Crowding at egress points and waiting areas in public transport environments during peak periods can potentially impede passenger movements, causing delays to scheduled services. Passenger modelling is a complex task. There are relatively few models able to simulate the complex behavioural characteristics of large volumes of people walking through confined public transport environments such as rail station concourse and platform areas. With the aid of robotic sensing technology however, rich data can be acquired to provide high quality inputs on which passenger behaviour models can be based.

This paper presents a methodology for predicting the preferred route selected by passengers during their egress. Proposed in this paper are a basic principle and a methodology for route choice based on the least effort that a passenger may consume during their travel between destinations. The methodology proposed takes into consideration the movement based passenger and congestion state. We employ the principle of least effort, formulated in terms of a metabolic energy, and congestion states. Our approach uses a new mathematical model for representing effort expended for each path, based on a formulation that minimizes the total amount of metabolic energy used when moving on a trajectory. Using results from an empirical study at Brisbane Central rail station, we show our approach collates well with real patterns of passenger egress. Our discussion concludes with an overview of how our approach could be used by rail service providers to optimise operations and improve customer experience.