Instead of autonomous vehicles being owned privately, they might instead be used as part of a ridesharing service. Without the need to pay a driver, the cost may be competitive to personal vehicles. Shared autonomous vehicles create many different challenges, such as how to match passengers to vehicles, and how to coordinate vehicle route choice. If the fleet of shared autonomous vehicles is sufficiently large, they may contribute significantly to congestion.

Autonomous vehicles permit hands-free driving, and vehicles could even travel empty. Travelers may be willing to drive more, live farther from work, or avoid parking costs by sending their vehicle on empty trips to park elsewhere. Overall, autonomous vehicles could cause increases in the number and length of trips, resulting in significant increases in travel demand.

Max-pressure intersection control models the network as a Markov decision process; queue lengths are the state, traffic signals are the control variables, and stochasticity comes from entering demand and turning ratios. Rather than solve it numerically (which would be computationally intractable due to the curse of dimensionality), max-pressure control proves that an analytical pressure-based adaptive signal control results in a positive recurrent Markov chain if at all possible. Equivalently, network outflow = inflow for max-pressure control if it could be achieved by any signal timing. Furthermore, max-pressure control is decentralized; each intersection optimizes its own timing based on the surrounding vehicle queues. 

In static flow models, marginal cost tolls have been shown to induce system optimal route choice. For dynamic or non-recurrent congestion, dynamic microtolling could encourage vehicles to avoid congested roads. For instance, raising the toll on an incident link would induce diversions. Computer-aided route guidance with real-time updates of toll changes is necessary to anticipate and respond effectively to dynamic microtolling.

28% of U.S. greenhouse gas emissions were caused by transportation in 2016. New vehicle technologies such as autonomous vehicles and intelligent transportation systems could reduce the environmental impacts in several ways. Technologies that reduce congestion also improve the fuel efficiency. Autonomous vehicles could support alternative fuels such as electric vehicles through empty repositioning to improve access to recharging. Reservation-based intersection controls could reduce acceleration/deceleration cycles at intersections.