使用贝叶斯动态物流回归来预测收费站分离区域的微观车辆碰撞风险
Xi Li1,2, Yi Fei1,2, Kongning Jin1,2
1School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, Hunan, PR China.
PloS one
|October 27, 2025
概括
本研究引入了贝叶斯动态物流回归模型,用于预测收费站车辆碰撞风险. 新方法适应交通变化,以更少的数据改进安全预测.
科学领域:
- 交通工程是交通工程.
- 运输安全运输安全
- 数据科学数据科学数据科学
背景情况:
- 没有车道标志的收费站分离区域是由于车辆编织而造成的高风险交通瓶.
- 现有的冲突预测模型无法适应动态的交通条件.
研究的目的:
- 开发一种自适应的贝叶斯动态物流回归方法,用于预测在收费站分离区域的车辆碰撞风险.
- 提高交通冲突预测模型的适应性和效率.
主要方法:
- 从高精度的车辆轨迹数据中提取了聚合的交通特征.
- 利用延长碰撞时间 (ETTC) 来测量多方向碰撞风险.
- 开发了贝叶斯动态物流回归模型,使用不同的数据采样策略.
主要成果:
- 贝叶斯模型表现出强大的自我适应性校正能力,曲线下的面积 (AUC) 值超过0.9.
- 识别了比标准后勤回归更大的影响因素,并且只需要20%的数据进行初始化.
- 随着数据输入的不断更新,显著减少了计算需求.
结论:
- 提出的贝叶斯动态物流回归方法为交通碰撞风险提供了更高的预测准确性和效率.
- 结合更丰富的预先信息和持续的数据更新,可以提高模型性能.
- 这些发现支持制定针对收费站安全的有针对性的管理策略.
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