Trip Cost Estimation of Connected Autonomous Vehicle Mixed Traffic Flow in a Two-Route Traffic Network

With the advancement of connected autonomous vehicle (CAV) technology, research on future traffic conditions after the popularization of CAVs needs to be resolved urgently. Bounded rationality of human drivers is essential for simulating traffic flow precisely, but few studies focus on the traffic flow simulation considered bounded rationality in CAV mixed traffic flow. In this study, we introduce random bounded rationality into the hybrid feedback strategy (HFS) under CAV mixed traffic flow to explore the impacts of CAV penetration rate on the trip cost of vehicles. First, we investigated the bounded rationality of drivers, and we found that it follows normal contribution. Then, we proposed HFS considering random bounded rationality and the CAV penetration rate to simulate the traffic condition. The numerical results show that the enhancement of the CAV penetration rate could reduce total trip cost. The research could help us to simulate the CAVs mixed traffic flow more precisely and realistically.