Examining morning peak travel behavior of urban residents under mixed conditions of intelligent vehicles

The analysis of travel behavior is the basis for urban road traffic planning, management, and policy formulation. This paper studies the morning peak travel behavior of urban residents when autonomous vehicles and conventional vehicles coexist, based on the behavioral differences between the two in driving and parking. The paper addresses the following three scenarios: (1) Autonomous vehicles only improve road capacity; (2) Autonomous vehicles only reduce travel time; (3) They affect both road capacity and travel time. For each scenario, all possible equilibrium travel modes were considered to derive the system costs under different proportions of intelligent and non-intelligent vehicle users. Under the assumptions of the proposed model, the choice of travel behavior in different travel modes is deduced, as well as the departure rate, critical time point, and travel cost of travelers. It can be seen that increasing the capacity of intelligent vehicles relative to conventional vehicles, reducing the coefficient of travel cost reduction, and increasing the proportion of autonomous vehicles all reduce the total system cost. This research helps to deepen the understanding of morning peak travel modes in a mixed vehicle environment and provides theoretical guidance for transportation management departments to formulate multi-modal travel plans.