Citation

Wang X, Zhou Q, Yang J, You S, Song Y, Xue M. Accid. Anal. Prev. 2019; 125: 249-256.

Affiliation

Shanghai City, Comprehensive Transportation Planning Institute.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.aap.2019.02.014

PMID

30798150

Abstract

Continuing rapid growth in Shanghai, China, requires traffic safety to be considered at the earliest possible stage of transport planning. Macro-level traffic safety studies have been carried out extensively in many countries, but to date, few have been conducted in China. This study developed a macro-level safety model for 263 traffic analysis zones (TAZs) within the urban area of Shanghai in order to examine the relationship between traffic crash frequency and road network, traffic, socio-economic characteristics, and land use features. To account for the spatial correlations among TAZs, a Bayesian conditional autoregressive negative binomial model was estimated, linking crash frequencies in each TAZ to several independent variables. Modeling results showed that higher crash frequencies are associated with greater populations, road densities, total length of major and minor arterials, trip frequencies, and with shorter intersection spacing. The results from this study can help transportation planners and managers identify the crash contributing factors, and can lead to the development of improved safety planning and management.

Copyright © 2019 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Bayesian conditional autoregressive model; Macro-level safety modeling; Traffic analysis zone; Transportation safety planning