Congestion Pricing in a World of Self-driving Vehicles: an Analysis of 1 Different Strategies in Alternative Future Scenarios

15 The introduction of autonomous (self-driving) and shared autonomous vehicles (AVs and SAVs) will affect 16 travel destinations and distances, mode choices, vehicle-miles traveled, and congestion. Although some 17 congestion reduction may be achieved (thanks to fewer crashes and tighter headways, long-term), car-trip 18 frequencies and VMT are likely to rise significantly in most settings, compromising the benefits of 19 driverless vehicles. Congestion pricing (CP) and road tolls are key tools for moderating demand and 20 incentivizing more socially and environmentally optimal travel choices. 21 This work develops multiple CP and tolling scenarios and investigates their effects on Austin, Texas 22 network conditions and traveler welfare, using the agent-based simulation model MATSim. Results suggest 23 that, while all pricing strategies reduce congestion, their social welfare impacts differ in meaningful ways. 24 More complex and advanced strategies may considerably improve traffic conditions, but do not necessarily 25 improve traveler welfare. The possibility to refund users by reinvesting toll revenues as traveler budgets, 26 plays a salient role in the overall efficiency of each CP strategy. 27 INTRODUCTION 28 Recent advances in autonomous vehicles (AVs) are generating much discussion across academia, industry, 29 and news media. Considerable progress has been in made in AV technologies, thanks to investments by 30 technology companies and auto manufacturers (Muoio, 2017), along with support of public institutions 31 (Kang, 2016). 32 Since driverless passenger vehicles represent a new travel option, many passenger-trips made via existing, 33 traditional modes, like cars, public transit, and bikes, will be replaced by trips in AVs and SAVs. While AV 34 and SAV benefits may accrue in road safety and energy consumption (Fagnant and Kockelman, 2015), 35 network congestion impacts are less well understood and may be quite problematic (Litman, 2017; Wadud 36 et al., 2016). 37 On one hand, automated technologies can ultimately road network performance by reducing traffic crashes 38 (and the delays those entail) and eventually increasing traffic throughput by ensuring tighter headways 39 between vehicles and by making better use of intersections. However, AVs and SAVs will likely increase 40 the number and the distance of motorized trips by making driving “easier” (by removing the driving burden) 41 and car-travel more accessible (to persons with disabilities and those not owning cars, for example). 42 Congestion may dramatically worsen, and demand management options will become even more valuable 43 along congested corridors and in urban regions. 44