Citation

Wang X, Wu X, Abdel-Aty MA, Tremont PJ. Accid. Anal. Prev. 2013; 56: 22-31.

Affiliation

School of Transportation Engineering, Tongji University, Shanghai 201804, China; Road and Traffic Key Laboratory, Ministry of Education, Shanghai 201804, China. Electronic address: wangxs@tongji.edu.cn.

Copyright

(Copyright © 2013, Elsevier Publishing)

DOI

10.1016/j.aap.2013.02.026

PMID

23584537

Abstract

The analysis of road network designs can provide useful information to transportation planners as they seek to improve the safety of road networks. The objectives of this study were to compare and define the effective road network indices and to analyze the relationship between road network structure and traffic safety at the level of the Traffic Analysis Zone (TAZ). One problem in comparing different road networks is establishing criteria that can be used to scale networks in terms of their structures. Based on data from Orange and Hillsborough Counties in Florida, road network structural properties within TAZs were scaled using 3 indices: Closeness Centrality, Betweenness Centrality, and Meshedness Coefficient. The Meshedness Coefficient performed best in capturing the structural features of the road network. Bayesian Conditional Autoregressive (CAR) models were developed to assess the safety of various network configurations as measured by total crashes, crashes on state roads, and crashes on local roads. The models' results showed that crash frequencies on local roads were closely related to factors within the TAZs (e.g., zonal network structure, TAZ population), while crash frequencies on state roads were closely related to the road and traffic features of state roads. For the safety effects of different networks, the Grid type was associated with the highest frequency of crashes, followed by the Mixed type, the Loops & Lollipops type, and the Sparse type. This study shows that it is possible to develop a quantitative scale for structural properties of a road network, and to use that scale to calculate the relationships between network structural properties and safety.

Language: en