Smoothing regression and impact measures for accidents of traffic flows

Abstract

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.

Keywords:

• Negative binomial regression model
• smoothing spline ANOVA model
• traffic flow pattern
• holiday traffic flow
• impact factor of accidents

Disclosure statement

The authors declare no conflict of interest. All authors reviewed the results and approved the final version of the manuscript.

Additional information

Funding

This research was supported in part by the National Science Foundation grants, DMS-1924792 and DMS-1924859.