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

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 may be 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. INTRODUCTION Recent advances in autonomous vehicles (AVs) are generating much discussion across academia, industry, and news media. Considerable progress has been in made in AV technologies, thanks to investments by technology companies and auto manufacturers (Muoio, 2017), along with support of public institutions (Kang, 2016). Since driverless passenger vehicles represent a new travel option, many passenger-trips made via existing, traditional modes, like cars, public transit, and bikes, will be replaced by trips in AVs and SAVs. While AV and SAV benefits may accrue in road safety and energy consumption (Fagnant and Kockelman, 2015), network congestion impacts are less well understood and may be quite problematic (Litman, 2017; Wadud et al., 2016). On one hand, automated technologies can ultimately road network performance by reducing traffic crashes (and the delays those entail) and eventually increasing traffic throughput by ensuring tighter headways between vehicles and by making better use of intersections. However, AVs and SAVs will likely increase the number and the distance of motorized trips by making driving “easier” (by removing the driving burden) and car-travel more accessible (to persons with disabilities and those not owning cars, for example).