Modelling social identification and helping in evacuation simulation
- Submitting institution
-
University of Sussex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 23611_61931
- Type
- D - Journal article
- DOI
-
10.1016/j.ssci.2016.07.001
- Title of journal
- Safety Science
- Article number
- -
- First page
- 288
- Volume
- 89
- Issue
- -
- ISSN
- 0925-7535
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2016
- URL
-
http://dx.doi.org/10.1016/j.ssci.2016.07.001
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
7
- Research group(s)
-
-
- Citation count
- 20
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Because we had identified the need for including modern understanding of collective behaviour (specifically self-categorisation theory) in models of crowd movement in a preceding highly-cited systematic review [1], this paper for the first time incorporated social identification in behavioural simulations. This allowed us to explain counter-intuitive data on egress times during London bombings. Impact is evidenced through (lead author) Drury’s participation in SPI-B advisory sub-group (human behaviours) of SAGE [2], independent SAGE and Covid taskforces [3]. It was published in a top journal for emergency/disaster safety management. Field-weighted citation impact 2.43 (Scopus).
1. https://doi.org/10.1037/gpr0000032
2. https://www.gov.uk/government/publications/scientific-advisory-group-for-emergencies-sage-coronavirus-covid-19-response-membership/list-of-participants-of-sage-and-related-sub-groups#scientific-pandemic-influenza-group-on-behaviours-spi-b
3. https://profiles.sussex.ac.uk/p92858-john-drury"
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -