A Simulation Modeling Framework With Autonomous Vehicle Region-Based Routing And Public Transit Diversion Integration

In this paper, we present a simulation modeling framework that can accommodate multiple classes of travelers and integrates several distinct features, which in turn can be associated with each of the traveler classes, thus providing flexibility and a so-called bird's-eye view to any potential user. Concretely, we integrate into the multi-class region-based dynamic traffic model, called multi-class Network Transmission Model (McNTM), several features, including a public transit diversion component, as well as routing methods associated with different traveler classes. Three distinct traveler classes are defined, the 1st class of travelers equipped with autonomous vehicles, the 2nd traveler class comprising of RGIS-equipped, conventional vehicles and the 3rd traveler class comprising of unequipped, conventional vehicles. Certain assumptions are made for each traveler class. The gain in overall performance for the case where 1st and 2nd class travelers are present in the system, ranges from 0.78% - 23.43%. Region-based routing methods employed by the 1st and 2nd class respectively, not only benefit overall network performance, but with their respective market penetration rates exceeding certain thresholds, can prove beneficial to the individual performance of other traveler classes.