Data-driven inverse learning of passenger preferences in urban public transits

Urban public transit planning is crucial in reducing traffic congestion and enabling green transportation. However, there is no systematic way to integrate passengers' personal preferences in planning public transit routes and schedules so as to achieve high occupancy rates and efficiency gain of ride-sharing. In this paper, we take the first step tp exact passengers' preferences in planning from history public transit data. We propose a data-driven method to construct a Markov decision process model that characterizes the process of passengers making sequential public transit choices, in bus routes, subway lines, and transfer stops/stations. Using the model, we integrate softmax policy iteration into maximum entropy inverse reinforcement learning to infer the passenger's reward function from observed trajectory data. The inferred reward function will enable an urban planner to predict passengers' route planning decisions given some proposed transit plans, for example, opening a new bus route or subway line. Finally, we demonstrate the correctness and accuracy of our modeling and inference methods in a large-scale (three months) passenger-level public transit trajectory data from Shenzhen, China. Our method contributes to smart transportation design and human-centric urban planning.