Congestion Pricing in a World of Self-driving vehicles: an Analysis of Different Strategies in Alternative Future Scenarios

TL;DR

This paper analyzes various congestion pricing strategies in future scenarios with autonomous vehicles, showing that advanced strategies improve traffic and welfare, with revenue reinvestment enhancing efficiency and acceptance.

Contribution

It develops and evaluates multiple congestion pricing strategies in autonomous vehicle futures using agent-based simulation, highlighting their social welfare impacts and revenue reinvestment effects.

Findings

All strategies reduce congestion.

Advanced strategies outperform simpler ones.

Revenue reinvestment improves public acceptability.

Abstract

The introduction of autonomous (self-driving) and shared autonomous vehicles (AVs and SAVs) will affect travel destinations and distances, mode choice, and congestion. From a traffic perspective, although some congestion reduction may be achieved (thanks to fewer crashes and tighter headways), car-trip frequencies and vehicle miles traveled (VMT) are likely to rise significantly, reducing the benefits of driverless vehicles. Congestion pricing (CP) and road tolls are key tools for moderating demand and incentivizing more socially and environmentally optimal travel choices. This work develops multiple CP and tolling strategies in alternative future scenarios, and investigates their effects on the Austin, Texas network conditions and traveler welfare, using the agent-based simulation model MATSim. Results suggest that, while all pricing strategies reduce congestion, their social welfare impacts differ in meaningful ways. More complex and advanced strategies perform better in terms of traffic conditions and traveler welfare, depending on the development of the mobility landscape of autonomous driving. The possibility to refund users by reinvesting toll revenues as traveler budgets plays a salient role in the overall efficiency of each CP strategy as well as in the public acceptability.