城市规模的高分辨率交通数据集与精细的网络用于层次化的交通控制
Qinzhou Ma1, Xinling Guo1, Weifan Zhong1
1Guangdong Provincial Key Laboratory of Intelligent Transportation Systems, School of Intelligent Systems Engineering, Sun Yat-Sen University (Shenzhen Campus), Shenzhen, 518107, China.
Scientific data
|February 26, 2026
概括
本研究介绍了全面的城市交通数据集,用于对比控制策略. 这些数据集在多种城市网络中提供高分辨率数据,实现现实的交通模拟和先进的控制方法.
科学领域:
- 运输科学 运输科学
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 城市交通控制受到各种网络拓学的大规模,高分辨率数据集缺乏的阻碍.
- 现有的数据集往往缺乏先进的交通管理策略所需的细节性.
研究的目的:
- 提供全面的交通数据集,用于对城市交通控制策略进行基准测试.
- 提供高可靠性数据,支持层次交通控制架构的开发.
- 通过各种城市规模和网络配置实现现实的交通模拟.
主要方法:
- 收集并处理了来自五个城市的交通数据,其规模和交叉路口布局各不相同.
- 为城数据集开发了精细的网络表示,包括完整的车辆路径记录.
- 集成的车辆类型信息和长期历史行程数据与需求模式分析.
主要成果:
- 生成多层次的交通状态数据 (车辆,车道交叉点,网络层).
- 城数据集拥有数百个具有高时空分辨率的交叉点.
- 数据集捕捉周期性和假期引起的需求变化,以实现现实的模拟.
结论:
- 提出的数据集解决了在城市交通控制研究中对高质量的数据的关键需求.
- 这些资源有助于开发和评估先进的多层次交通控制策略.
- 这些数据集支持创建现实的模拟环境,用于测试交通管理解决方案.
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