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基于视频监控数据和蛇优化算法的交通信号时间优化模型.

Ruixiang Cheng1, Zhihao Qiao1, Jiarui Li1

  • 1School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的交通信号时间优化模型,使用人工智能来预测交通流量并改善城市交通管理. 该模型显著减少了交通拥堵,为更智能的城市规划提供了切实可行的解决方案.

关键词:
信号时间优化信号时间优化模拟模拟是指一个模拟模拟.蛇优化算法 蛇优化算法交通堵塞 交通堵塞 交通堵塞

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科学领域:

  • 智能运输系统 智能运输系统
  • 城市规划 城市规划
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 城市的快速增长加剧了交通拥堵和环境污染.
  • 有效的城市交通管理在很大程度上依赖于信号定时优化.
  • 现有的方法可能无法充分应对动态的交通条件.

研究的目的:

  • 提出一个基于VISSIM模拟的交通信号定时优化模型.
  • 为解决由动态交通流动引起的城市交通拥堵问题.
  • 提高城市交通管理系统的效率.

主要方法:

  • 使用YOLO-X从视频监控中提取道路信息.
  • 使用长期短期内存 (LSTM) 来预测未来的流量.
  • 使用蛇优化 (SO) 算法优化模型.
  • 通过使用VISSIM模拟的实证示例验证了模型.

主要成果:

  • 与固定时间相比,拟议的模型提供了一个改进的信号定时方案.
  • 在观察期间,交通拥堵明显减少了23.34%.
  • 维西姆模拟证实了模型在现实场景中的有效性.

结论:

  • 开发的模型提供了一种可行和有效的交通信号时间优化的方法.
  • 人工智能模型的整合增强了对流量流动的预测能力.
  • 这项研究有助于缓解城市交通拥堵和改善交通管理.