Abstract
The increasing adoption of connected and autonomous vehicles (CAVs) has motivated researchers and practitioners to better understand their impact on traffic congestion. It is well acknowledged that congestion results in increased travel time, fuel consumption/emission, and reduced traffic throughput on roads. Furthermore, causes of congestion such as lane closures and cyber infrastructure failures present various challenges to drivers in reaching their destinations. Strategies such as high occupancy vehicle lanes and fast-track routes have been implemented nationwide to improve the performance of road networks. However, the advent of CAVs has opened up new avenues for research to explore their positive effects on traffic and their contribution to improved network performance. Thus, we developed and validated an agent-based simulation model to capture the interactions of CAVs, regular vehicles, traffic lights, and the road network under both physical and cyber disruption scenarios. Experiments were conducted in two different study sites—highway and urban road networks in the State of Oklahoma. The results indicated that introducing CAVs to the selected road networks improved travel times under different magnitudes of random lane closures and communication failures. Despite a 30% communication failure and 20% random lane closures, CAVs outperformed non-CAVs with better mean travel times. Redundancy mechanisms also allowed CAVs to manage congestion effectively, although CAVs with functional communication still exhibited 5% better performance than those relying solely on redundancy mechanisms.
Published Version
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