Abstract-Autonomous vehicles present new opportunities for addressing traffic congestion through flexible traffic control schemes. This paper explores the possibility that auctions could be run at each intersection to determine the order in which drivers perform conflicting movements. While such a scheme would be infeasible for human drivers, autonomous vehicles are capable of quickly and seamlessly bidding on behalf of human passengers. Specifically, this paper investigates applying autonomous vehicle auctions at traditional intersections using stop signs and traffic signals, as well as to autonomous reservation protocols. This paper also addresses the issue of fairness by having a benevolent system agent bid to maintain a reasonable travel time for drivers with low budgets. An implementation of the mechanism in a microscopic simulator is presented, and experiments on city-scale maps are performed.

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Autonomous vehicles have the potential to improve link and intersection traffic behavior. Computer reaction times may admit reduced following headways and increase capacity and backwards wave speed. The degree of these improvements will depend on the proportion of autonomous vehicles in the network. To model arbitrary shared road scenarios, we develop a multiclass cell transmission model that admits variations in capacity and backwards wave speed in response to class proportions within each cell. The multiclass cell transmission model is shown to be consistent with the hydrodynamic theory. This paper then develops a car following model incorporating driver reaction time to predict capacity and backwards wave speed for multiclass scenarios. For intersection modeling, we adapt the legacy early method for intelligent traffic management (Bento et al., 2013) to general simulation-based dynamic traffic assignment models. Empirical results on a city network show that intersection controls are a major bottleneck in the model, and that legacy early method improves over traffic signals when the autonomous vehicle proportion is sufficiently high.

Autonomous vehicles (AVs) may significantly change traveler behavior and network congestion. Empty repositioning trips allow travelers to avoid parking fees or share the vehicle with other household members. Computer precision and reaction times may also increase road and intersection capacities. AVs are currently being test driven on public roads and may be publicly available within the next two decades; they therefore may be within the span of 20- to 30-year planning analyses. Despite this time scale, AV behavior has yet to be incorporated into planning models. This paper presents a multiclass, four-step model that includes AV repositioning to avoid parking fees (although incurring additional fuel costs) and increases in link capacity as a function of the proportion of AVs on the link. Demand is divided into classes by value of time and AV ownership. Mode choice—parking, repositioning, or transit—is determined through a nested logit model. Traffic assignment is based on a generalized cost function of time, fuel, and tolls. The results on a city network show that transit ridership decreases and the number of personal vehicle trips sharply increases as a result of repositioning. However, increases in link capacity offset the additional congestion. Although link volume increases significantly, only modest decreases in average link speeds are observed.

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