Smoothing regression and impact measures for accidents of traffic flows
Smoothing regression and impact measures for accidents of traffic flows
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交通流事故的平滑回归和影响措施
DOI：
10.1080/02664763.2023.2175799
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发表时间：
2023
影响因子：
1.5
通讯作者：
Huang, Hsin-Hsiung
中科院分区：
文献类型：
--
作者：
Yu, Zhou;Yang, Jie;Huang, Hsin-Hsiung
交通模式识别和事故评估是提高交通规划、道路安全和交通管理水平的关键。本文通过建立分类回归模型来刻画交通流与不同时间点之间的关系，并用带光滑样条的负二项模型来识别不同的交通流模式。它提供了交通流的平均响应曲线和贝叶斯可信区间、单一指标和对数似然差，用于交通流模式识别。在此基础上，提出了一种基于负二项模型的交通事故对交通流影响的评价方法。该方法已成功应用于实际交通流中，可用于改善交通管理。
Traffic pattern identification and accident evaluation are essential for improving traffic planning, road safety, and traffic management. In this paper, we establish classification and regression models to characterize the relationship between traffic flows and different time points and identify different patterns of traffic flows by a negative binomial model with smoothing splines. It provides mean response curves and Bayesian credible bands for traffic flows, a single index, and the log-likelihood difference, for traffic flow pattern recognition. We further propose an impact measure for evaluating the influence of accidents on traffic flows based on the fitted negative binomial model. The proposed method has been successfully applied to real-world traffic flows, and it can be used for improving traffic management.