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

Title
Predicting injury risk using Big Data: The case of Metropolitan Melbourne
Publication Date
2023
Author(s)
Soltani, Ali
( author )
OrcID: https://orcid.org/0000-0001-8042-410X
Email: asoltani@une.edu.au
UNE Id une-id:asoltani
Tanoori, Betsabeh
Pettit, Christopher J
Editor
Editor(s): Doina Olaru; Brett Smith; Amanda Eaton
Type of document
Conference Publication
Language
en
Entity Type
Publication
Publisher
Australasian Transport Research Forum (ATRF)
Place of publication
Canberra, Australia
UNE publication id
une:1959.11/74931
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.

Link
Citation
Australasian Transport Research Forum Conference Proceedings 2023., p. 1038-1049
ISBN
9780646890401
Start page
1038
End page
1049

Files:

NameSizeformatDescriptionLink