Research on the Control Method of Urban Expressway Weaving Area
DOI:
https://doi.org/10.54097/ajst.v6i1.9130Keywords:
Urban expressway weaving area, Traffic efficiency, Deep reinforcement learning, Vissim-python simulation.Abstract
With the rapid development of urban expressway construction, there are some problems in the actual operation process, which leads to the traffic efficiency not reaching the expected level. Due to the long-term and large amount of actual data is difficult to obtain, this paper uses the method of combining actual data and simulation to explore the traffic operation law of urban expressway weaving area, analyzes the factors affecting the traffic efficiency of weaving area, and applies the variable speed limit guidance control method based on DQN algorithm, which can effectively improve the traffic efficiency of urban expressway weaving area.
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Han Y, Hegyi A, Yuan Y, et al. Resolving freeway jam waves by discrete first-order model-based predictive control of variable speed limits[J]. Transportation Research Part C: Emerging Technologies, 2017, 77: 405-420.
Kattan L, Khondaker B, Derushkina O, et al. A probe-based variable speed limit system[J]. Journal of Intelligent Transportation Systems, 2015, 19(4): 339-354.
Zhibin, Li, Pan, et al. Optimal Mainline Variable Speed Limit Control to Improve Safety on Large-Scale Freeway Segments[J]. Computer Aided Civil & Infrastructure Engineering, 2016, 31(5):366-380.
Edara P, Sun.Evaluation of variable advisory speed limits in congested work zones[J].Journal of Transportation Safety & Security, 2017, 9(2): 123-145.
Highway Capacity Mannual.2010, Washington, D.C: Transportation Research Board, National Research Council.
Li Z, Liu P, Xu C, et al. Reinforcement learning-based variable speed limit control strategy to reduce traffic congestion at freeway recurrent bottlenecks[J]. IEEE transactions on intelligent transportation systems, 2017, 18(11): 3204-3217.
Wang C, Zhang J, Xu L, et al. A New Solution for Freeway Congestion: Cooperative Speed Limit Control Using Distributed Reinforcement Learning[J]. IEEE Access, 2019, 7: 41947-41957.
Zhu F, Ukkusuri S V. Accounting for dynamic speed limit control in a stochastic traffic environment: A reinforcement learning approach[J]. Transportation research part C: emerging technologies, 2014, 41: 30-47.
Lu C, Huang J, Gong J. Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters[J]. Promet-Traffic&Transportation, 2016, 28(4): 371-381.
J. U. Duncombe, “Infrared navigation—Part I: An assessment of feasibility (Periodical style),” IEEE Trans. Electron Devices, vol. ED-11, pp. 34–39, Jan. 1959.
Wu Y, Tan H, Qin L, et al. Differential variable speed limits control for freeway recurrent bottlenecks via deep actor-critic algorithm[J]. Transportation research part C: emerging technologies, 2020, 117: 102649.
Ke Z, Li Z, Cao Z, et al. Enhancing transferability of deep reinforcement learning-based variable speed limit control using transfer learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, PP(99):1-12.
Muller E R , Carlson R C , Kraus W , et al. Microscopic simulation analysis of Mainstream Traffic Flow Control with Variable Speed Limits[C]// International IEEE Conference on Intelligent Transportation Systems. IEEE, 2013.
G. RG.R. Iordanidou, I. Papamichail, C. Roncoli, and M. Papageorgiou,“Feedback-based integrated motorway traffic flow control with delay balancing,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 9, pp. 2319-2329,Sep. 2017.
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