基于多源数据融合的城市动脉道路的车辆轨迹重建方法
Zhanhang Shi1, Dong Guo1, Lili Bian2
1School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究引入了一种新的方法,使用多源数据融合重建车辆轨迹. 该方法通过提高轨迹准确性来增强交通管理,特别是在有限的探测器车辆 (PV) 数据的情况下.
科学领域:
- 运输科学 运输科学
- 数据融合数据融合
- 交通工程是交通工程.
背景情况:
- 车辆轨迹数据对于城市交通分析和管理至关重要.
- 在获得全面的轨迹数据方面存在挑战,原因是探测器车辆 (PV) 透率低和传感器覆盖率低.
- 准确的轨迹数据对于优化交通流和信号控制至关重要.
研究的目的:
- 开发一种多源数据融合方法,用于重建车辆轨迹.
- 为了解决低光伏透率和不完整的传感器数据的局限性.
- 为了提高重建车辆轨迹的准确性和流性,以实现精细的交通管理.
主要方法:
- 对未检测到的车辆的轨迹状态估计分为四种类型.
- 使用扩展的智能驾驶员模型重建初始轨迹.
- 基于颗粒过器的自我优化算法,集成固定传感器数据进行代轨迹校正.
主要成果:
- 与基线方法相比,拟议的方法显著降低了平均绝对误差 (MAE),平均绝对百分比误差 (MAPE) 和根平均平方误差 (RMSE).
- 在复杂的交通环境和低光伏透率下,性能优异.
- 重建错误随着交通密度和光伏透率的增加而减少,光伏透率对准确性产生重大影响.
结论:
- 多源数据融合方法在重建车辆轨迹方面表现出稳定性和有效性.
- 该方法为精细的城市交通管理和优化决策提供了关键的技术支持.
- 这些发现强调了光伏透率在改善轨迹重建准确度方面的重要性.
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