Congestion Pricing in a World of Self-driving vehicles: an Analysis of Different Strategies in Alternative Future Scenarios
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The introduction of autonomous (self-driving) and shared autonomous vehicles(AVs and SAVs) will affect travel destinations and distances, mode choices,vehicle-miles traveled, and congestion. Although some congestion reduction maybe achieved (thanks to fewer crashes and tighter headways, long-term), car-trip frequencies and VMT are likely to rise significantly in most settings, compromising 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 scenarios and investigates their effects on 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 may considerably improve traffic conditions, but do not necessarily improve traveler welfare. The possibility to refund users by reinvesting toll revenues as traveler budgets, plays a salient role in the overall efficiency of each CP strategy.
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