可解释的多变量变压器网络用于区域一级的短期自行车撞车风险预测
Ling Deng1, Chengcheng Xu1, Pan Liu1
1School of Transportation, Southeast University, Southeast University Road #2, Nanjing 211189, China.
Accident; analysis and prevention
|July 27, 2025
概括
可解释多变量变压器网络 (IMTN) 通过有效建模时空数据和处理类不平衡来改善短期自行车撞车预测. 它确定了加强城市安全规划的关键基础设施特征.
科学领域:
- 城市规划和交通安全.
- 人工智能和机器学习
- 运输工程 运输工程 运输工程
背景情况:
- 短期自行车撞车预测对于城市安全干预至关重要.
- 现有的方法难以处理复杂的数据,阶级不平衡和可解释性.
- 自行车基础设施在碰撞风险中的作用尚未得到充分研究.
研究的目的:
- 开发一种可解释的深度学习模型,用于准确的短期自行车撞车预测.
- 解决时空依赖模型和阶级不平衡方面的挑战.
- 确定影响区域撞车风险的关键自行车基础设施特征.
主要方法:
- 提出了使用变压器编码器块的可解释多变量变压器网络 (IMTN).
- 通过预测每个地区的风险,实施了针对阶级不平衡的新方法.
- 使用了改进的层级相关性传播 (LRP) 来提高模型的可解释性.
- 集成多种数据源,包括事故记录,交通,天气和48个基础设施功能.
主要成果:
- IMTN的表现优于基线模型,虚假阳性率 (FPR) 降低了高达9.08%,AUC提高了3.49%.
- 该模型在1小时的时间分辨率下实现了峰值性能,这表明在高分辨率设置中的有效性.
- 解释性分析强调了分离的自行车道,谢菲尔德站点和彩色路径标记作为重要的基础设施变量.
结论:
- IMTN为短期自行车撞车预测提供了一种有效和可解释的解决方案.
- 拟议的类失衡处理方法提高了精细时间分辨率的性能.
- 识别的基础设施特征为改善城市自行车安全提供了可操作的见解.
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