基于POI行程特征和行人暴露强度的空间时空深度学习方法来预测行人撞车风险
Manze Guo1, Bruce Janson2, Yongxin Peng3
1Civil Aviation Management Institute of China, Beijing 100102, China.
Accident; analysis and prevention
|February 9, 2024
概括
这项研究开发了一个深度学习模型 (VT-NET) 来预测行人撞车风险. 高风险地区集中在城市中心,特别是在周末和商业区附近.
科学领域:
- 城市规划和交通安全.
- 深度学习在风险评估中的应用.
- 易受伤害的道路使用者安全
背景情况:
- 步行者是脆弱的道路使用者,在复杂的交通环境中面临重大风险.
- 量化行人暴露和预测碰撞风险的现有方法是有限的.
- 了解行人风险的时空模式对于有针对性的干预至关重要.
研究的目的:
- 开发一种用于量化行人暴露的多维指标.
- 提出并验证VT-NET深度学习模型,用于预测行人碰撞风险.
- 分析高风险行人区域的时空特征.
主要方法:
- 使用POI属性,强度,交通量和可行性构建了一个多维的行人暴露指标.
- 工程特征包括天气,社会经济因素和建筑环境.
- 开发了VT-NET模型,集成VGG16和变压器网络,单独训练时间和非时间风险特征.
- 在四个尺度上生成了时空空间网格级碰撞风险预测.
主要成果:
- VT-NET模型有效地预测了行人撞车风险.
- 高风险地区主要在城市中心被确定,持续数小时.
- 风险区域在深夜/早晨消散,周末在商业,公共,教育和医疗设施附近更集群.
结论:
- VT-NET模型提供了一个强大的方法,用于空间时空行人撞车风险预测.
- 城市中心,特别是在活动高峰时期和周末,代表了行人安全干预的关键区域.
- 了解建筑环境和社会经济因素的影响是减轻行人风险的关键.
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