Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach
Joint Authors
Wang, Shaohua
Lu, Yao
Ning, Chen
Yan-yan, Chen
Huang, Jianling
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-06
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This study presents a spatial approach for the macrolevel traffic crashes analysis based on point-of-interest (POI) data and other related data from an open source.
The spatial autoregression is explored by Moran’s I Index with three spatial weight features (i.e., (a) Rook, (b) Queen, and (c) Euclidean distance).
The traditional Ordinary Least Square (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) were developed to describe the spatial correlations among 2,114 Traffic Analysis Zones (TAZs) of Tianjin, one of the four municipalities in China.
Results of the models indicated that the SDM with the Rook spatial weight feature is found to be the optimal spatial model to characterize the relationship of various variables and crashes.
The results show that population density, consumption density, intersection density, and road density have significantly positive influence on traffic crashes, whereas company density, hotel density, and residential density have significant but negative effects in the local TAZ.
The spillover effects coefficient of population density and road density are positive, indicating that the increase of these variables in the surrounding TAZs will lead to the increase of crashes in the target zone.
The impacts of company density and hotel density are just the opposite.
In general, the research findings can help transportation planners and managers better understand the general characteristics of traffic crashes and improve the situation of traffic security.
American Psychological Association (APA)
Wang, Shaohua& Yan-yan, Chen& Huang, Jianling& Ning, Chen& Lu, Yao. 2019. Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1196069
Modern Language Association (MLA)
Wang, Shaohua…[et al.]. Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach. Mathematical Problems in Engineering No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1196069
American Medical Association (AMA)
Wang, Shaohua& Yan-yan, Chen& Huang, Jianling& Ning, Chen& Lu, Yao. Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1196069
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196069