Spatiotemporal intersection control in a connected and automated vehicle environment
Spatiotemporal intersection control in a connected and automated vehicle environment
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DOI：
10.1016/j.trc.2018.02.001
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发表时间：
2018-04
影响因子：
8.3
通讯作者：
Yiheng Feng;Chunhui Yu;Henry X. Liu
中科院分区：
文献类型：
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作者：
Yiheng Feng;Chunhui Yu;Henry X. Liu
目前的交通控制研究主要集中在交通信号的优化和车辆轨迹的优化上。随着互联和自动驾驶汽车（CAV）技术的快速发展，配备专用短程通信（DSRC）的车辆不仅可以与其他汽车通信，还可以与基础设施通信。车辆轨迹和交通信号的联合控制变得可行，并且可以在系统效率和环境可持续性方面获得更大的效益。交通管制框架有望从一维（空间或时间）扩展到二维（时空）。本文研究了一种孤立交叉口的联合控制框架。将控制框架建模为两阶段优化问题，第一阶段为信号优化，第二阶段为车辆轨迹控制。将信号优化建模为一个以最小化车辆延误为目标的动态规划问题。将最优控制理论应用于以燃油消耗和排放最小为目标的车辆轨迹控制问题。采用简化的目标函数对最优控制问题进行解析求解，使两阶段模型得到有效求解。仿真结果表明，与不优化车辆轨迹的固定时间和自适应信号控制相比，所提出的联合控制框架能够在不同需求水平下降低车辆延迟和排放。对于一个简单的两相交叉口，减少的车辆延误和二氧化碳排放量分别可达24.0%和13.8%。灵敏度分析表明，最大加减速率对车辆延迟性能和减排性能均有显著影响。进一步扩展到一个完整的八相交叉口，通过联合控制框架显示出类似的延迟和减排模式。
Current research on traffic control has focused on the optimization of either traffic signals or vehicle trajectories. With the rapid development of connected and automated vehicle (CAV) technologies, vehicles equipped with dedicated short-range communications (DSRC) can communicate not only with other CAVs but also with infrastructure. Joint control of vehicle trajectories and traffic signals becomes feasible and may achieve greater benefits regarding system efficiency and environmental sustainability. Traffic control framework is expected to be extended from one dimension (either spatial or temporal) to two dimensions (spatiotemporal). This paper investigates a joint control framework for isolated intersections. The control framework is modeled as a two-stage optimization problem with signal optimization at the first stage and vehicle trajectory control at the second stage. The signal optimization is modeled as a dynamic programming (DP) problem with the objective to minimize vehicle delay. Optimal control theory is applied to the vehicle trajectory control problem with the objective to minimize fuel consumption and emissions. A simplified objective function is adopted to get analytical solutions to the optimal control problem so that the two-stage model is solved efficiently. Simulation results show that the proposed joint control framework is able to reduce both vehicle delay and emissions under a variety of demand levels compared to fixed-time and adaptive signal control when vehicle trajectories are not optimized. The reduced vehicle delay and CO2emissions can be as much as 24.0% and 13.8%, respectively for a simple two-phase intersection. Sensitivity analysis suggests that maximum acceleration and deceleration rates have a significant impact on the performance regarding both vehicle delay and emission reduction. Further extension to a full eight-phase intersection shows a similar pattern of delay and emission reduction by the joint control framework.