信号相位、配时和自动车辆轨迹的实时交叉口优化
Real-time Intersection Optimization for Signal Phasing, Timing, and Automated Vehicles' Trajectories
摘要：
本研究旨在开发一种实时交叉口优化（RIO）控制算法，以有效地服务于互联和自动驾驶车辆（CAV）和传统车辆（CNV）的交通。本文扩展了以前的工作，考虑需求超过能力的条件和轨迹偏差重新优化决策。为了联合优化信号相位和定时（SPaT）和CAV的出发时间，我们制定了一个联合优化模型，它被归结为一个最小成本流（MCF）问题，并解决。基于MCF的优化模型被嵌入到RIO算法中，以操作信号控制器和规划CAV的运动。模拟实验表明，与基本场景相比，旅行时间减少了18-22%，容量提高了12%。
英文摘要：
This study aims to develop a real-time intersection optimization (RIO) control algorithm to efficiently serve traffic of Connected and Automated Vehicles (CAVs) and conventional vehicles (CNVs). This paper extends previous work to consider demand over capacity conditions and trajectory deviations by re-optimizing decisions. To jointly optimize Signal Phase and Timing (SPaT) and departure time of CAVs, we formulated a joint optimization model which is reduced to and solved as a Minimum Cost Flow (MCF) problem. The MCF-based optimization models is embedded into the RIO algorithm to operate the signal controller and to plan the movement of CAVs. Simulation experiments showed 18-22% travel time decrease and up to 12% capacity improvement compared to the base scenario.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
