Smoothing regression and impact measures for accidents of traffic flows

• Published in: Journal of applied statistics Vol. 51; no. 6; pp. 1041 - 1056
• Main Authors: Yu, Zhou, Yang, Jie, Huang, Hsin-Hsiung
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 25.04.2024; Taylor & Francis Ltd
• Subjects: Accidents; Evaluation; Flow distribution; holiday traffic flow; impact factor of accidents; Negative binomial regression model; Pattern recognition; Regression models; Safety management; Smoothing; smoothing spline ANOVA model; Spline functions; Statistical methods; Traffic flow; traffic flow pattern; Traffic management; Traffic planning; Traffic safety; holiday traffic flow; Negative binomial regression model; impact factor of accidents; traffic flow pattern; smoothing spline ANOVA model
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2023.2175799

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.