对于无信号交叉路口的最佳车辆调度模型,在连接和自动化车辆环境中考虑总线优先级.
Dongliang Wang1, Shunjie Jiang1, Guorong Zheng2
1College of Software, Jilin University, Changchun 130012, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究优化了连接和自动化车辆 (CAV) 在无信号交叉点上的车辆调度. 新型车型减少了平均车辆延迟,并优先考虑公共汽车,提高了交通效率.
科学领域:
- 智能运输系统 智能运输系统
- 交通工程是交通工程.
- 优化算法 优化算法
背景情况:
- 连接和自动化车辆 (CAV) 在没有信号的交叉口上为交通管理带来了新的挑战.
- 现有的模型往往过于简化了冲突区域,忽视了公共汽车优先级,导致效率低下.
- 冲突点的空间时间稀疏性需要先进的调度解决方案.
研究的目的:
- 开发一个最佳的调度模型,用于CAV在没有信号的交叉路口与公共汽车优先.
- 解决现有模型在冲突区域分析和公共汽车优先级方面的局限性.
- 为了提高计算效率和交通安排的准确性.
主要方法:
- 建立了交叉点坐标系统和冲突区域分析模型.
- 开发了一个最佳的调度模型,使用车辆进入时间和横向车道变化作为决策变量.
- 实施了搜索空间缩小方法和改进的双层多种群粒子群集优化 (PSO) 算法.
主要成果:
- 与传统和其他无信号算法相比,拟议的方法显著减少了平均车辆延迟.
- 它在计算成本和效率之间实现了有利的平衡.
- 与非优先方案相比,纳入公共汽车优先方案使人均平均延误减少了18.95%.
结论:
- 开发的最佳调度模型有效地管理在无信号交叉点的CAV.
- 包括公共汽车优先级显然提高了整体交通效率,并减少了人均延误.
- 该方法为智能运输系统提供了计算效率高,准确的解决方案.
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