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Developing multivariate time series models to examine the interrelations between police enforcement, traffic violations, and traffic crashes

journal contribution
posted on 18.03.2021, 11:13 by Mingjie Feng, Xuesong Wang, Mohammed Quddus
© 2020 Elsevier Ltd Safer roads and police enforcement are closely associated since the latter directly encourages road users to improve their behavior by complying with basic traffic rules and laws. Understanding the relationships between police enforcement, driving behavior, and traffic safety is a prerequisite for optimizing enforcement strategies. However, there is a dearth of research on the contemporaneous relationships between these three parameters. Using multivariate time series techniques, this study provides an in-depth analysis of contemporaneous relationships and dynamic interactions among police enforcement, traffic violations, and traffic crashes. The amount of police patrol time per day was used as a surrogate measure for police enforcement intensity. A vector autoregressive (VAR) model was first used to examine the influences of exogenous factors including weather conditions and holidays. Based on the findings of the VAR model, a structural vector autoregressive (SVAR) model was developed to determine contemporaneous effects; the Granger causality test was employed to detect any dynamic interactions between the three parameters. The results indicated that traffic crashes and violations had weekly variation and were significantly impacted by holiday and weather conditions, while police patrol time was not impacted. A contemporaneous negative impact of police patrol time was found in traffic crashes: each 1% increase in police patrol time was associated with a 0.15% decrease in contemporaneous crash frequency. The findings from the Granger causality test demonstrated that police patrol time did not Granger-cause traffic crashes, but crashes Granger-caused police patrol time. The significant bidirectional interactions in conditional variances of police enforcement, traffic violations, and traffic crashes further confirm the necessity to analyze the three simultaneously. The findings of this study are expected to assist the relevant traffic authorities in devising policies and strategies such as optimal police patrol scheduling.

Funding

International Science & Technology Cooperation Program of China (2017YFE0134500)

Science and Technology Commission of Shanghai Municipality (18DZ1200200), and the 111 Project (B17032)

History

School

  • Architecture, Building and Civil Engineering

Published in

Analytic Methods in Accident Research

Volume

28

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Analytic Methods in Accident Research and the definitive published version is available at https://doi.org/10.1016/j.amar.2020.100139

Acceptance date

15/10/2020

Publication date

2020-10-21

Copyright date

2020

ISSN

2213-6657

Language

en

Depositor

Prof Mohammed Quddus. Deposit date: 16 March 2021

Article number

100139