ClicSim - Real Time Simulation of Passenger Crowding on Trains and at Stations



ClicSim - Real Time Simulation of Passenger Crowding on Trains and at Stations

Authors

N Langdon, C McPherson, SKM Colin Buchanan, UK

Description

Modelling passenger congestion via simulation by integrating a behavioural choice component to represent passengers' decision-making processes and an agent-based simulation to model dynamic congestion levels on trains and in stations.

Abstract

Just as traffic congestion constrains the efficient movement of people and goods on the road system, passenger congestion on the rail network affects the on-time running of trains and operation of stations.

In Australia, population growth, fare policy changes and fuel price instability have contributed to unprecedented growth in public transport patronage in recent years, leading to significant crowding issues in parts of the rail network.

This paper describes a new approach to the forecasting of passenger congestion on rail networks using simulation to predict crowding on trains and in stations. The simulation technique models the choices of individual passengers as they select their departure time, train service, interchange points and walking paths through stations. The position of each passenger is then mapped in time and space to determine when and where crowding may occur.

Unlike other train loading models, the new model uses a behavioural choice component to represent passengers' decision-making processes, and integrates this with an agent-based simulation to model dynamic congestion levels on trains and in stations. This integrated approach allows a great deal of flexibility for planners as the complete journey of each passenger on the rail system is modelled - both the train component and station (walking) component; accurate transfer penalties (that take into account the walking time between platforms) are automatically calculated; the peaks in pedestrian congestion caused by trains arriving simultaneously at a station can be simulated; crowding on trains will cause some retiming of passenger journeys, simulating the peak spreading effect; and outputs from the model are available at the individual passenger level, allowing planners to diagnose why certain trains are overloaded.

The model has been successfully applied in Melbourne and Brisbane to test changes to the train timetable and to estimate when congestion mitigation measures may be required in central city stations. It has also been used to model the effects of congestion due to major sporting and entertainment events and to determine the adequacy of stations to deal with emergency evacuations. This approach will help planners make better use of existing infrastructure before costly capital works are required.

The presentation of this paper would include a description of the technical background to the modelling technique, give examples of practical implementations of the model and provide a demonstration of the model, including visualisations of train loadings and passenger crowding through stations.

Publisher

Association for European Transport