Transport policy optimization with AVs

Autonomous vehicles (AVs, here self-driving and driverless vehicles, SAE (1) levels 4 and 5) are becoming more clearly a reality. Potential services based on AVs, their detailed design for optimal performance, and their consequences for the transport system are of increasing importance. This paper investigates policy combinations for a world with such services. The policy measures investigated are pricing of public transport (through subsidies), pricing of private motorized transport (through taxation or mobility pricing), and the organization of AV services (monopoly vs. oligopoly, with or without ride-sharing). Further, the perception of travel times for autonomous private cars is considered. All combinations of policies (respectively two to three levels each) are implemented in a simulation to determine their synergies. The applied model is the agent-based transportation simulation framework MATSim. The scenario employed for the tests is the agglomeration of Zug, Switzerland. The results suggest that, given the current spatial distribution of the demand and the current transport system, none of the tested AV services is able to improve the system. All tested systems lead to an increase in total vehicle kilometers travelled and a decrease of the average accessibility. It could be shown however, that using cost savings by public transport automation to reduce its price has a positive effect. Therefore, this paper suggests that policy makers are critical when assessing the promises of future transport services. To invest the benefits of automation into an improvement of the existing transport system might be a very good alternative