Highlights A macro-level model considering spatial correlation was developed for urban Shanghai. Land use, traffic, socio-economic and road network variables were investigated. Arterial length, population and road density are positively correlated to crashes. Average intersection spacing and mean speed are negatively correlated to crashes.
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.
Macro-level traffic safety analysis in Shanghai, China
Accident Analysis and Prevention ; 125 ; 249-256
2019-02-11
8 pages
Article (Journal)
Electronic Resource
English
Macro-level traffic safety analysis in Shanghai, China
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