Dynamic Patrolling Policy for Optimizing Urban Mobility Networks

A dynamic patrolling policy is presented for a fleet of service vehicles operating in response to incident requests in an urban transportation network. We modify an existing adaptive, informative path controller so that the fleet of vehicles is driven to locally optimal service configurations within the environment. These configurations, called patrolling loops, minimize the distance between the instantaneous vehicle position and incident customer request. Our patrolling algorithm is trained using one month of data from a fleet of 16,000 vehicles. This historical dataset is used to learn the parameters required to set up a representative urban mobility model. Using this model we conduct large-scale simulations to show the global stability of the patrolling policy and evaluate the performance of our system by comparing it against a greedy service policy and historical data.