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Application of Cognitive Work Analysis to Explore Passenger Behaviour Change Through Provision of Information to Help Relieve Train Overcrowding

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Advances in Human Factors of Transportation (AHFE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 964))

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It is unrealistic to expect rail passengers to experience a comfortable journey while travelling in crowded trains. Given that passenger behaviour is one of the contributing factors of crowding, understanding and promoting changes in their behaviour would help moderate overcrowding. Therefore, this study aims to develop strategies to encourage passenger behaviour change. Focusing particularly on the provision of train occupancy information, Cognitive Work Analysis (CWA) is applied to gain a systematic understanding about constraints of the behaviour in the rail system environment. Participant observations, staff interview, and online survey data were used to develop an Abstraction Hierarchy (AH), which was validated with two rail subject-matter experts. The output enhance our understanding about passenger behaviour while travelling in crowded conditions, and provide insights about how rail service providers could better assist passengers’ decision making to inform real behaviour change. The AH provides the foundation for how to reduce crowding by supporting passengers’ decision making so they can select less crowded trains or carriages.

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The authors would like to thank all the participants for their valuable opinions and collaboration. We also acknowledge the funder of this project, the Rail Safety and Standards Board for their support.

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Correspondence to Jisun Kim .

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Kim, J., Revell, K., Preston, J. (2020). Application of Cognitive Work Analysis to Explore Passenger Behaviour Change Through Provision of Information to Help Relieve Train Overcrowding. In: Stanton, N. (eds) Advances in Human Factors of Transportation. AHFE 2019. Advances in Intelligent Systems and Computing, vol 964. Springer, Cham.

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  • Print ISBN: 978-3-030-20502-7

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