Impact of self-parking autonomous vehicles on urban traffic congestion

The advent of autonomous vehicles (AVs) is likely to introduce new mobility experiences for travelers. In particular, AVs would allow travelers to get off at the destinations and then drive themselves elsewhere to park rather than cruise for parking or park at a location with a high parking fee. The self-parking capability is likely to increase the utility of private-owned AVs (PAVs) and make this mobility option more attractive than human-driven vehicles. The present study investigates the dynamics of travelers shifting to PAVs from other transport modes and its negative impact on road traffic congestion. To this end, we propose an agent-based demand model which considers different travel cost components depending on crucial travel attributes such as trip purpose and activity duration. The estimated demand is then fed into a mesoscopic traffic simulation model to examine the resulting road traffic conditions. As charging private vehicles for the congestion they cause is an effective tool for demand management and congestion alleviation, we also integrate a distance-based pricing scheme into the overall modeling framework to investigate its impact on mode choice and transport network performance. A case study is conducted in Melbourne, Australia to demonstrate the proposed methodology. The results indicate that the distance-based pricing scheme can effectively limit the usage of PAVs and reduce traffic congestion, especially in the city center and peripheral suburbs.