Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.00821 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913570282799104 |
|---|---|
| author | Reyya, Shriyan Cheng, Yao |
| author_facet | Reyya, Shriyan Cheng, Yao |
| contents | Many agencies have adopted the FHWA-recommended systemic approach to traffic safety, an essential supplement to the traditional hotspot crash analysis which develops region-wide safety projects based on identified risk factors. However, this approach narrows analysis to specific crash and facility types. This specification causes inefficient use of crash and inventory data as well as non-comprehensive risk evaluation and countermeasure selection for each location. To improve the comprehensiveness of the systemic approach to safety, we develop an enhanced process, ROADFIRST, that allows users to identify potential crash types and contributing factors at any location. As the knowledge base for such a process, crash types and contributing factors are analyzed with respect to features of interest, including both dynamic and static traffic-related features, using Random Forest and analyzed with the SHapley Additive exPlanations (SHAP) analysis. We identify and rank features impacting the likelihood of three sample contributing factors, namely alcohol-impaired driving, distracted driving, and speeding, according to crash and road inventory data from North Carolina, and quantify state-wide road segment risk for each contributing factor. The introduced models and methods serve as a sample for the further development of ROADFIRST by state and local agencies, which benefits the planning of more comprehensive region-wide safety improvement projects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00821 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ROADFIRST: A Comprehensive Enhancement of the Systemic Approach to Safety for Improved Risk Factor Identification and Evaluation Reyya, Shriyan Cheng, Yao Machine Learning Many agencies have adopted the FHWA-recommended systemic approach to traffic safety, an essential supplement to the traditional hotspot crash analysis which develops region-wide safety projects based on identified risk factors. However, this approach narrows analysis to specific crash and facility types. This specification causes inefficient use of crash and inventory data as well as non-comprehensive risk evaluation and countermeasure selection for each location. To improve the comprehensiveness of the systemic approach to safety, we develop an enhanced process, ROADFIRST, that allows users to identify potential crash types and contributing factors at any location. As the knowledge base for such a process, crash types and contributing factors are analyzed with respect to features of interest, including both dynamic and static traffic-related features, using Random Forest and analyzed with the SHapley Additive exPlanations (SHAP) analysis. We identify and rank features impacting the likelihood of three sample contributing factors, namely alcohol-impaired driving, distracted driving, and speeding, according to crash and road inventory data from North Carolina, and quantify state-wide road segment risk for each contributing factor. The introduced models and methods serve as a sample for the further development of ROADFIRST by state and local agencies, which benefits the planning of more comprehensive region-wide safety improvement projects. |
| title | ROADFIRST: A Comprehensive Enhancement of the Systemic Approach to Safety for Improved Risk Factor Identification and Evaluation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.00821 |