Promoting passenger behaviour change with provision of occupancy information to help moderate train overcrowding: A cognitive work analysis approach
Promoting passenger behaviour change with provision of occupancy information to help moderate train overcrowding: A cognitive work analysis approach
Passengers' travel behaviour is one of the significant factors affecting train overcrowding. Train occupancy information has been introduced as a tool to stimulate passengers' behaviour change to ease in-vehicle crowding. However, there are limitations to this strategy as it often fails to consider other elements in the complex rail system that influence behaviour. This research provides insights to service providers to promote passenger behaviour change by revealing the behavioural constraints in the environment. Cognitive Work Analysis (CWA) was applied to systematically analyse passengers’ behaviour and related constraints in the environment. Specifically, Work Domain Analysis (WDA) and Social Organisation and Cooperation Analysis (SOCA) were conducted and presented in the forms of Abstraction Hierarchy (AH) and Contextual Activity Template (CAT). Results showed that a wide range of informational, navigational and physical support alongside provision of occupancy information could better encourage passengers to select and use less busy carriages and trains. Behaviour change goals are likely to be achieved more effectively when the constraints of the system are better understood.
Cognitive work analysis, Occupancy information, Passenger behaviour change, Train crowding
103801
Kim, Jisun
95e8d9df-8383-4fb5-9806-5b5d064cda37
Madeira-revell, Kirsten
58c66c29-ca9a-4e29-911c-e110f54f6b2b
Preston, John
ef81c42e-c896-4768-92d1-052662037f0b
1 June 2022
Kim, Jisun
95e8d9df-8383-4fb5-9806-5b5d064cda37
Madeira-revell, Kirsten
58c66c29-ca9a-4e29-911c-e110f54f6b2b
Preston, John
ef81c42e-c896-4768-92d1-052662037f0b
Kim, Jisun, Madeira-revell, Kirsten and Preston, John
(2022)
Promoting passenger behaviour change with provision of occupancy information to help moderate train overcrowding: A cognitive work analysis approach.
Applied Ergonomics, 104, , [103801].
(doi:10.1016/j.apergo.2022.103801).
Abstract
Passengers' travel behaviour is one of the significant factors affecting train overcrowding. Train occupancy information has been introduced as a tool to stimulate passengers' behaviour change to ease in-vehicle crowding. However, there are limitations to this strategy as it often fails to consider other elements in the complex rail system that influence behaviour. This research provides insights to service providers to promote passenger behaviour change by revealing the behavioural constraints in the environment. Cognitive Work Analysis (CWA) was applied to systematically analyse passengers’ behaviour and related constraints in the environment. Specifically, Work Domain Analysis (WDA) and Social Organisation and Cooperation Analysis (SOCA) were conducted and presented in the forms of Abstraction Hierarchy (AH) and Contextual Activity Template (CAT). Results showed that a wide range of informational, navigational and physical support alongside provision of occupancy information could better encourage passengers to select and use less busy carriages and trains. Behaviour change goals are likely to be achieved more effectively when the constraints of the system are better understood.
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Promoting passenger behaviour change_a CWA approach_Accepted version
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Accepted/In Press date: 11 May 2022
Published date: 1 June 2022
Additional Information:
Funding Information:
The authors acknowledge the funders of this project, the UK Department for Transport, FutureRailway, and Rail Safety and Standards Board. We also thank Govia Thameslink Railway Ltd. for their excellent support.
Publisher Copyright:
© 2022 Elsevier Ltd
Keywords:
Cognitive work analysis, Occupancy information, Passenger behaviour change, Train crowding
Identifiers
Local EPrints ID: 467544
URI: http://eprints.soton.ac.uk/id/eprint/467544
ISSN: 0003-6870
PURE UUID: 099706d3-94ba-4b86-88b4-80f57d77e74c
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Date deposited: 12 Jul 2022 16:46
Last modified: 17 Mar 2024 07:22
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Author:
Jisun Kim
Author:
Kirsten Madeira-revell
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