Achieving Energy-Efficient and Travel Time-Optimized Trajectory and Signal Control for CAEVs

Electric Vehicles (EVs) are cost-effective and widely recognized for their significant role in reducing greenhouse gas (GHG) emissions. However, concerns surrounding range anxiety and charge anxiety have hindered their widespread adoption. To address these concerns, traffic engineers have been working on developing control strategies to reduce energy consumption (EC). Unlike traditional gasoline-powered vehicles, EVs experience a notable increase in EC at speeds exceeding 25km/h. Consequently, minimizing EC often results in reduced speed and longer total travel time (TTT). In light of this, our paper proposes a novel trajectory and signal control method that leverages connected and automated vehicle (CAV) technology to realize a tradeoff between EC and TTT. Initially, the approach assumes all vehicles are connected and automated electric vehicles (CAEVs) capable of communication and coordination, to which a cooperative adaptive cruise control (CACC) model was applied. Then, the vehicles were controlled to avoid stops and achieve smoother trajectories at the intersections. Finally, the signal control was integrated to further reduce EC and TTT. The proposed method was evaluated with a simulation conducted in SUMO based on a busy corridor in the City of Edmonton, Canada. The developed method successfully balanced energy and traffic efficiency, reducing both EC and TTT by 14% and 38% respectively. More importantly, the computational burden of our method is considerably lighter compared to existing studies, making it highly suitable for real-time applications. Overall, the results presented in our study showcase the potential of achieving a more efficient and sustainable traffic system with the future existence of CAEVs.