Region-based prescriptive route guidance for travelers of multiple classes

The performance and complicated interactions of different classes of travelers on regional urban networks are presented and analyzed. A new multi-class extension of a regional dynamic traffic model, the Network Transmission Model is proposed. The classes in question correspond to travelers using autonomous vehicles, conventional vehicles, equipped with Route Guidance and Information Systems, and unequipped vehicles. Each class is represented by a different routing method. Incremental Route Planning, an innovative predictive simulation-based routing method, Proxy Regret Matching, a non-predictive strategic learning-based method and Multinomial Logit-based Routing for 1st, 2nd and 3rd class respectively. All routing methods include a Public Transit Diversion mechanism and are assumed to provide prescriptive route guidance, with pre-trip information dissemination for every departing vehicle. We consider the possibility of non-compliance for conventional vehicles equipped with Route Guidance and Information Systems. We also consider 2 possible scenarios for autonomous vehicles that affect their travel time prediction accuracy. We simulate regional traffic dynamics for simultaneous application of all aforementioned routing methods, employing a market penetration scheme for each class of travelers. We analyze results regarding the overall network performance for various combinations of traveler class market penetration rates and non-compliance rates. We come to the conclusion that autonomous vehicles will not only provide benefits for 1st class travelers, but for all traveler classes on the network.