新型混合交通流场景下交叉口信号控制和轨迹控制协同优化方法OA北大核心CSTPCD
Joint Optimization of Intersection Signal Control and Trajectory Control in Novel Heterogenous Traffic Flow Scenarios
针对人类驾驶车辆(human driven vehicle,HDV)和智能网联车辆(connected and autonomous vehicle,CAV)组成的新型混合交通流场景,现有的交叉口协同控制方法中,集中控制和单车控制分别对中央控制器的算力和车载计算单元的算力要求较高.本文研究了1种将元胞传输模型(cell transmission model,CTM)与双层规划模型相结合的协同优化方法,利用可调整的元胞长度平衡求解信号控制与CAV轨迹优化2个问题所需的算力,从而灵活地根据中央控制器和车载计算单元的算力分配计算资源;通过上层模型预测交通流状态并优化信号控制参数,引入动态调整元胞长度规则,降低中央控制器的计算负担;基于上层的交通状态预测结果,利用下层模型对CAV轨迹进行全局规划,进一步提升交叉口通行效率.同时,为了提升解的最优性和求解的实时性,采用结合随机梯度下降法和遗传算法的迭代优化算法,避免陷入局部最优的同时提升求解效率.最后以无锡市先锋中路与春风南路交叉口数据为例,验证了不同CAV渗透率下优化的效果,结果表明:①相较于基准方案,本文提出的协同优化方案最高可以降低交叉口8.09%的车均行程时间,降低了交叉口拥堵向上游的传播;②当CAV渗透率为30%、60%和90%时,优化比例为2.51%、5.08%和7.88%;③在进口道流量大于3 000 pcu/h时,仍能在100s内获得最优信号控制方案,可支持实时优化.该方法可以有效改善城市交通拥堵,提高新型混合交通流场景下交叉口的通行效率.
In scenarios of mixed traffic flows consisting of human-driven vehicles(HDVs)and connected and auton-omous vehicles(CAVs),existing intersection joint optimization methods place high computational demands on ei-ther centralized controllers or on-board computing units due to centralized and individual vehicle controls,respec-tively.This paper studies a joint optimization method that integrates the cell transmission model(CTM)with a bi-level programming model.This approach utilizes adjustable cell lengths to balance the computational require-ments needed for signal control and CAV trajectory optimization,thereby flexibly allocating computational resourc-es based on the capacities of central controllers and on-board computing units.The upper-level model predicts traf-fic flow states and optimizes signal control parameters by dynamically adjusting cell lengths to reduce the computa-tional load on central controllers.The lower-level model uses these traffic state predictions to globally plan CAV tra-jectories,thereby enhancing intersection throughput.To improve solution optimality and real-time response,an itera-tive optimization algorithm that combines stochastic gradient descent with a genetic algorithm is employed to avoid local optima and enhance solution efficiency.Using data from the intersection of Xian-feng Middle Road and Chun-feng South Road in Wuxi City as an example,the optimization effects under different CAV penetration rates were verified.Results show:①Compared to the baseline scenario,the proposed collaborative optimization scheme can reduce average vehicle travel time at the intersection by up to 8.09%,effectively reducing congestion propaga-tion upstream.② With CAV penetration rates of 30%,60%and 90%,the optimization percentages are 2.51%,5.08%and 7.88%respectively.③In scenarios where the inbound flow rate exceeds 3,000 pcu/h,optimal signal con-trol schemes can still be obtained within 100 seconds,supporting real-time optimization.The method can effectively improve urban traffic congestion and enhance the efficiency of intersections in novel mixed traffic flow scenarios.
王方凯;杨晓光;江泽浩;刘聪健
同济大学道路与交通工程教育部重点实验室 上海 200092华中科技大学土木与水利工程学院 武汉 430074
交通运输
交通控制新型混合交通流信号控制与轨迹优化双层规划模型
traffic controlnovel heterogenous traffic flowsignal control and trajectory optimizationbi-level pro-gramming model
《交通信息与安全》 2024 (001)
76-83,123 / 9
国家自然科学基金项目(52102377、52072264)、道路与交通工程教育部重点实验室(同济大学)开放基金项目(K202201)资助
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