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

王方凯; 杨晓光; 江泽浩; 刘聪健

doi:10.3963/j.jssn.1674-4861.2024.01.009

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

doi: 10.3963/j.jssn.1674-4861.2024.01.009

1.
同济大学道路与交通工程教育部重点实验室上海 200092
2.
华中科技大学土木与水利工程学院武汉 430074

基金项目:

国家自然科学基金项目 52102377

国家自然科学基金项目 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

详细信息

作者简介:
王方凯（1982—），博士研究生. 研究方向：交通管理与控制. E-mail: fangkaiwang@tongji.edu.cn

通讯作者:
刘聪健（1997—），博士研究生. 研究方向：人机混驾交通系统优化设计. E-mail: liucongjian97@hust.edu.cn

中图分类号: U491
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- 被引次数: 0
出版历程
- 收稿日期: 2023-06-28
- 网络出版日期: 2024-05-31

Joint Optimization of Intersection Signal Control and Trajectory Control in Novel Heterogenous Traffic Flow Scenarios

1.
Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 200092, China
2.
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

摘要

摘要: 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。
- 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
Abstract: In scenarios of mixed traffic flows consisting of human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), existing intersection joint optimization methods place high computational demands on either centralized controllers or on-board computing units due to centralized and individual vehicle controls, respectively. 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 requirements needed for signal control and CAV trajectory optimization, thereby flexibly allocating computational resources based on the capacities of central controllers and on-board computing units. The upper-level model predicts traffic flow states and optimizes signal control parameters by dynamically adjusting cell lengths to reduce the computational load on central controllers. The lower-level model uses these traffic state predictions to globally plan CAV trajectories, thereby enhancing intersection throughput. To improve solution optimality and real-time response, an iterative 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: ① Compared 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 propagation upstream. ② With CAV penetration rates of 30%, 60% and 90%, the optimization percentages are 2.51%, 5.08% and 7.88% respectively. ③ In scenarios where the inbound flow rate exceeds 3, 000 pcu/h, optimal signal control 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.
- traffic control /
- novel heterogenous traffic flow /
- signal control and trajectory optimization /
- bi-level programming model

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图 1 研究场景

Figure 1. Research scenario

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图 2 交叉口元胞化方法

Figure 2. Intersection cellular method

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图 3 随机梯度下降法与遗传算法相结合求解流程图

Figure 3. Flow chart of stochastic gradient descent method combined with genetic algorithm

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图 4 案例交叉口与全息轨迹数据获取

Figure 4. Case intersection and holographic trajectory data acquisition

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图 5 改进CTM模拟实际交通流的效果

Figure 5. Effect of improved CTM to simulate actual traffic flow

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图 6 算法收敛效果

Figure 6. Effect of solution algorithm convergence

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图 7 平均运行速度

Figure 7. Average running speed

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图 8 时空轨迹图

Figure 8. Space-time trajectory diagram

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图 9 各策略对TTPFU的优化效果

Figure 9. Optimization effect of each strategy on TTPFU

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图 10 Optimization effect of each strategy on TTPFU

Figure 10. Comparison of algorithm solving time

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表 1 仿真参数设定

Table 1. Simulation parameter Settings

参数	取值
元胞长度/m	20
左转进口道的通行能力/（pcu/h）	1 200
直行进口道的通行能力/（pcu/h）	1 400
阻塞密度/（pcu/km）	200
CAV间的饱和车头时距/s	1.5
HV间的饱和车头时距/s	2.5
车辆通过交叉口的速度上限/（m/s）	10
加速度上限/（m/s²）	2
加速度下限/（m/s²）	-4

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