Transport Policy Optimization with Autonomous Vehicles
place - europe, mode - mass transit, policy
Autonomous vehicles (AVs), policy measures, public transport, pricing
Autonomous vehicles (AVs), here self-driving and driverless vehicles, SAE levels 4 and 5 are becoming more clearly a reality. Potential services based on AVs 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 four levels each) were implemented in a simulation to determine their synergies. The applied model was the agent-based transportation simulation MATSim. The scenario employed for the tests was the agglomeration of Zug, Switzerland. The results suggest that, given the current spatial distribution of the demand and the current transport system, AV systems are only able to reduce travel times at the cost of substantial mode shifts and additional vehicle kilometers driven. Of the tested policy measures, although all showed the expected causality, only the organizational form of the AV service had a statistically significant effect. Therefore, this paper suggests that policy makers should be cautious when confronted with the promises of future transport services. To invest the benefits of automation into an improvement of the existing transport system (e.g., automation of public mass transit or complementing public mass transit with ride-sharing AVs in low-demand areas) might be a good alternative.
Permission to publish the abstract has been given by SAGE, copyright remains with them.
Bösch, P.M., Ciari, F., & Axhausen, K.W. (2018). Transport Policy Optimization with Autonomous Vehicles. Transportation Research Record. https://doi.org/10.1177/0361198118791391