不确定性意识的概率图神经网络用于道路级交通事故预测和预测
Xiaowei Gao1, Xinke Jiang2, James Haworth1
1SpaceTimeLab, University College London (UCL), London, UK.
Accident; analysis and prevention
|October 3, 2024
概括
一个新的空间时空零膨胀Tweedie图神经网络 (STZITD-GNN) 模型改善了城市交通事故预测. 这种先进的深度学习方法准确地识别高风险道路并量化事故不确定性,以提高道路安全.
科学领域:
- 城市规划和交通安全.
- 人工智能和机器学习
- 统计建模和风险评估.
背景情况:
- 交通事故对城市的安全和流动性构成重大风险.
- 现有的预测模型与精细的时空尺度和固有的碰撞不确定性作斗争.
- 当前的方法往往无法提供对碰撞风险等级的细微了解.
研究的目的:
- 开发一种不确定性意识的概率深度学习模型,用于道路级,每日交通事故预测.
- 解决传统方法在捕获零星碰撞事件和非碰撞数据方面的局限性.
- 为事故风险提供全面的预测,包括高风险,低风险和无风险场景.
主要方法:
- 介绍了时空零膨胀图形神经网络 (STZITD-GNN).
- 用图形神经网络集成Tweedie统计家族的非高斯碰撞数据.
- 使用零膨胀元件来区分非碰撞和低风险情况.
主要成果:
- STZITD-GNN模型在英国伦敦的现实数据中表现出比基线模型更好的性能.
- 对点估计的回归误差降低了34.60%.
- 提高了超过47%的基于间隔的不确定性指标.
结论:
- STZITD-GNN是第一个不确定性意识的概率图深度学习模型,用于多步骤的道路交通事故预测.
- 该模型通过准确预测和区分各种撞车风险水平,提供了道路安全的整体视图.
- 这种方法通过提供更精确,更可靠的交通事故风险洞察力,提高了城市交通安全.
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