. Transp. Syst. 21 (3): 1086–1095. https://doi.org/10.1109/TITS.2019.2901791.
Chung, K., J. Rudjanakanoknad, and M. J. Cassidy. 2007. “Relation between traffic density and capacity drop at three freeway bottlenecks.” Transp. Res. Part B 41 (1): 82–95. https://doi.org/10.1016/j.trb.2006.02.011.
Dervisoglu, G., G. Gomes, J. Kwon, R. Horowitz, and P. Varaiya. 2009. “Automatic calibration of the fundamental diagram and empirical observations on capacity.” In Proc., 88th Transportation Research Board Annual Meeting. Washington, DC: Transportation Research Board.
Du, S. M., and S. Razavi. 2019. “Variable speed limit for freeway work zone with capacity drop using discrete-time sliding mode control.” J. Comput. Civ. Eng. 33 (2): 04019001. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000815.
Ghiasi, A., O. Hussain, Z. S. Qian, and X. Li. 2017. “A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method.” Transp. Res. Part B 106 (Dec): 266–292. https://doi.org/10.1016/j.trb.2017.09.022.
Gong, S., and L. Du. 2018. “Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles.” Transp. Res. Part B 116 (Oct): 25–61. https://doi.org/10.1016/j.trb.2018.07.005.
Grumert, E., X. L. Ma, and A. Tapani. 2015. “Analysis of a cooperative variable speed limit system using microscopic traffic simulation.” Transp. Res. Part C 52 (Mar): 173–186. https://doi.org/10.1016/j.trc.2014.11.004.
Hadiuzzaman, M., and T. Z. Qiu. 2013. “Cell transmission model based variable speed limit control for freeways.” Can. J. Civ. Eng. 40 (1): 46–56. https://doi.org/10.1139/cjce-2012-0101.
Han, Y., D. Chen, and S. Ahn. 2017a. “Variable speed limit control at fixed freeway bottlenecks using connected vehicles.” In Proc., 98th Transportation Research Board Annual Meeting. Washington, DC: Transportation Research Board.
Han, Y., A. Hegyi, Y. Yuan, S. Hoogendoorn, M. Papageorgiou, and C. Roncoli. 2017b. “Resolving freeway jam waves by discrete first-order model-based predictive control of variable speed limits.” Transp. Res. Part C Emerging Technol. 77 (Apr): 405–420. https://doi.org/10.1016/j.trc.2017.02.009.
Hegyi, A., B. De Schutter, and J. Hellendoorn. 2005. “Optimal coordination of variable speed limits to suppress shock waves.” IEEE Trans. Intell. Transp. Syst. 6 (1): 102–112. https://doi.org/10.1109/TITS.2004.842408.
Hoel, C. J., K. Wolff, and L. Laine. 2018. “Automated speed and lane change decision making using deep reinforcement learning.” In Proc., 21st Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), 584–589. New York: IEEE.
Khan, S. M., K. C. Dey, and M. Chowdhury. 2017. “Real-time traffic state estimation with connected vehicles.” IEEE Trans. Intell. Transp. Syst. 18 (7): 1687–1699. https://doi.org/10.1109/TITS.2017.2658664.
Khondaker, B., and L. Kattan. 2015. “Variable speed limit: A microscopic analysis in a connected vehicle environment.” Transp. Res. Part C 58 (Part A): 146–159. https://doi.org/10.1016/j.trc.2015.07.014.
Lee, C., B. Hellinga, and F. Saccomanno. 2006. “Evaluation of variable speed limits to improve traffic safety.” Transp. Res. Part C 14 (3): 213–228. https://doi.org/10.1016/j.trc.2006.06.002.
Letter, C., and L. Elefteriadou. 2015. “An investigation of lane by lane variable speed limit control using simulation.” In Proc., 94th Transportation Research Board Annual Meeting. Washington, DC: Transportation Research Board.
Li, Z. B., P. Liu, C. C. Xu, H. Duan, and W. Wang. 2017. “Reinforcement learning-based variable speed limit control strategy to reduce traffic congestion at freeway recurrent bottlenecks.” IEEE Trans. Intell. Transp. Syst. 18 (11): 3204–3217. https://doi.org/10.1109/TITS.2017.2687620.
Lin, P. W., K. P. Kang, and G. L. Chang. 2004. “Exploring the effectiveness of variable speed limit controls on highway work-zone operations.” J. Intell. Transp. Syst. 8 (3): 155–168. https://doi.org/10.1109/TITS.2017.2687620.
Litman, T. 2017. “Autonomous vehicle implementation predictions.” In Proc., 94th Transportation Research Board Annual Meeting. Washington, DC: Transportation Research Board.
Liu, H., X. D. Kan, S. E. Shladover, X.-Y. Lu, and R. E. Ferlis. 2018. “Modeling impacts of cooperative adaptive cruise control on mixed traffic flow in multi-lane freeway facilities.” Transp. Res. Part C 95 (7): 261–279. https://doi.org/10.1016/j.trc.2018.07.027.
Liu, S., H. Hellendoorn, and B. D. Schutter. 2017. “Model predictive control for freeway networks based on multi-class traffic flow and emission models.” IEEE Trans. Intell. Transp. Syst. 18 (2): 306–320. https://doi.org/10.1109/TITS.2016.2573306.
Lu, X. Y., and S. E. Shladover. 2014. “Review of variable speed limits and advisories: Theory, algorithms, and practice.” Transp. Res. Rec. 2423 (1): 15–23. https://doi.org/10.3141/2423-03.
Lu, X. Y., S. E. Shladover, I. Jawad, R. Jagannathan, and T. Phillips. 2015. “Novel algorithm for variable speed limits and advisories for a freeway corridor with multiple bottlenecks.” Transp. Res. Rec. 2489 (1): 86–96. https://doi.org/10.3141/2489-10.
Lyles, R. W., W. C. Taylor, D. Lavansiri, and J. Grossklaus. 2004. “A field test and evaluation of variable speed limits in work zones.” In Proc., 81st Transportation Research Board Annual Meeting. Washington, DC: Transportation Research Board.
Milanés, V., and S. E. Shladover. 2014. “Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data.” Transp. Res. Part C: Emerging Technol. 48 (Nov): 285–300. https://doi.org/10.1016/j.trc.2014.09.001.
Milanés, V., S. E. Shladover, J. Spring, C. Nowakowski, H. Kawazoe, and M. Nakamura. 2013. “Cooperative adaptive cruise control in real traffic situations.” IEEE Trans. Intell. Transp. Syst. 15 (1): 296–305. https://doi.org/10.1109/TITS.2013.2278494.
Mirchevska, B., C. Pek, M. Werling, M. Althoff, and J. Boedecker. 2018. “High-level decision making for safe and reasonable autonomous lane changing using reinforcement learning.” In Proc., 21st Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), 2156–2162. New York: IEEE.
Mousavi, S. S., M. Schukat, and E. Howley. 2017. “Traffic light control using deep policy-gradient and value-function-based reinforcement learning.” IET Intel. Transp. Syst. 11 (7): 417–423. https://doi.org/10.1049/iet-its.2017.0153.
OpenITS. 2022. “Whitemud drive freeway open data.” Accessed March 1, 2022. https://www.openits.cn/openData1/700.jhtml.
OpenStreetMap. 2022. “OpenStreetMap.” Accessed March 6, 2022. https://www.openstreetmap.org/.
Papageorgiou, M., E. Kosmatopoulos, and I. Papamichail. 2008. “Effects of variable speed limits on motorway traffic flow.” Transp. Res. Rec. 2047 (1): 37–48. https://doi.org/10.3141/2047-05.
Park, B., and S. Yadlapati. 2003. “Development and testing of variable speed limit logics at work zones using simulation.” In Proc., 82nd Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Pasquale, C., D. Anghinolfi, S. Sacone, S. Siri, and M. Papageorgiou. 2016. “A comparative analysis of solution algorithms for nonlinear freeway traffic control problems.” In Proc., IEEE 19th Int. Conf. on IntelligentTransportation Systems, 1773–1778. New York: IEEE.
Piao, J., and M. McDonald. 2008. “Safety impacts of variable speed limits a simulation study.” In Proc., IEEE 11th Int. Conf. on Intelligent Transportation Systems, 833–837. New York: IEEE.
Schick, P. 2003. “Effects of corridor control systems upon capacity of freeways and stability of traffic flow.” Ph.D. dissertation, Institute for Road and Transportation Science, Faculty of Civil and Environmental Engineering, Univ. of Stuttgart.
Seliman, S. M. S., A. W. Sadek, and Q. He. 2020. “Optimal variable, lane group-based speed limits at freeway lane drops: A multiobjective approach.” J. Trans. Eng. 146 (8): 04020074. https://doi.org/10.1061/JTEPBS.0000395.
Treiber, M., A. Hennecke, and D. Helbing. 2000. “Congested traffic states in empirical observations and microscopic simulations.” Phys. Rev. E 62 (2): 1805. https://doi.org/10.1103/PhysRevE.62.1805.
USDOT. 2014. “DOT Strategic Plan for FY2014-2018.” Accessed November 26, 2014. https://www.transportation.gov/administrations/office-policy/fy-2014-2018-strategic-plan.
Van den Hoogen, E., and S. Smulders. 1994. “Control by variable speed signs: Results of the Dutch experiment.” In Proc., IEEE 17th Int. Conf. on Road Traffic Monitoring and Control, 145–149. London: IEEE.
Van der Pol, E., and F. A. Oliehoek. 2016. “Coordinated deep reinforcement learners for traffic light control.” In Proc., of Learning, Inference and Control of Multi-Agent Systems. San Diego: Neural Information Processing Systems.
van de Weg, G., A. Hegyi, H. Hellendoorn, and S. E. Shladover. 2014. “Cooperative systems based control for integrating ramp metering and variable speed limits.” In Proc., 93rd Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Wang, M., W. Daamen, S. P. Hoogendoorn, and B. van Arem. 2016. “Connected variable speed limits control and car-following control with vehicle-infrastructure communication to resolve stop-and-go waves.” J. Intell. Transp. Syst. 20 (6): 559–572. https://doi.org/10.1080/15472450.2016.1157022.
Wang, P., C. Y. Chan, and L. A. D Fortelle. 2018. “A reinforcement learning based approach for automated lane change maneuvers.” In Proc., IEEE 29th Int. Conf. on Intelligent Vehicles Symp., 1379–1384. New York: IEEE.
Wang, R., Y. Li, and D. B. Work. 2017. “Comparing traffic state estimators for mixed human and automated traffic flows.” Transp. Res. Part C 78 (May): 95–110. https://doi.org/10.1016/j.trc.2017.02.011.
Wei, H., G. Zheng, H. Yao, and Z. Li. 2018. “IntelliLight: A reinforcement learning approach for intelligent traffic light control.” In Proc., ACM SIGKDD 24th Int. Conf. on Knowledge Discovery & Data Mining, 2496–2505. London: Association for Computing Machinery.
Wu, Y., H. Tan, L. Qin, and B. Ran. 2020. “Differential variable speed limits control for freeway recurrent bottlenecks via deep actor-critic algorithm.” Transp. Res. Part C 117 (Aug): 102649. https://doi.org/10.1016/j.trc.2020.102649.
Yang, X., Y. Lu, and Y. Lin. 2017. “Optimal variable speed limit control system for freeway work zone operations.” J. Comput. Civ. Eng. 31 (1): 04016044. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000610.
Yao, Z., Y. Wang, B. Liu, B. Zhao, and Y. Jiang. 2021. “Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway.” Energy 230 (Sep): 120766. https://doi.org/10.1016/j.energy.2021.120766.
Yu, M., and W. Fan. 2018. “Tabu search strategies for variable speed limit control at a lane drop bottleneck.” J. Transp. Eng. 144 (7): 04018033. https://doi.org/10.1061/JTEPBS.0000147.
Yu, M., and W. D. Fan. 2019. “Optimal variable speed limit control in connected autonomous vehicle environment for relieving freeway congestion.” J. Trans. Eng. 145 (4): 04019007. https://doi.org/10.1061/JTEPBS.0000227.
Zhang, K. P., N. Jia, L. Zheng, and Z. J. Liu. 2019. “A novel generative adversarial network for estimation of trip travel time distribution with trajectory data.” Transp. Res. Part C 108 (Nov): 223–244. https://doi.org/10.1016/j.trc.2019.09.019.
Zhang, K. P., Z. J. Liu, and L. Zheng. 2020. “Short-term prediction of passenger demand in multi-zone level: Temporal convolutional neural network with multi-task learning.” IEEE Trans. Intell. Transp. Syst. 21 (4): 1480–1490. https://doi.org/10.1109/TITS.2019.2909571.
Zhang, Y. H., and P. A. Ioannou. 2017. “Combined variable speed limit and lane change control for highway traffic.” IEEE Trans. Intell. Transp. Syst. 18 (7): 1812–1823. https://doi.org/10.1109/TITS.2016.2616493.
Zhu, F., and S. V. Ukkusuri. 2014. “Accounting for dynamic speed limit control in a stochastic traffic environment: A reinforcement learning approach.” Transp. Res. Part C 41 (Apr): 30–47. https://doi.org/10.1016/j.trc.2014.01.014.
Zhu, M., X. Wang, A. Tarko, and S. Fang. 2018a. “Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study.” Transp. Res. Part C 93 (Dec): 425–445. https://doi.org/10.1016/j.trc.2018.06.009.
Zhu, M., X. Wang, and Y. Wang. 2018b. “Human-like autonomous car-following model with deep reinforcement learning.” Transp. Res. Part C 97 (Dec): 348–368. https://doi.org/10.1016/j.trc.2018.10.024.
Zhu, M., Y. Wang, Z. Pu, J. Hu, X. Wang, and R. Ke. 2020. “Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving.” Transp. Res. Part C 117 (Aug): 102662. https://doi.org/10.1016/j.trc.2020.102662.
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Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 4 • April 2023
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© 2023 American Society of Civil Engineers.
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Received: Apr 1, 2022
Accepted: Nov 16, 2022
Published online: Jan 23, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 23, 2023
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