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【背景】随着网联自动驾驶车辆的普及，人类驾驶车辆与自动驾驶车辆共用道路资源已成为常态，如何在保障安全性的前提下，提升由此产生的混合交通流效率是当前的研究热点。【目标】聚焦于城市快速路与支线道路并存的混合交通环境，探讨如何通过合理设计与实施分流控制策略，对交通流进行更优的分配与调度，从而有效提升整体交通流的效率，缓解路段间可能出现的拥堵情况，并为城市道路网络提供更稳定、高效的运行保障。【方法】基于城市主干道与支路通行能力差异，设计了面向未来高级自动驾驶的人机混合交通分流控制策略，尽量将自动驾驶车辆分流到支线道路，利用自主协同驾驶达成支线道路交叉口的快速通行。【结果】实验初步验证了支路分流策略在路网交通流拥堵情况下的可行性和价值，在多种流量与渗透率条件下均能显著改善路网平均出行时间，为混合交通流管控提供了新的管控思路。【应用】该策略在更大规模的城市路网中具备广阔的推广前景，为城市交通系统的数字化与智能化建设提供更多可行方案，从而为智能交通系统的进一步发展奠定坚实的基础。
Abstract：[Background] Owing to the increasing prevalence of connected and autonomous vehicles,the sharing of road infrastructure by human-driven and automated vehicles has become commonplace.Thus, ensuring safety while improving the efficiency of emerging mixed-traffic flows has become a key research focus. [Objective] This study focuses on a mixed-traffic environment in which urban expressways and secondary roads coexist to determine the best method to employ diversion control strategies such that traffic flows can be distributed and managed optimally. The ultimate goal is to effectively enhance the overall efficiency of transportation networks and mitigate potential congestion across various road segments, thereby ensuring more stable and efficient urban road operations.[Methods] A separation control strategy for advanced autonomous driving was developed based on the capacity difference between urban freeways and branch roads, with emphasis on the redirection of autonomous vehicles on branch roads while leveraging cooperative driving to enhance intersection throughput. [Results] Experimental results provide preliminary evidence demonstrating the feasibility and value of the side-road diversion strategy under congested network conditions. Specifically, it can significantly improve the average travel times across various traffic volumes and penetration rates.Thus, this study offers a new perspective for managing mixed-traffic flows. [Application] The proposed strategy shows significant potential for deployment in large-scale urban road networks and offers additional feasible solutions for the digital and intelligent transformation of urban transportation systems. Thus, it provides a solid foundation for the further advancement of intelligent transportation.
[1]贺正冰.微观交通模型：智能网联化转型与通用驾驶人模型框架[J].交通运输工程与信息学报，2022, 20(2):1-13.HE Zhengbing. Microscopic traffic models:transformation in connected environment and generalized driver model[J]. Journal of Transportation Engineering and Information, 2022, 20(2):1-13.
[2]GE J W, XU H L, ZHANG J W, et al. Heterogeneous driver modeling and corner scenarios sampling for automated vehicles testing[J]. Journal of Advanced Transportation,2022, 1:8655514.
[3]LI J, YU C, SHEN Z, et al. A survey on urban traffic control under mixed traffic environment with connected automated vehicles[J]. Transportation Research Part C:Emerging Technologies, 2023, 154:104258.
[4]AN L, YANG X F, HU J. Modeling system dynamics of mixed traffic with partial connected and automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9):15755-15764.
[5]XU B, BAN X J, BIAN Y, et al. Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections[J]. IEEE Transactions on IntelligentTransportation Systems, 2019, 20(4):1390-1403.
[6]ZHANG J W, CHANG C, LI S, et al. Unleashing the twodimensional benefits of connected and automated vehicles via dedicated intersections in mixed traffic[J]. Transportation Research Part C:Emerging Technologies, 2024,160:104501.
[7]吴红兰，胡德富，郭旭周.基于Informer的车辆多意图运动轨迹预测[J].交通运输工程与信息学报，2024, 22(3):68-79.WU Honglan, HU Defu, GUO Xuzhou. Vehicle multi-intention motion trajectory prediction based on Informer[J].Journal of Transportation Engineering and Information,2024, 22(3):68-79.
[8]ZHANG J E, CHANG C, ZENG X L, et al. Multi-agent DRL-based lane change with right-of-way collaboration awareness[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(1):854-869.
[9]张坤鹏，常成，王世璞，等.自动驾驶汽车仿真器综述:能力、挑战和发展方向[J].交通运输工程与信息学报,2024, 22(1):1-24.ZHANG Kunpeng, CHANG Cheng, WANG Shipu, et al.Review of autonomous vehicle simulators:capabilities,challenges, and development directions[J]. Journal of Transportation Engineering and Information, 2024, 22(1):1-24.
[10]YU H Y, JIANG R, HE Z B, et al. Automated vehicle-involved traffic flow studies:a survey of assumptions,models, speculations, and perspectives[J]. Transportation Research Part C:Emerging Technologies, 2021,127:103101.
[11]WANG W S, WANG L T, ZHANG C Y, et al. Social interactions for autonomous driving:a review and perspectives[J]. Foundations and Trends®in Robotics, 2022, 10(3/4):198-376.
[12]ZHU S Y, LI D S, LIU M L. Hindrance-aware platoon formation for connected vehicles in mixed traffic[J].IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12):24876-24890.
[13]LI K Q, WANG J W, ZHENG Y. Cooperative formation of autonomous vehicles in mixed traffic flow:beyond platooning[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9):15951-15966.
[14]GAO Z B, WU Z Z, HAO W, et al. Deployment optimization of connected and automated vehicle lanes with the safety benefits on roadway networks[J]. Journal of Advanced Transportation, 2020,(1):9401062.
[15]YAO Z H, WU Y X, JIANG Y S, et al. Modeling the fundamental diagram of mixed traffic flow with dedicated lanes for connected automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(6):6517-6529.
[16]蒋明智，吴天昊，张琳.基于深度强化学习的无信号交叉口车辆协同控制算法[J].交通运输工程与信息学报，2022, 20(2):14-24.JIANG Mingzhi, WU Tianhao, ZHANG Lin. Deep reinforcement learning based vehicular cooperative control algorithm at signal-free intersection[J]. Journal of Transportation Engineering and Information, 2022, 20(2):14-24.
[17]ZHANG J W, LIU Q, Y LI S, et al. Unleashing the power of connected and automated vehicles:a dedicated link strategy for efficient management of mixed traffic[J].IEEE Transactions on Intelligent Transportation Systems, 2024, 25(9):12315-12332.
[18]SINGH B, GUPTA A. Recent trends in intelligent transportation systems:a review[J]. Journal of Transport Literature, 2015, 9(2):30-34.
[19]YE L H, YAMAMOTO T. Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput[J]. Physica A:Statistical Mechanics and Its Applications, 2018, 512:588-597.
[20]ZHANG J W, GE J W, LI S S, et al. A bi-level networkwide cooperative driving approach including deep reinforcement learning-based routing[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(1):1243-1259.
[21]LIN S, KONG Q J, HUANG Q M. A simulation analysis on the existence of network traffic flow equilibria[J].IEEE Transactions on Intelligent Transportation Systems, 2014, 15(4):1706-1713.
[22]GEROLIMINIS N, DAGANZO C F. Existence of urbanscale macroscopic fundamental diagrams:Some experimental findings[J]. Transportation Research Part B:Methodological, 2008, 42(9):759-770.
[23]LODER A, AMBÜHL L, MENENDEZ M, et al. Understanding traffic capacity of urban networks[J]. Scientific Reports, 2019, 9(1):16283.
[24]ZHANG J W, CHANG C, HE Z M, et al. CAVSim:a microscopic traffic simulator for evaluation of connected and automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(9):10038-10054.
[25]ZHANG J W, PEI H X, BAN X G, et al. Analysis of cooperative driving strategies at road network level with macroscopic fundamental diagram[J]. Transportation Research Part C:Emerging Technologies, 2022, 135:103503.
基本信息:
DOI：10.19961/j.cnki.1672-4747.2025.03.027
中图分类号:U491
引用信息:
[1]王鹏博,张嘉玮,李晓理,等.考虑道路通行能力的混合交通分流控制策略[J].交通运输工程与信息学报,2025,23(04):162-170.DOI:10.19961/j.cnki.1672-4747.2025.03.027.
基金信息:
国家重点研发计划项目（2023YFB2504400）