Traffic injury severity prediction along with identification of contributory factors using learning vector quantization: a case study of the city of London-Reference-Cited by-同舟云学术

Traffic injury severity prediction along with identification of contributory factors using learning vector quantization: a case study of the city of London

Published:2019-09-24 Issue:10 Volume:1 Page:
ISSN:2523-3963
Container-title:SN Applied Sciences
language:en
Short-container-title:SN Appl. Sci.

Author:

Siamidoudaran Meisam,Iscioglu Ersun,Siamidodaran Mehdi

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Physics and Astronomy,General Engineering,General Environmental Science,General Materials Science,General Chemical Engineering

Link

http://link.springer.com/content/pdf/10.1007/s42452-019-1314-6.pdf

Reference45 articles.

1. World Health Organization (2018) Global status report on road safety 2018. World Health Organization

2. RoSPA (2018) Road safety advice, information and resource. Royal Society for the Prevention of Accidents. https://www.rospa.com/road-safety/. Accessed 22 May 2019

3. DfT (2018) Road safety statement progress report (Moving Britain Ahead). Department for Transport. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/719259/road-safety-statement-progress-report.pdf. Accessed 1 July 2019

4. City of London Corporation (2018) Road danger reduction and active travel plan 2018–2023. Department of the Built Environment City Transportation. https://www.cityoflondon.gov.uk/services/transport-and-streets/road-safety/Documents/road-danger-reduction-plan-2018.pdf. Accessed 28 June 2019

5. Xie Y, Lord D, Zhang Y (2007) Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis. Accid Anal Prev 39(5):922–933

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