Dynamic Train Demand Estimation and Passenger Assignment

Understanding real-time train occupancy is a critical problem for public transport management, especially in the service disruption scenarios. To address this problem, this paper proposes a public transport passenger assignment method for estimating the time-dependent train occupancy comprising of a three-step modelling approach. Firstly, we make use of train station tap-on and tap-off information collected by Automated Fare Collection systems to estimate the initial time-dependent Origin-Destination matrix (OD) of the train network. Secondly, we take advantage of real-time train scheduling data to calibrate the initial OD matrix according to travel time, transfer time and waiting times across train lines. Thirdly, the calibrated OD matrix together with train scheduling data are used to generate entire passenger travel trajectories from origins to destinations including all path segments, by following a probabilistic hybrid Markov-driven approach. Lastly, after knowing all passenger trajectories, we further estimate the passenger occupancy for every train in the entire network in a given short time window. The results are applied over the real Sydney train network in Australia, and showcase that the proposed method can accurately quantify time-dependent passenger flows at a station platform level of granularity.