Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach
Bibliographic Data
| ID | 15480322 |
|---|---|
| Authors | Weixi Ren (Tongji University), Bo Yu (0000-0001-7481-4611, Tongji University, corresponding author), Yuren Chen (0000-0003-4702-9241, Tongji University), Kun Gao (0000-0002-4175-850X, Chalmers University of Technology) |
| Year | 2022 |
| Volume | 19 |
| Issue | 18 |
| Pages | 11358-11358 |
| Publication date | 2022-09-09 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph191811358 |
| PMID | 36141640 |
| OpenAlex | W4295749216 |
| Language | EN |
| References cited | 72 |
Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors' flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions
Bayesian probability · Crash · Environmental health · Injury prevention · Mode (computer interface · Poison control · Transport engineering · Autonomous Vehicle Technology and Safety · Computer Science · Engineering · Human Factors and Ergonomics · Medicine · Traffic and Road Safety · Urban Transport and Accessibility · Artificial Intelligence
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
Practical Bayesian model evaluation using leave-one-out cross-validation and Waic
Exploring spatial variation of the bus stop influence zone with multi-source data
Reductions in Injury Crashes Associated With Red Light Camera Enforcement in Oxnard, California
Psychological roadblocks to the adoption of self-driving vehicles
| Citation velocity | historical |
|---|---|
| Highly cited | No |