Joint optimization of vehicle trajectories and intersection controllers with connected automated vehicles: Combined dynamic programming and shooting heuristic approach
Joint optimization of vehicle trajectories and intersection controllers with connected automated vehicles: Combined dynamic programming and shooting heuristic approach
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DOI：
10.1016/j.trc.2018.11.010
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
Yi Guo;Jiaqi Ma;Chenfeng Xiong;X. Li;Fang Zhou;Wei Hao
中科院分区：
文献类型：
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作者：
Yi Guo;Jiaqi Ma;Chenfeng Xiong;X. Li;Fang Zhou;Wei Hao
互联和自动驾驶汽车（CAV）技术为当今交通系统面临的挑战提供了有前途的解决方案。基于CAV技术的车辆轨迹控制和交叉口控制器优化是两种具有显著潜力的方法，以缓解拥堵，降低碰撞风险，降低燃油消耗，并减少交叉口的排放。这两种方法应整合到一个单一的过程中，这样两个方面可以同时优化，以实现最大效益。提出了一种有效的DP-SH（Dynamic Programming with Shooting Heuristic as a Subroutine）集成优化算法，该算法可以同时优化CAV和交叉口控制器的轨迹（即信号配时和交通信号相位），并开发了一个两步的方法（DP-SH和轨迹优化），以有效地获得接近最优的交叉口和轨迹控制计划。此外，所提出的DP-SH算法还可以考虑混合交通流的情况下，不同水平的CAV市场渗透。数值实验结果表明，该优化框架具有良好的性能和有效性。与自适应信号控制相比，DP-SH算法可以减少平均行程时间高达35.72%，节省能耗高达31.5%。在混合业务场景中，系统性能随着市场渗透率的提高而提高。即使是低水平的渗透，在节省燃料消耗方面也有显着的好处。的计算效率，证明在案例研究中，表明DP-SH的实时实现的适用性。
Connected and automated vehicle (CAV) technologies offer promising solutions to challenges that face today’s transportation systems. Vehicular trajectory control and intersection controller optimization based on CAV technologies are two approaches that have significant potential to mitigate congestion, lessen the risk of crashes, reduce fuel consumption, and decrease emissions at intersections. These two approaches should be integrated into a single process such that both aspects can be optimized simultaneously to achieve maximum benefits. This paper proposes an efficient DP-SH (dynamic programming with shooting heuristic as a subroutine) algorithm for the integrated optimization problem that can simultaneously optimize the trajectories of CAVs and intersection controllers (i.e., signal timing and phasing of traffic signals), and develops a two-step approach (DP-SH and trajectory optimization) to effectively obtain near-optimal intersection and trajectory control plans. Also, the proposed DP-SH algorithm can also consider mixed traffic stream scenarios with different levels of CAV market penetration. Numerical experiments are conducted, and the results prove the efficiency and sound performance of the proposed optimization framework. The proposed DP-SH algorithm, compared to the adaptive signal control, can reduce the average travel time by up to 35.72% and save the consumption by up to 31.5%. In mixed traffic scenarios, system performance improves with increasing market penetration rates. Even with low levels of penetration, there are significant benefits in fuel consumption savings. The computational efficiency, as evidenced in the case studies, indicates the applicability of DP-SH for real-time implementation.