智能交通信号灯管理使用预测和动态交通流分析
Kuldeep Nautiyal1, Durgaprasad Gangodkar1, Manoj Diwakar1
1Department of CSE, Graphic Era Deemed to be University, Dehradun, India.
Scientific reports
|October 24, 2025
概括
本研究介绍了一种自适应交通信号灯系统 (ATLS),该系统使用机器学习来预测交通情况并动态调整信号. 智能系统大大减少了城市交通等待时间和排放量.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 机器学习应用 机器学习应用
- 城市规划与交通管理
背景情况:
- 交通拥堵在城市地区带来了重大挑战,导致污染,燃料消耗和事故增加.
- 智能交通系统 (ITS) 和智能交通信号灯系统 (TLS) 对于改善城市交通和基础设施效率至关重要.
- 自适应交通信号系统 (ATLS) 基于实时交通需求提供动态信号定时调整,优于固定时间系统.
研究的目的:
- 提出一个自适应交通灯系统 (ATLS),将基于机器学习的交通量预测与基于压力的控制方法相结合.
- 评估拟议ATLS在减少交通拥堵和环境影响方面的有效性.
- 为了在ATLS框架内比较各种机器学习算法,以实现最佳的流量量预测.
主要方法:
- 开发了一个自适应交通信号灯系统 (ATLS),使用机器学习集成每小时和每天的交通量预测.
- 采用以压力为基础的方法来动态控制交通信号灯相位,响应当前的交通条件.
- 利用城市移动模拟器 (SUMO) 在一个孤立的十字路口对ATLS进行基于模拟的评估.
- 进行了随机森林,K-最近邻居,决策树,梯度提升和XGBoost的比较分析,用于交通预测.
主要成果:
- 拟议的ATLS显示,平均等待时间平均减少了26.3%,平均时间损失减少了22.4%.
- 总时间损失 (19.4%),平均二氧化碳排放 (23.8%) 和平均二氧化碳排放 (17.4%) 显著下降.
- 该系统的性能在12个不同的场景中得到了验证,超过了现有的方法.
结论:
- 拟议的机器学习集成自适应交通信号灯系统有效地管理城市交通流量并减少对环境的影响.
- 基于预测建模的动态交通信号控制,与传统方法相比,为缓解交通拥堵提供了更好的解决方案.
- 该研究强调了先进的ITS解决方案在创建更高效和可持续的城市交通网络方面的潜力.
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