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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.

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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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Autonomous vehicle (AV) technology is maturing, and AVs are being test-driven on public roads. A promising intersection control policy, tile-based reservation (TBR), proposed by Dresner and Stone in 2004, could improve intersection capacity beyond the capabilities of optimized traffic signals. Although TBR has been studied in several microsimulation models, it has yet to be analyzed under user equilibrium behavior. In this study, TBR was modeled in the dynamic traffic assignment to draw on the extensive literature on vehicle routing behaviors. With the proposed model, TBR can be computationally simulated on large city networks, with the goal of solving the traffic assignment problem. TBR also arbitrarily prioritizes vehicle movement, and high-value-of-time travelers may be able to gain priority through intersection auctions, as suggested by the literature. An in-depth study of simple intersection auctions found that much of the benefit (over first-come, first-served prioritization) resulted from the randomizing effect of auctions giving larger queues of vehicles greater shares of the intersection capacity.

Autonomous vehicles admit consideration of novel traffic behaviors such as reservation-based intersection controls and dynamic lane reversal. We present a cell transmission model formulation for dynamic lane reversal. For deterministic demand, we formulate the dynamic lane reversal control problem for a single link as an integer program and derive theoretical results. In reality, demand is not known perfectly at arbitrary times in the future. To address stochastic demand, we present a Markov decision process formulation. Due to the large state size, the Markov decision process is intractable. However, based on theoretical results from the integer program, we derive an effective heuristic. We demonstrate significant improvements over a fixed lane configuration both on a single bottleneck link with varying demands, and on the downtown Austin network.