TY - GEN
T1 - Application of Cognitive Work Analysis to Explore Passenger Behaviour Change Through Provision of Information to Help Relieve Train Overcrowding
AU - Kim, Jisun
AU - Revell, Kirsten
AU - Preston, John
N1 - Publisher Copyright: © 2020 Springer Nature Switzerland AG
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Behaviour change
KW - Cognitive work analysis
KW - Decision making
KW - Human factors
KW - Information
UR - http://www.scopus.com/inward/record.url?scp=85067348390&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007/978-3-030-20503-4_24#chapter-info
U2 - 10.1007/978-3-030-20503-4_24
DO - 10.1007/978-3-030-20503-4_24
M3 - Conference publication
AN - SCOPUS:85067348390
SN - 9783030205027
T3 - Advances in Intelligent Systems and Computing
SP - 261
EP - 271
BT - Advances in Human Factors of Transportation
A2 - Stanton, Neville
PB - Springer
T2 - AHFE International Conference on Human Factors in Transportation, 2019
Y2 - 24 July 2019 through 28 July 2019
ER -