对于交通流动事故的平滑回归和影响措施
Zhou Yu1, Jie Yang1, Hsin-Hsiung Huang2
1Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, Chicago, IL, USA.
Journal of applied statistics
|April 17, 2024
概括
本研究引入了一种使用负二项式模型进行交通模式识别和事故影响评估的新方法. 这些发现通过数据驱动的洞察力来增强交通管理和道路安全.
科学领域:
- 运输科学 运输科学
- 统计建模 统计建模
- 道路安全工程 道路安全工程
背景情况:
- 有效的交通规划,道路安全和交通管理依赖于准确的交通模式识别和事故评估.
- 现有的方法可能无法充分捕捉交通流的复杂动态或有效量化事故影响.
研究的目的:
- 开发和验证用于描述不同时间点的交通流动模式的模型.
- 为评估交通事故对交通流动动态的影响建立一个可靠的衡量标准.
- 通过加强模式识别和影响评估,改进交通管理策略.
主要方法:
- 利用分类和回归模型来分析交通流与时间之间的关系.
- 采用负二项式模型,用于交通流动模式识别.
- 开发了一种基于拟合负二项式模型的影响测量方法,以评估事故影响.
主要成果:
- 通过使用平均响应曲线和贝叶斯可信带,成功识别了不同的流量流模式.
- 用单个指数和日志概率差的量化流量模式.
- 展示了拟议方法在真实世界的交通数据上的实际应用.
结论:
- 开发的负二项式模型有效地描述了交通流动模式及其变化.
- 拟议的影响测量提供了一种可靠的方式来评估事故对交通流量的影响.
- 这种数据驱动的方法为改善交通管理和道路安全提供了巨大的潜力.
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