A Data-Driven and Optimal Bus Scheduling Model With Time-Dependent Traffic and Demand

Urban bus companies have collected a tremendous amount of travel data from passengers in the past years. In spite of great value for bus schedule optimization, these data have not been fully exploited. In this paper, we leverage hundreds of millions of bus transaction records, generated when passengers board and alight, to infer time-dependent traffic and customer demand. When the traffic and demand information are available, we build an optimal model to schedule the departure time of each bus service with the objective of minimizing the average waiting time. Experimental results show that compared with the existing bus scheduling system, our model can help reduce the waiting time by a wide margin.