Self-interested autonomous vehicle cruise-induced congestion: Exploring system-optimal cruise pricing strategies

Autonomous vehicles (AVs) may exacerbate traffic congestion due to zero-occupant cruise to avoid parking and save parking fees. Congestion pricing strategies are considered as important solutions to reduce traffic congestion. However, there is still insufficient research on the congestion pricing strategy on zero-occupancy AVs, which we refer to as the cruise pricing strategy. Therefore, this paper explores and optimizes the impact of cruise pricing strategies on AVs cruise-induced traffic congestion. A Multi-Agent-Based Model (ABM) is constructed to simulate the parking and cruising behavior of zero-occupancy AVs and evaluate the impact of different cruise pricing strategies on traffic systems. Previous research on AVs cruise pricing strategies has largely overlooked actual travel patterns and road network conditions. In this paper, we use real traffic network and trip data in Hangzhou as a real-world case study, finding that AVs push the system into congestion in a completely self-interested way to save individual travel costs, which results in an additional 63.0% increase in the average travel time for travelers and a 58.7% increase in the total network Vehicle Kilometers Traveled (VKT). Compared to baseline pricing methods, the proposed link-based cruise pricing strategy can effectively minimize AVs cruise-induced traffic congestion while maintaining a balance between individual benefits and system-optimal goals.