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Evaluation of CACC Vehicles Clustering on Freeway Performance
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Vehicle clustering strategy can harness the full potential of cooperative adaptive cruise control (CACC). Vehicle clustering involves finding nearby CACC equipped vehicles and forming a close spaced platoon with them if certain criteria are met. The aim of this research is to come up with a vehicle clustering strategy and evaluate the impact of this strategy on freeway performance measures such as throughput and emissions. VISSIM is used to simulate CACC equipped vehicles. Only CACC equipped trucks were modelled as one of the main focus of this research was to evaluate emission benefits of CACC system and emission benefits of platooning are more for vehicles with large frontal area. VISSIM’s external driver model application programming interface (API) is used to code the driver model and vehicle clustering strategy for CACC equipped vehicles. The author developed lane change logics and platooning logics for CACC equipped vehicles. VISSIM external driver model API calculated and sent the values of control related parameters such as acceleration to VISSIM at each time step and for all the CACC equipped vehicles in the network. The author evaluated the impact of volume, market penetration rate of CACC, wireless communication, lane restriction policy and desired gap for vehicles in platoon on freeway performance. A regression model was fitted to predict the percent reduction in CO2 based on factors such percent of time spent as a follower in platoon, desired gap and lane restriction policy. The study showed that a dedicated lane for CACC equipped vehicles increases the throughput. In addition, there is a reduction in emissions as compared to the case when vehicles are free to choose a lane. Also, a higher market penetration rate improves emission benefits. It was also seen that good communication between CACC equipped vehicles increases average speed.
Bibeka, Apoorba (2016). Evaluation of CACC Vehicles Clustering on Freeway Performance. Master's thesis, Texas A & M University. Available electronically from