Optimizing Small-Sized Automated Transit Operations and Its Applications

Autonomous and connected vehicles (CVs) will be available in the near future and will change the paradigm for transportation users and industries, as well as for public transportation (including mass transit, ridesharing and carsharing). Not only can they improve travel safety (e.g., reducing crashes), but also will change our lives and travel patterns. When autonomous vehicles are available, users’ travel behaviors and modal choices will become completely different. Autonomous vehicles likely will result in reductions in car ownership and increases in carsharing and ridesharing. Also, small automated transit vehicles could be utilized to pick up passengers as a feeder for mass transit, such as bus, light rail transit (LRT) and metro. In this research, the optimal operation of small-sized automated transit vehicles will be examined as a flexible ridesharing operation and as a feeder service for mass transit, so that users’ travel behaviors and modal choices in the future can be predicted. In order to do so, optimal automated small transit routing algorithms will be developed. The algorithm also will be adapted to the transit user information system for a flexible transit service.