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A Bayesian modeling framework for crash severity effects of active traffic management systems
Zulqarnain H Khattak1, Michael D Fontaine2
1Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States.
Accident; Analysis and Prevention
|July 28, 2020
Summary
Active traffic management (ATM) systems reduce crash severity. Deployments with hard shoulder running (HSR) decreased severe and minor injury likelihoods, while ATM without HSR also showed significant reductions in injury severity.
Area of Science:
- Transportation Engineering
- Traffic Safety
- Road Infrastructure Management
Background:
- Active Traffic Management (ATM) systems dynamically manage congestion using real-time data.
- Previous research indicates some safety benefits of ATM, but its effect on crash injury severity remains unclear.
Purpose of the Study:
- To quantify the impact of Active Traffic Management (ATM) deployment on road crash injury severity.
- To differentiate the effects of ATM with and without hard shoulder running (HSR).
Main Methods:
- Utilized full Bayesian mixed logit models to analyze crash data.
- Quantified the marginal effects of ATM deployment on different injury severity levels.
- Validated model performance using forecast error and temporal transferability.
Main Results:
- ATM deployment was associated with reduced crash severities.
- ATM with hard shoulder running (HSR) reduced severe/moderate injury likelihood by 15.9% and minor injury by 10.1%.
- ATM without HSR reduced severe/moderate injury likelihood by 12.4% and minor injury by 8.33%.
Conclusions:
- ATM systems demonstrably lower injury severity in freeway crashes.
- HSR is a key component in maximizing the injury reduction benefits of ATM.
- Findings provide crucial insights for enhancing freeway safety at critical locations.
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