Spatial analysis of moving-vehicle crashes and fixed-object crashes based on multi-scale geographically weighted regression
Previous researches have demonstrated that traffic crashes in urban areas are geographical events and strongly linked to local characteristics such as road network and land attributes. However, with a significant emphasis on moving-vehicle crashes, the spatial pattern of fixed-object crashes is unclear so far. The difference between these two types of crashes, and whether existing spatial tools such as geographically weighted regression can interpretate the occurrence mode have not been investigated before. To fill this gap, this paper focuses on understanding the spatial features and occurrence of these two types of crash, i.e., moving-vehicle and fixed-object on the city level. Crash data from Dalian, China were aggregated into subdistricts and calibrated with multi-scale geographically weighted regression (MGWR) models. A noticeable but similar clustering pattern was revealed in both types, with spatial overlap of their accident-prone regions. The spatial influence of explanatory variables (road network, geographic, demographic, socio-economic, and land-use variables) was also found mostly similar in both types of crashes. However, fixed-object crash in downtown is more affected by node count, while POI entrance/exit count, especially those in areas with more industrial zones tend to significantly reduce crash risk. In both types of crashes, terrain slope rather than elevation is found to mitigate the crash risk, especially in the downtown area. Compared to traditional Geographically Weighted Regression (GWR) with a fixed bandwidth, the improvement in modeling performance using MGWR highlights the reasonability and benefits to consider the influence scale of each contributing factor in urban spatial analysis of traffic collisions. This study could help transportation authorities identify high-risk regions, understand their contributing factors and take precautions for improving the local traffic safety.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
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Supplemental Notes:
- © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Tang, Xiao
- Bi, Ronghui
- Wang, Zongyao
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0000-0001-9117-9760
- Publication Date: 2023-9
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 107123
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Serial:
- Accident Analysis & Prevention
- Volume: 189
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0001-4575
- Serial URL: http://www.sciencedirect.com/science/journal/00014575
Subject/Index Terms
- TRT Terms: Built environment; Crash analysis; Crash data; Crash risk forecasting; Spatial analysis
- Geographic Terms: China
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01886120
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 28 2023 2:56PM