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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A self-driving, fully automated, or “autonomous” vehicle (AV) revolution is imminent, with the potential to eliminate driver costs and driver error, while ushering in an era of shared mobility. Dynamic ride-sharing (DRS), which refers to sharing rides with strangers en route, is growing, with top transportation network companies providing such services. This work uses an agent-based simulation tool called MATSim to simulate travel patterns in Austin, Texas in the presence of personal AVs, and shared AVs (SAVs), with DRS and advanced road-pricing policies in place. Fleet size, pricing, and fare level impacts are analyzed in depth to provide insight into how SAVs may best be introduced to a city or region. Results indicate that the cost-effectiveness of traveling with strangers overcomes inconvenience and privacy issues at moderate-to-low fare levels, with high fares being more detrimental than the base case. A moderately sized Austin, Texas fleet (one SAV for every 25 people) serves nearly 30% of all trips made during the day. The average vehicle occupancy of this fleet was around 1.48 [after including the 12.7% of SAV vehicle-miles traveled (VMT) empty/without passengers], with a 4.5% increase in VMT. This same fleet performs better when road-pricing is enforced in the peak periods (4 h a day), moderating VMT by 2%, increasing SAV demand and in turn fleet-manager revenue. SAVs are able to earn around $100 per SAV per day even after paying tolls, but only at low-fare levels.

Transportation Network Companies (TNCs) have been steadily increasing the share of total trips in metropolitan areas across the world. Micro-modeling TNC operation is essential for large-scale transportation systems simulation. In this study, an agent-based approach for analyzing supply and demand aspects of ride-sourcing operation is done using POLARIS, a high-performance simulation tool. On the demand side, a mode-choice model for the agent and a vehicle-ownership model that informs this choice are developed. On the supply side, TNC vehicle-assignment strategies, pick-up and drop-off operations, and vehicle repositioning are modeled with congestion feedback, an outcome of the mesoscopic traffic simulation. Two case studies of Bloomington and Chicago in Illinois are used to study the framework’s computational speed for large-scale operations and the effect of TNC fleets on a region’s congestion patterns. Simulation results show that a zone-based vehicle-assignment strategy scales better than relying on matching closest vehicles to requests. For large regions like Chicago, large fleets are seen to be detrimental to congestion, especially in a future in which more travelers will use TNCs. From an operational point of view, an efficient relocation strategy is critical for large regions with concentrated demand, but not regulating repositioning can worsen empty travel and, consequently, congestion. The TNC simulation framework developed in this study is of special interest to cities and regions, since it can be used to model both demand and supply aspects for large regions at scale, and in reasonably low computational time.

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