Return to search

Agent based micro-simulation of a passenger rail system using customer survey data and an activity based approach

No / Passenger rail overcrowding is fast becoming a problem in major cities worldwide. This problem therefore calls for efficient, cheap and prompt solutions and policies, which would in turn require accurate modelling tools to effectively forecast the impact of transit demand management policies. To do this, we developed an agent-based model of a particular passenger rail system using an activity based simulation approach to predict the impact of public transport demand management pricing strategies. Our agent population was created using a customer/passenger mobility survey dataset. We modelled the temporal flexibility of passengers, based on patterns observed in the departure and arrival behavior of real travelers. Our model was validated using real life passenger count data from the passenger rail transit company, after which we evaluated the use of peak demand management instruments such as ticketing fares strategies, to influence peak demand of a passenger rail transport system. Our results suggest that agent-based simulation is effective in predicting passenger behavior for a transportation system, and can be used in predicting the impact of demand management policies.

Identiferoai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/16761
Date11 August 2018
CreatorsMakinde, O., Neagu, Daniel, Gheorghe, Marian
Source SetsBradford Scholars
LanguageEnglish
Detected LanguageEnglish
TypeConference paper, No full-text in the repository
Rights© Springer Nature Switzerland AG 2019. Reproduced in accordance with the publisher's self-archiving policy. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-97982-3_10.

Page generated in 0.0017 seconds