适应式动态编程信号控制系统用于连接车辆环境中的人
Zongyuan Wu1, Shiming Li1, Gen Li2
1School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了使用联网车辆数据的基于人身的自适应控制算法 (PB-ACA),以减少城市交叉路口的延误. PB-ACA根据人级延迟优先考虑交通信号,显著减少平均延迟,有利于高占用车辆.
科学领域:
- 交通工程是交通工程.
- 运输系统 运输系统
- 智能运输系统 (ITS) 是一种智能运输系统.
背景情况:
- 城市交通拥堵和人员延迟是重大挑战.
- 传统的交通控制方法往往侧重于车辆,而不是个人.
- 联网车辆 (CV) 数据为交通管理提供了新的机会.
研究的目的:
- 为城市交叉路口提出和评估一种新的基于人体的自适应控制算法 (PB-ACA).
- 通过利用简历数据和个人层面的影响,尽量减少普通人的延迟.
- 为了提高交通信号控制在互联环境中的公平性和效率.
主要方法:
- 开发了一个基于人身的自适应控制算法 (PB-ACA),集成CV数据 (占用率,轨迹,速度).
- 采用三层动态编程方法来最大限度地减少人员延迟.
- 集成了一个信号相位过渡探索机制和通用的车辆模型,用于准确的排队预测.
- 使用SUMO进行了微模拟实验,将PB-ACA与FTCA,ILACA和VBACVSC进行比较.
主要成果:
- 与固定时间控制 (FTCA) 相比,PB-ACA可将平均人体延迟降低高达55%.
- 与诱导环激活控制 (ILACA) 相比,PB-ACA实现了42%的减少.
- 与基于车辆的自适应式CV信号控制 (VBACVSC) 相比,PB-ACA显示了11%的改进,特别是在高占用率的车辆中.
结论:
- PB-ACA有效地减少了城市交叉路口的人员延误,优先考虑人员而不是车辆.
- 该算法在交通效率和公平性方面取得了显著的改善.
- PB-ACA为未来的智能运输系统提供了一个有希望的方法,利用CV数据.
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