Yu Z, Yang J, Huang HH. Smoothing regression and impact measures for accidents of traffic flows.
J Appl Stat 2023;
51:1041-1056. [PMID:
38628452 PMCID:
PMC11018049 DOI:
10.1080/02664763.2023.2175799]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 01/27/2023] [Indexed: 02/12/2023]
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.
Collapse