Crash severity analysis of vulnerable road users using machine learning.

Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approa...

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Autores principales:	Md Mostafizur Rahman Komol, Md Mahmudul Hasan, Mohammed Elhenawy, Shamsunnahar Yasmin, Mahmoud Masoud, Andry Rakotonirainy
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/90ed1fcca3d44135a108770cb903001d
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spelling	oai:doaj.org-article:90ed1fcca3d44135a108770cb903001d2021-12-02T20:18:37ZCrash severity analysis of vulnerable road users using machine learning.1932-620310.1371/journal.pone.0255828https://doaj.org/article/90ed1fcca3d44135a108770cb903001d2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255828https://doaj.org/toc/1932-6203Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users-pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups-for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for individual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each individual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists.Md Mostafizur Rahman KomolMd Mahmudul HasanMohammed ElhenawyShamsunnahar YasminMahmoud MasoudAndry RakotonirainyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255828 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Md Mostafizur Rahman Komol Md Mahmudul Hasan Mohammed Elhenawy Shamsunnahar Yasmin Mahmoud Masoud Andry Rakotonirainy Crash severity analysis of vulnerable road users using machine learning.
description	Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users-pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups-for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for individual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each individual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists.
format	article
author	Md Mostafizur Rahman Komol Md Mahmudul Hasan Mohammed Elhenawy Shamsunnahar Yasmin Mahmoud Masoud Andry Rakotonirainy
author_facet	Md Mostafizur Rahman Komol Md Mahmudul Hasan Mohammed Elhenawy Shamsunnahar Yasmin Mahmoud Masoud Andry Rakotonirainy
author_sort	Md Mostafizur Rahman Komol
title	Crash severity analysis of vulnerable road users using machine learning.
title_short	Crash severity analysis of vulnerable road users using machine learning.
title_full	Crash severity analysis of vulnerable road users using machine learning.
title_fullStr	Crash severity analysis of vulnerable road users using machine learning.
title_full_unstemmed	Crash severity analysis of vulnerable road users using machine learning.
title_sort	crash severity analysis of vulnerable road users using machine learning.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/90ed1fcca3d44135a108770cb903001d
work_keys_str_mv	AT mdmostafizurrahmankomol crashseverityanalysisofvulnerableroadusersusingmachinelearning AT mdmahmudulhasan crashseverityanalysisofvulnerableroadusersusingmachinelearning AT mohammedelhenawy crashseverityanalysisofvulnerableroadusersusingmachinelearning AT shamsunnaharyasmin crashseverityanalysisofvulnerableroadusersusingmachinelearning AT mahmoudmasoud crashseverityanalysisofvulnerableroadusersusingmachinelearning AT andryrakotonirainy crashseverityanalysisofvulnerableroadusersusingmachinelearning
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Crash severity analysis of vulnerable road users using machine learning.

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