Fine-Grained Passenger Load Prediction inside Metro Network via Smart Card Data

Metro system serves as the backbone for urban public transportation. Accurate passenger load prediction for the metro system plays a crucial role in metro service quality improvement, such as helping operators schedule train timetables and passengers plan their trips. However, existing works can only predict low-grained passenger flows of origin-destination (O-D) paths or inflows/outflows of each station but cannot predict passenger load distribution over the whole metro network. To this end, this paper proposes an end-to-end inference framework, PIPE, for passenger load prediction of every metro segment between two adjacent stations, by only utilizing smart card data. In particular, PIPE includes two modules. The first is the core. It formulates the travel time distribution of each metro segment as a truncated Gaussian distribution. Since there might be several possible routes for certain O-D paths, the population-level travel time distribution of these O-D paths would be a mixture of travel times of different routes. Considering the route preference may change over time, a dynamic truncated Gaussian mixture model is proposed for parameter inference of each truncated Gaussian distribution of each metro segment. The second module serves as the supplement, which compiles a bunch of methods for predicting passenger flows of O-D paths. Built upon them, PIPE is able to predict the travel time that future passengers of each O-D path will take for passing each metro segment and consequently can predict the passenger load of each metro segment in the short future. Numerical studies from Singapore’s metro system demonstrate the efficacy of our method.