论文标题
实时交叉点优化，用于信号相分，时机和自动化车辆的轨迹
Real-time Intersection Optimization for Signal Phasing, Timing, and Automated Vehicles' Trajectories
论文作者
论文摘要
这项研究旨在开发实时交叉点优化（RIO）控制算法，以有效地为连接和自动化车辆（CAVS）和常规车辆（CNV）的交通服务。本文扩展了以前的工作，以考虑对能力条件和轨迹偏差的需求，通过重新提高决策。为了共同优化CAV的信号相和时机（吐口水）和出发时间，我们制定了一个关节优化模型，该模型被简化为最小成本流（MCF）问题。基于MCF的优化模型嵌入了RIO算法中，以操作信号控制器并计划CAVS的运动。模拟实验显示，与基本情况相比，旅行时间减少了18-22％，容量提高了12％。
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.
