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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.car-travel more accessible (to persons with disabilities and those not owning cars, for example). Congestion may dramatically worsen, and demand management options will become even more valuable along congested corridors and in urban regions.Charging drivers for the delays or congestion they cause (to those behind them, for example) is a wellknown concept among economists, traffic engineers and transport professionals. Various congestion pricing policies now exist in cities like Singapore, London (UK), Stockholm (Sweden), Milan (Italy), and Gothenburg (Sweden) (Litman, 2018). Most are limited to rather simplistic cordon-based or area-based tolls that do not vary by congestion. Smartphones and/or connected vehicles offer cities, states and nations an opportunity to implement more economically efficient and behaviorally effective strategies, thanks to advanced communication and location capabilities and fast information sharing.This study investigates the effects of different congestion pricing strategies in future scenarios characterized by strong market penetration of AVs and SAVs. Strategies include a travel time-based charge that varies with the Austin, Texas region's overall network condition and a time-varying link-based tollthat reflects marginal delay costs at the link level. The traffic and social welfare impacts of these policies are investigated and compared to those of two much simpler but rather classic strategies: a distance-based toll and a flat facility-based toll (for the most congested 2 to 4% links of the network links). To reflect the technology's uncertain development costs, capabilities and adoption rates, this work estimates two distinctive technology-adoption scenarios: one with relatively high private AV reliance...

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Recently, ridesharing services have grown rapidly. In future, fleets of shared and pooled autonomous vehicles may transform urban mobility. In this paper, we introduce an approach to dynamically simulate these services within a fullstack transport simulation using an insertion-based algorithm. In a first test case, using a taxi data set from Berlin, the potential for shared rides is evaluated using a fleet of vehicles with a capacity between two and four ride requests. The simulation suggests that the overall vehicle kilometers traveled may be reduced by 15-20 % , while travel time increases can be kept at a relatively low level of less than three minutes per person. Additionally, the simulation results suggest in which areas of the city it may be the most rewarding to offer shared services.

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