使用DEA在混合交通中使用人类驾驶和自动驾驶车辆的十字路口过路策略
Jiajun Shen1, Zhipeng Zhou1, Guanyu Fu1
1College of Civil Science and Engineering, Yangzhou University, Yangzhou, China.
PloS one
|April 29, 2025
概括
本研究介绍了一种新的交通信号优化模型,用于混合交通环境. 该模型通过减少人均延误和旅行时间来提高交叉路口的效率.
科学领域:
- 交通工程是交通工程.
- 智能运输系统 智能运输系统
- 运营研究 运营研究
背景情况:
- 现代交叉路口面临的挑战是管理混合交通,包括人驾驶和自动驾驶车辆.
- 优化道路权利分配对于高效的交通流量和减少拥堵至关重要.
研究的目的:
- 在混合交通环境中开发和评估交叉路口的多目标优化模型.
- 通过最大限度地减少人均延误,旅行时间和交通量来提高交叉路口的效率.
主要方法:
- 使用多目标数据环境分析 (DEA) 评估来优化优先权.
- 采用过路效率评估方法 (CREE) 来确定最佳的交通流程顺序.
- 模拟了使用SUMO软件的拟议策略,并与执行控制等基准测试进行了对比.
主要成果:
- 拟议的优化策略显著减少了人均延误和旅行时间.
- 与传统的动作控制和先来先服务的方法相比,实现了更好的交通流效率.
- 在混合自动化和人类驾驶场景中证明了该模型的有效性.
结论:
- 开发的优先优化模型有效地提高了混合交通中的交叉路口性能.
- 该模型为未来的智能运输系统改进提供了一个有希望的方法.
- 进一步的研究可以探索适应动态交通条件的实时调整.
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