Machine Learning Tools for Predicting the Outcome of Pedestrian Crashes: Preliminary Findings in the Metropolitan City of Rome-Reference-Cited by-同舟云学术

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Machine Learning Tools for Predicting the Outcome of Pedestrian Crashes: Preliminary Findings in the Metropolitan City of Rome

Published:2024 Issue: Volume: Page:116-132
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:en
Short-container-title:

Author:

D’Apuzzo Mauro^ORCID,Cappelli Giuseppe^ORCID,Nardoianni Sofia^ORCID,De Guidi Michele,Nicolosi Vittorio^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-65332-2_8

Reference54 articles.

1. Global Status Report on Road Safety. WHO World Health Organisation, p. X (2015). https://www.who.int/publications/i/item/9789241565066. Accessed Jan 2024

2. Pedestrian Safety. WHO World Health Organization et al. Cap, pp. 1–2 (2013). https://iris.who.int/bitstream/handle/10665/79753/9789241505352_eng.pdf?sequence=1. Accessed Jan 2024

3. Yannis, G., et al.: Vulnerable road users: Cross-cultural perspectives on performance and attitudes 44(3), 220–229 (2020)

4. Sun, Z., et al.: Exploring injury severity of vulnerable road user involved crashes across seasons: a hybrid method integrating random parameter logit model and Bayesian network. ScienceDirect 150, 105682 (2022)

5. Wolfensohn, J., Cherpitel, D. (2002)

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