Analysis of Spatiotemporal Factors Affecting Traffic Safety Based on Multisource Data Fusion
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 10
Abstract
Traffic state information, road network structure characteristics, and built environment characteristics are factors influencing traffic safety, which will alleviate or aggravate traffic safety problems. This paper analyzes the relationship between these factors and traffic accidents involving either property damage only (PDO) crashes or killed and severe injury (KSI) crashes. The spatiotemporal distribution of the two types of accidents was analyzed. Abundant explanatory variables were extracted from accident data, license plate recognition (LPR) data, OpenStreetMap (OSM) data, and point of interest (POI) data based on complex network methods and information entropy theories. Geographical and temporal weighted regression (GTWR), geographically weighted regression (GWR), and ordinary least squares (OLS) models were used to analyze the influencing factors of traffic safety, respectively. The results demonstrate that the GTWR model performs best in modeling spatiotemporal heterogeneity data. Traffic state information, road network structure, and built environment factors all have significant effects on traffic accidents, and traffic state information have the highest correlation with traffic accidents among all factors. The greater the traffic volume, the more likely are traffic accidents. The strongest correlation is between PDO crashes and traffic state in the morning peak, in the evening peak, and at night. For a road network divided into grids, the more important the intersections in the grid, the greater is the street circuity, and the more chaotic the street direction, the more likely PDO crashes are to occur in the grid. Furthermore, the diversity of land use is positively correlated with traffic accidents in urban areas, whereas the correlation is negative in suburban areas, which reflects the spatial heterogeneity.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Natural Science Foundation of Zhejiang Province (Grant Nos. LGF21E080002, LGF22F030008, and LR23E080002), and the National Natural Science Foundation of China (Grant No. 92046011).
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Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 10 • October 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Mar 15, 2023
Accepted: May 23, 2023
Published online: Jul 26, 2023
Published in print: Oct 1, 2023
Discussion open until: Dec 26, 2023
ASCE Technical Topics:
- Buildings
- Data collection
- Design (by type)
- Engineering fundamentals
- Highway and road management
- Highway and road structures
- Highway transportation
- Infrastructure
- Methodology (by type)
- Models (by type)
- Research methods (by type)
- Structural design
- Structural engineering
- Structural safety
- Structures (by type)
- Traffic accidents
- Traffic analysis
- Traffic engineering
- Traffic management
- Traffic models
- Traffic safety
- Transportation engineering
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