Clarifying Rear-End Crash Propensity with Spatial Spillover Effect: Artificial Bayes-GLM Method
Publication: International Conference on Transportation and Development 2024
ABSTRACT
Numerous studies have highlighted the significant influence of adjacent geographic units on rear-end crashes, termed as the spatial spillover effect. This paper introduces an innovative systematic model based on Bayes-GLM to illustrate the propensity for rear-end crashes for improving safety countermeasures on a broader scale. The approach integrates hotspot analysis with K-means clustering into the crash causation model, creating a multi-layered strategy, seeking to improve functionality and scalability by systematically screening potential factors contributing to rear-end crashes. It further identifies relationships between the spillover effect and microscopic impacting factors. The primary advantage of employing such an integrated macro- and microscopic approach lies in the swift identification of critical areas. Moreover, a heuristic analysis of vehicle-to-vehicle interactions, reflecting pre-crash behaviors, is systematically connected to the spillover effect. This linkage enables the integration of a larger scale of crash-influencing factors, thereby enhancing the understanding of the rear-end crash occurrence mechanism.
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Published In
International Conference on Transportation and Development 2024
Pages: 502 - 514
History
Published online: Jun 13, 2024
ASCE Technical Topics:
- Accidents
- Algorithms
- Business management
- Engineering fundamentals
- Infrastructure
- Innovation
- Layered systems
- Mathematics
- Models (by type)
- Practice and Profession
- Public administration
- Public health and safety
- Safety
- Scale models
- Systems engineering
- Systems management
- Traffic accidents
- Traffic engineering
- Traffic management
- Transportation engineering
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