Public Transportation Analytics, Planning, and Control
MIT Investigator(s): Jinhua ZHAO (LEAD), Emilio FRAZZOLI
Topic 1: Embedding TNC in Public Transportation Envision a public transportation system that embeds Transport Network Companies such as UBER and Lyft, including pricing integration, ticketing technology integration, customer information integration, and service planning integration.
Topic 2: Individualized Information Provision (IIP) Deep individualization of customer information to nudge travel behavior, in combination with pricing incentives to address peak hour crowding problems.
Topic 3: Demand prediction and planning for large events We apply web mining and machine learning techniques to better predict transit demand for large events (or aggregations of several small ones) in Singapore. Predictions help understand capacity constraints and plan ahead weeks or months in advance. (Collaborator: Prof. Francisco C PEREIRA)
Topic 4: Improving arrival time predictions using confidence intervals We apply conditional quantile regression techniques to determine the reliability of each arrival time prediction. This research also concerns to understand, from the point of view of the user, how such information should be presented and how much it influences decision making. (Collaborator: Prof. Francisco C PEREIRA)