Citation

Yu Z, Yang J, Huang HH. Journal of Applied Statistics 2024; 51(6): 1041-1056.

Copyright

(Copyright © 2024, Sheffield City Polytechnic)

DOI

10.1080/02664763.2023.2175799

PMID

38628452

PMCID

PMC11018049

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.

Language: en

Keywords

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