Uber Meets Bus: A Simulation Study of Public Ride-hailing Service

To prepare for the future of mobility in areas with widespread infrastructure, it is essential to address the shortcomings of public transit systems that fail to effectively serve their inhabitants’ needs due to a lack of optimization. In this study, we developed a simulation solution-based approach to help decision-makers build an efficient and low-cost public transit system for widespread cities backed by data. This is accomplished by implementing a diala-ride service to increase versatility and scope of rides. The simulation solution strategy utilizes agent-based modeling, which is executed in real-time in response to stochastic ride requests, thereby generating numerous individual dial-a-ride scenarios to identify the most efficient outcome. Using this simulation, cities will be able to develop a plan to prepare for the future of mobility within their city limits. Knoxville Area Transit (KAT) is being used as a case study to gather real-world insight and data to better our understanding of already-in-place systems.