通过道路类型对单车撞车严重性的时空空间相互作用进行建模
1Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430000, PR China.
Journal of safety research
|June 18, 2023
概括
新的时空相互作用逻辑 (STI-logit) 模型通过考虑复杂的时空相关性,有效地分析单车 (SV) 撞车严重性. 这种先进的模型提供了一个卓越的框架,用于识别关键的事故原因,并提高道路安全.
科学领域:
- 交通安全工程 交通安全工程
- 运输科学 运输科学
- 统计建模 统计建模
背景情况:
- 在单车 (SV) 撞车严重程度分析中,时空相关性至关重要.
- 在SV机中时空因素之间的相互作用仍未得到充分研究.
- 现有的模型往往无法充分捕捉这些复杂的相互作用.
研究的目的:
- 提出和验证一种新的时空相互作用逻辑 (STI-logit) 模型用于SV撞击严重性回归.
- 将STI-logit模型的性能与传统的时空和随机参数逻辑模型进行比较.
- 调查各种因素对不同类型道路撞车严重性的影响.
主要方法:
- 空间时间交互逻辑 (STI-logit) 模型的开发.
- 使用混合元件和高斯条件自回归 (CAR) 进行时空相互作用.
- 与时空逻辑模型和随机参数逻辑模型进行校准和比较.
- 为动脉,次要和分支道路进行单独的建模.
主要成果:
- 在预测SV撞车严重程度方面,STI-logit模型显著优于现有模型.
- 与高斯的CAR相比,STI-logit模型具有混合组件,无论道路类型如何,都显示出更好的适应性.
- 分心驾驶,醉酒驾驶,摩托车,黑暗条件和固定物体碰撞增加了严重的SV碰撞.
- 卡车和行人碰撞降低了严重的SV碰撞的可能性.
- 路边的硬障碍物与严重的车祸有显著的积极关联,仅在分支道路上.
结论:
- STI-logit模型提供了一个强大的框架,通过结合时空相互作用来分析SV撞击严重程度.
- 同时建模稳定和不稳定的时空风险模式可以提高模型的准确性.
- 确定特定的促成因素及其在道路类型上的不同影响对于有针对性的安全干预至关重要.
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