Autonomous intersection management (AIM) (which coordinates intersection movements to avoid signal phases) and dynamic lane reversal (DLR) (which frequently changes lane directions in response to time-varying demand) have previously been proposed for connected autonomous vehicles. A major open question for both is finding the optimal control policy. This paper develops a decentralized max-pressure policy that controls both AIM and DLR based on queue lengths on adjacent links. Using a stochastic queueing model, we prove that the max-pressure policy is also throughput-optimal; any demand that can be stabilized (queue lengths remain bounded) will be stabilized by the max-pressure policy. We show numerically that DLR significantly increases the stability region, particularly for asymmetric demand. Since the stochastic queueing model excludes some realistic aspects of traffic flow, we adapt the max-pressure control for simulation-based dynamic traffic assignment. Results on a city network show significant improvements from max-pressure AIM with and without DLR.
- Autonomous intersection management
- cell transmission model
- dynamic lane reversal
- dynamic traffic assignment
- max-pressure control