在车辆互联网中的协作智能交通规划
1School of Telecommunications Engineering, Xidian University, Xi'an 710071, China.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
本研究介绍了一种人工智能驱动的交通规划算法,以应对城市拥堵. 该方法优化了交通信号定时和车辆路线,大大增加了准时到达和改善城市流动性.
科学领域:
- 智能运输系统 智能运输系统
- 运营研究 运营研究
- 人工智能的人工智能
背景情况:
- 城市交通拥堵是由非智能系统和规划限制加剧的.
- 现有的方法与不可预测的交通流量和通勤时间作斗争.
- 越来越多的车辆给当前的城市交通基础设施带来了压力.
研究的目的:
- 为城市环境开发智能交通规划算法.
- 为了应对交通拥堵和不可预测的通勤时间的挑战.
- 提高城市交通管理系统的效率.
主要方法:
- 构建多队列,多服务器排队模型与服务器假期.
- 开发用于通勤路径的多跳式级联队列模型.
- 关于使用交通传感器进行动态规划的协作AI算法的建议.
- 优化交通信号持续时间和车辆行驶路径.
主要成果:
- 基于交通流量和信号持续时间的路径延迟成本的理论分析.
- 人工智能算法根据实时拥堵动态调整信号和路线.
- 模拟显示,车辆准时到达比率至少增加了20%.
- 在提高效率的同时,满足通勤延迟要求.
结论:
- 拟议的基于人工智能的协作交通规划算法有效地减少了城市拥堵.
- 该方法显著提高了车辆准时到达的比例.
- 这种方法为未来的汽车互联网 (IoV) 系统提供了可行的解决方案.
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