Assessing the Impact of Adverse Weather on Performance and Safety of Connected and Autonomous Vehicles

Abuzwidah, Muamer; Elawady, Ahmed; Wang, Ling; Zeiada, Waleed · 2024 · Crossref

DOI: 10.28991/cej-2024-010-09-019

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Summary

This study addresses the critical challenge of ensuring Connected and Autonomous Vehicles (CAVs) operate safely and efficiently under adverse weather conditions, a significant gap in current transportation research. While CAVs promise enhanced safety and efficiency, their performance in scenarios involving reduced visibility and traction, such as rain and fog, remains largely unexplored. The authors aim to bridge this gap by comprehensively assessing CAV impacts on traffic operations and safety across varying weather scenarios, assuming optimal operational conditions free from system errors. The researchers employed microscopic traffic simulation using PTV VISSIM and the Surrogate Safety Assessment Model (SSAM) to evaluate performance metrics. The study focused on a 3-kilometer urban freeway corridor in Sharjah, United Arab Emirates, calibrated using field-collected traffic data from peak periods. The simulation analyzed 21 scenarios covering clear weather, light rain, heavy rain, and fog, across different CAV market penetration rates. The VISSIM model utilized the Wiedemann 99 car-following algorithm, with parameters adjusted to reflect CAV behaviors such as closer following distances and cooperative lane changing. Weather effects were simulated by modifying parameters related to safe distance, following variability, and sensitivity to speed changes. The results demonstrated significant improvements in traffic performance and safety with CAV integration across all weather conditions. In clear weather, average speed increased by 55%, while average delay, number of stops, travel time, and accidents decreased by 50%, 50%, 95%, and 68%, respectively. Under light rain, speed improved by 43%, with reductions in delay, stops, travel time, and accidents of 43%, 56%, 96%, and 74%. Heavy rain conditions saw an 82% increase in average speed and reductions in delay, stops, travel time, and accidents of 62%, 68%, 96%, and 74%. In fog, average speed rose by 32%, while delay, stops, travel time, and accidents decreased by 33%, 47%, 90%, and 83%. These findings indicate that CAVs can substantially mitigate the negative impacts of adverse weather on traffic flow and safety, outperforming conventional vehicles in maintaining efficiency and reducing conflicts. The study highlights the necessity for resilient CAV systems capable of adapting to diverse environmental conditions. By providing empirical evidence of CAV benefits in complex urban settings under varied weather, this research supports the development of robust integration strategies and contributes to sustainable urban mobility planning. It lays a foundation for future work on optimizing CAV algorithms for dynamic environmental changes and broadening the deployment of these technologies.

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• weather rain fog snow