Exploring Spatiotemporal Patterns of Expressway Traffic Accidents Based on Density Clustering and Bayesian Network
Bibliographic Data
| ID | 22035009 |
|---|---|
| Authors | Yunfei Zhang (0000-0002-8123-7805, Changsha University of Science and Technology, corresponding author), Fangqi Zhu (Changsha University of Science and Technology), Qiuping Li (0000-0002-5370-8606, Sun Yat-sen University), Zehang Qiu (Changsha University of Science and Technology), Yajun Xie (0009-0001-1803-1820, Changsha University of Science and Technology) |
| Year | 2023 |
| Volume | 12 |
| Issue | 2 |
| Pages | 73 |
| Publication date | 2023-02-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi12020073 |
| OpenAlex | W4321371844 |
| Language | EN |
| Citations received | 3 |
| References cited | 31 |
Exploring spatiotemporal patterns of traffic accidents from historic crash databases is one essential prerequisite for road safety management and traffic risk prevention. Presently, with the emergence of GIS and data mining technologies, numerous geospatial analysis methods have been successfully adopted for traffic accident analysis. As characterized by high driving speeds, diverse vehicle types, and isolated traffic environments, expressways are confronted with more serious accident risks than urban roads. In this paper, we propose a combined method based on improved density clustering and the Bayesian inference network to explore spatiotemporal patterns of expressway accidents. Firstly, the spatiotemporal accident neighborhood is integrated into the DBSCAN clustering algorithm to discover multi-scale expressway black spots. Secondly, the Bayesian network model is separately employed in both local-scale black spots and regional-scale expressway networks to fully explore spatially heterogenous accident factors in various black spots and expressways. The experimental results show that the proposed method can correctly extract spatiotemporal aggregation patterns of multi-scale expressway black spots and meanwhile efficiently discover diverse causal factors for various black spots and expressways, providing a comprehensive analysis of accident prevention and safety management
Bayesian inference · Bayesian network · Bayesian probability · Black spot · Cartography · Cluster analysis · Data mining · DBSCAN · Fuzzy clustering · Geography · Geospatial analysis · Inference · Transport engineering · Computer Science · Engineering · Traffic and Road Safety · Traffic Prediction and Management Techniques · Urban Transport and Accessibility · Artificial Intelligence
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Kernel density estimation and K-means clustering to profile road accident hotspots
Spatiotemporal Analysis of Traffic Accidents Hotspots Based on Geospatial Techniques
Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression
A GIS-based Bayesian approach for analyzing spatial–temporal patterns of intra-city motor vehicle crashes
| Unique citing works | 3 |
|---|---|
| Citations per year | 1,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |