Exploring the Determinants of Pedestrian Crash Severity Using an AutoML Approach
Publication: International Conference on Transportation and Development 2024
ABSTRACT
This study investigates pedestrian crash severity through automated machine learning (AutoML), offering a streamlined and accessible method for analyzing critical factors. Utilizing a detailed dataset from Utah spanning 2010−2021, the research employs AutoML to assess the effects of various explanatory variables on crash outcomes. The study incorporates SHAP (SHapley Additive exPlanations) to interpret the contributions of individual features in the predictive model, enhancing the understanding of influential factors such as lighting conditions, road type, and weather on pedestrian crash severity. Emphasizing the efficiency and democratization of data-driven methodologies, the paper discusses the benefits of using AutoML in traffic safety analysis. This integration of AutoML with SHAP analysis not only bolsters predictive accuracy but also improves interpretability, offering critical insights into effective pedestrian safety measures. The findings highlight the potential of this approach in advancing the analysis of pedestrian crash severity.
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Information & Authors
Information
Published In
International Conference on Transportation and Development 2024
Pages: 442 - 455
History
Published online: Jun 13, 2024
ASCE Technical Topics:
- Accidents
- Architectural engineering
- Artificial intelligence and machine learning
- Building systems
- Business management
- Computer programming
- Computing in civil engineering
- Infrastructure
- Light (artificial)
- Pedestrians and cyclists
- Practice and Profession
- Public administration
- Public health and safety
- Safety
- Traffic accidents
- Traffic analysis
- Traffic engineering
- Traffic management
- Traffic safety
- Transportation engineering
Authors
Metrics & Citations
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