Implications of static and dynamic road pricing strategies in the era of autonomous and shared autonomous vehicles using simulation-based dynamic traffic assignment: The case of Budapest

Abstract

The advent of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs) is expected to improve network performance and increase accessibility. However, the improved accessibility will most likely induce more vehicle miles traveled, which necessitates the use of travel demand management tools like road pricing (RP). This research deploys dynamic traffic assignment for Budapest network using “Visum” to investigate the impact of three RP schemes (static and dynamic) on network performance and social welfare in three proposed future traffic scenarios for the years 2030 and 2050. The 2030 scenario combines conventional cars (CC), AVs, and SAVs, where CC is the dominant private transport mode (Mix-Traffic Scenario), while the two scenarios for 2050 include AVs and SAVs only and are characterized by high adoption of AVs (AV-Focused Scenario) or wide usage of SAVs (SAV-Focused Scenario). The results revealed that the impact of RP schemes differs according to the different penetration rates of AVs and SAVs. Nevertheless, considering the social benefits gained, implementing a dynamic pricing strategy (Link-based Scheme) in the case of AV-Focused and SAV-Focused scenarios shows better outcomes compared to other pricing schemes. Conversely, the static pricing strategies (i.e., Bridge Toll and Distance-based Schemes) outperform dynamic strategies in the Mix-Traffic scenario.