Autonomous vehicle technology will transform fundamentally urban traffic systems.To better enhance the coming era of connected and autonomous vehicles,effective control strategies that interact wisely with these intel...Autonomous vehicle technology will transform fundamentally urban traffic systems.To better enhance the coming era of connected and autonomous vehicles,effective control strategies that interact wisely with these intelligent vehicles for signalized at-grade intersections are indispensable.Vehicle-to-infrastructure communication technology offers unprecedented clues to reduce the delay at signalized intersections by innovative information-based control strategies.This paper proposes a new dynamic control strategy for signalized intersections with vehicle-to-signal information.The proposed strategy is called periodic vehicle holding(PVH)strategy while the traffic signal can provide information for the vehicles that are approaching an intersection.Under preliminary autonomous vehicle(PAV)environment,left-turning and through-moving vehicles will be sorted based on different information they receive.The paper shows how PVH reorganizes traffic to increase the capacity of an intersection without causing severe spillback to the upstream intersection.Results show that PVH can reduce the delay by approximately 15%at a signalized intersection under relatively high traffic demand.展开更多
在异质交通流背景下,针对交通信号调度与车辆轨迹规划协同问题,本文提出集信号和轨迹于一体的融合控制模型。该模型采用竞争双深度Q网络算法(Dueling Double Deep Q Network, D3QN),通过深度强化学习技术对交通信号和车辆轨迹进行同步优...在异质交通流背景下,针对交通信号调度与车辆轨迹规划协同问题,本文提出集信号和轨迹于一体的融合控制模型。该模型采用竞争双深度Q网络算法(Dueling Double Deep Q Network, D3QN),通过深度强化学习技术对交通信号和车辆轨迹进行同步优化,旨在实现交通效率与生态驾驶的双重目标,并基于SUMO(Simulation of Urban Mobility)仿真平台对模型进行全面验证。仿真结果表明:与基准模型相比,单一优化策略虽然能在一定程度上改善交叉口性能,但存在整体效率提升受限的问题;本文提出的融合控制模型结合了宏观交通流与微观车辆行为的优化,使车均延误降低66.99%,燃油消耗减少11.26%,同时CO_(2)等污染物排放量也显著减少。进一步的敏感性分析揭示了系统性能随网联自动驾驶汽车(Connected and Autonomous Vehicles, CAV)渗透率的变化规律修正:当渗透率达到一定水平后,性能提升幅度逐渐减小,且模型在不同交通流量条件下均展现出稳定的优化效果,这一结果证实了该控制方法在城市交叉口环境中的适应性和鲁棒性。展开更多
既有交叉口信号配时与网联自动驾驶车辆(Connected and Automated Vehicle,CAV)轨迹规划协同优化中,未考虑CAV环境下出口、左转、直行及右转车道数在运营期可灵活动态调整的优势。本文结合CAV技术特征,提出一套CAV环境下交叉口车道分配...既有交叉口信号配时与网联自动驾驶车辆(Connected and Automated Vehicle,CAV)轨迹规划协同优化中,未考虑CAV环境下出口、左转、直行及右转车道数在运营期可灵活动态调整的优势。本文结合CAV技术特征,提出一套CAV环境下交叉口车道分配可动态调整的控制规则,称为灵活车道策略,与已有固定车道策略相比,实现了运营期交叉口各方向出口车道数和进口车道数(包括左转、直行和右转)的灵活调整。将车道分配和信号配时与CAV轨迹规划纳入到一个统一优化框架中,构建混合整数线性规划优化模型,同时,可根据各个方向车道分配情况自动生成可行的相位相序方案,并通过案例分析验证模型的有效性。研究结果表明:优化模型可根据各流向交通需求生成最优车道分配方案,尤其是当固定车道策略的车道分配与各流向交通组成不匹配时,灵活车道策略有助于提升交叉口通行效率;在低流量场景,灵活车道策略降低了4.08%的车均延误;在高流量场景,交叉口采用固定车道策略将处于过饱和状态,而灵活车道策略依然能满足通行需求。展开更多
文摘Autonomous vehicle technology will transform fundamentally urban traffic systems.To better enhance the coming era of connected and autonomous vehicles,effective control strategies that interact wisely with these intelligent vehicles for signalized at-grade intersections are indispensable.Vehicle-to-infrastructure communication technology offers unprecedented clues to reduce the delay at signalized intersections by innovative information-based control strategies.This paper proposes a new dynamic control strategy for signalized intersections with vehicle-to-signal information.The proposed strategy is called periodic vehicle holding(PVH)strategy while the traffic signal can provide information for the vehicles that are approaching an intersection.Under preliminary autonomous vehicle(PAV)environment,left-turning and through-moving vehicles will be sorted based on different information they receive.The paper shows how PVH reorganizes traffic to increase the capacity of an intersection without causing severe spillback to the upstream intersection.Results show that PVH can reduce the delay by approximately 15%at a signalized intersection under relatively high traffic demand.
文摘在异质交通流背景下,针对交通信号调度与车辆轨迹规划协同问题,本文提出集信号和轨迹于一体的融合控制模型。该模型采用竞争双深度Q网络算法(Dueling Double Deep Q Network, D3QN),通过深度强化学习技术对交通信号和车辆轨迹进行同步优化,旨在实现交通效率与生态驾驶的双重目标,并基于SUMO(Simulation of Urban Mobility)仿真平台对模型进行全面验证。仿真结果表明:与基准模型相比,单一优化策略虽然能在一定程度上改善交叉口性能,但存在整体效率提升受限的问题;本文提出的融合控制模型结合了宏观交通流与微观车辆行为的优化,使车均延误降低66.99%,燃油消耗减少11.26%,同时CO_(2)等污染物排放量也显著减少。进一步的敏感性分析揭示了系统性能随网联自动驾驶汽车(Connected and Autonomous Vehicles, CAV)渗透率的变化规律修正:当渗透率达到一定水平后,性能提升幅度逐渐减小,且模型在不同交通流量条件下均展现出稳定的优化效果,这一结果证实了该控制方法在城市交叉口环境中的适应性和鲁棒性。
文摘既有交叉口信号配时与网联自动驾驶车辆(Connected and Automated Vehicle,CAV)轨迹规划协同优化中,未考虑CAV环境下出口、左转、直行及右转车道数在运营期可灵活动态调整的优势。本文结合CAV技术特征,提出一套CAV环境下交叉口车道分配可动态调整的控制规则,称为灵活车道策略,与已有固定车道策略相比,实现了运营期交叉口各方向出口车道数和进口车道数(包括左转、直行和右转)的灵活调整。将车道分配和信号配时与CAV轨迹规划纳入到一个统一优化框架中,构建混合整数线性规划优化模型,同时,可根据各个方向车道分配情况自动生成可行的相位相序方案,并通过案例分析验证模型的有效性。研究结果表明:优化模型可根据各流向交通需求生成最优车道分配方案,尤其是当固定车道策略的车道分配与各流向交通组成不匹配时,灵活车道策略有助于提升交叉口通行效率;在低流量场景,灵活车道策略降低了4.08%的车均延误;在高流量场景,交叉口采用固定车道策略将处于过饱和状态,而灵活车道策略依然能满足通行需求。
