Literature DB >> 30798150

Macro-level traffic safety analysis in Shanghai, China.

Xuesong Wang1, Qingya Zhou2, Junguang Yang2, Shikai You2, Yang Song2, Meigen Xue3.   

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.

Entities:  

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

Mesh:

Year:  2019        PMID: 30798150     DOI: 10.1016/j.aap.2019.02.014

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  2 in total

1.  Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios.

Authors:  Jingwei Cao; Chuanxue Song; Silun Peng; Shixin Song; Xu Zhang; Yulong Shao; Feng Xiao
Journal:  Sensors (Basel)       Date:  2020-06-29       Impact factor: 3.576

2.  Land suitability assessment for supporting transport planning based on carrying capacity and construction demand.

Authors:  Long Li; Gaoru Zhu; Dafang Wu; Honglei Xu; Peifang Ma; Jie Liu; Zhaocheng Li; Yinjie He; Chenghui Li; Pan Wu
Journal:  PLoS One       Date:  2021-02-08       Impact factor: 3.240

  2 in total

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