Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions
Ben Zhai, Yanli Wang, Wenxuan Wang, Bing Wu

TL;DR
This paper develops an optimal variable speed limit control strategy tailored for foggy conditions on freeways, effectively reducing crash risks by 37% while maintaining minimal impact on travel time, using a combination of Bayesian risk prediction, simulation, and genetic algorithms.
Contribution
It introduces a novel VSL control strategy specifically designed for fog conditions, integrating crash risk prediction and optimization techniques to enhance safety and mobility.
Findings
37.15% reduction in crash risk under fog conditions
VSL control increased travel time by only 0.48%
Strategy outperforms VSL controls designed for sunny conditions
Abstract
Fog is a critical external factor that threatens traffic safety on freeways. Variable speed limit (VSL) control can effectively harmonize vehicle speed and improve safety. However, most existing weather-related VSL controllers are limited to adapt to the dynamic traffic environment. This study developed optimal VSL control strategy under fog conditions with fully consideration of factors that affect traffic safety risks. The crash risk under fog conditions was estimated using a crash risk prediction model based on Bayesian logistic regression. The traffic flow with VSL control was simulated by a modified cell transmission model (MCTM). The optimal factors of VSL control were obtained by solving an optimization problem that coordinated safety and mobility with the help of the genetic algorithm. An example of I-405 in California, USA was designed to simulate and evaluate the effects of…
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Taxonomy
TopicsTraffic control and management · Traffic and Road Safety · Traffic Prediction and Management Techniques
MethodsEmirates Airlines Office in Dubai
