An Agent-Based Simulation Approach for Urban Road Pricing Considering the Integration of Autonomous Vehicles With Public Transport

Abstract

The way in which autonomous transport will be adopted is likely to determine the net social benefits delivered by the technology and the sustainability of the transport system. Autonomous vehicles (AVs) will change travel behavior due to reduction in the effort needed for humans to drive a vehicle, the need for them to find a parking space, and the costs related to vehicle operation. The AVs’ benefits are likely to increase their adoption compared to conventional human-driven vehicles, possibly leading to more vehicle kilometers travelled (VKT) and consequently weakening their benefits in large cities particularly if they are used in competition with public transport (PT). This paper evaluates the interplay between AVs and PT, and how road network pricing can be used to influence behavioral changes when personal autonomous vehicles (PAVs) are highly available. An agent-based demand model framework is proposed to estimate the mode share of PAVs and PT based on their perceived travelling costs on a real transport network in Melbourne, Australia. The modelling results suggest that convenient and affordable PAVs could compete with traditional PT and reduce overall PT patronage by up to 10%. However, through considering road network pricing schemes, the role of PAVs could be shifted from competing with PT to a complementary first-and-last-mile service that increases PT share by almost 17%. The results also show that road pricing policies can be used as effective interventions to manage PAV operations by reducing empty vehicle trips by 20%.