Predicting injury risk using Big Data: The case of Metropolitan Melbourne

Ali Soltani; Betsabeh Tanoori; Christopher J Pettit

Predicting injury risk using Big Data: The case of Metropolitan Melbourne

Ali Soltani
, Betsabeh Tanoori
, Christopher J Pettit

Geography & Planning

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This research paper investigates the use of big data to analyse road accidents in the Melbourne metropolitan area. Using the Crash dataset and the Random Forest model, the study sought to determine the relationship between various factors and the proportion of accident victims who were maimed or slain. Results demonstrated that factors such as weekdays, months, and weather conditions can influence the severity of accidents. The study provides policymakers and transportation authorities with valuable insights for devising strategies to enhance road safety and reduce accident risk. Additional research can investigate other potential accident severity factors.

Original language	English
Title of host publication	Australasian Transport Research Forum Conference Proceedings 2023.
Editors	Doina Olaru; Brett Smith; Amanda Eaton
Place of Publication	Canberra, Australia
Pages	1038-1049
Publication status	Published - 31 Dec 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

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https://hdl.handle.net/1959.11/74931

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Cite this

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title = "Predicting injury risk using Big Data: The case of Metropolitan Melbourne",

abstract = "This research paper investigates the use of big data to analyse road accidents in the Melbourne metropolitan area. Using the Crash dataset and the Random Forest model, the study sought to determine the relationship between various factors and the proportion of accident victims who were maimed or slain. Results demonstrated that factors such as weekdays, months, and weather conditions can influence the severity of accidents. The study provides policymakers and transportation authorities with valuable insights for devising strategies to enhance road safety and reduce accident risk. Additional research can investigate other potential accident severity factors.",

author = "Ali Soltani and Betsabeh Tanoori and Pettit, \{Christopher J\}",

year = "2023",

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day = "31",

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pages = "1038--1049",

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Predicting injury risk using Big Data: The case of Metropolitan Melbourne. / Soltani, Ali; Tanoori, Betsabeh; Pettit, Christopher J.
Australasian Transport Research Forum Conference Proceedings 2023.. ed. / Doina Olaru; Brett Smith; Amanda Eaton. Canberra, Australia, 2023. p. 1038-1049.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Predicting injury risk using Big Data

T2 - The case of Metropolitan Melbourne

AU - Soltani, Ali

AU - Tanoori, Betsabeh

AU - Pettit, Christopher J

PY - 2023/12/31

Y1 - 2023/12/31

N2 - This research paper investigates the use of big data to analyse road accidents in the Melbourne metropolitan area. Using the Crash dataset and the Random Forest model, the study sought to determine the relationship between various factors and the proportion of accident victims who were maimed or slain. Results demonstrated that factors such as weekdays, months, and weather conditions can influence the severity of accidents. The study provides policymakers and transportation authorities with valuable insights for devising strategies to enhance road safety and reduce accident risk. Additional research can investigate other potential accident severity factors.

AB - This research paper investigates the use of big data to analyse road accidents in the Melbourne metropolitan area. Using the Crash dataset and the Random Forest model, the study sought to determine the relationship between various factors and the proportion of accident victims who were maimed or slain. Results demonstrated that factors such as weekdays, months, and weather conditions can influence the severity of accidents. The study provides policymakers and transportation authorities with valuable insights for devising strategies to enhance road safety and reduce accident risk. Additional research can investigate other potential accident severity factors.

UR - https://australasiantransportresearchforum.org.au/wp-content/uploads/2022/05/ATRF_2023_Paper_112.pdf

M3 - Conference contribution

SN - 9780646890401

SP - 1038

EP - 1049

BT - Australasian Transport Research Forum Conference Proceedings 2023.

A2 - Eaton, Doina Olaru; Brett Smith; Amanda

CY - Canberra, Australia

ER -

Predicting injury risk using Big Data: The case of Metropolitan Melbourne

Abstract

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