Modeling and predicting vehicle accident occurrence in Chattanooga, Tennessee.
Machine learning
Neural networks
Traffic accident prediction
Journal
Accident; analysis and prevention
ISSN: 1879-2057
Titre abrégé: Accid Anal Prev
Pays: England
ID NLM: 1254476
Informations de publication
Date de publication:
Jan 2021
Jan 2021
Historique:
received:
15
06
2020
revised:
15
09
2020
accepted:
29
10
2020
pubmed:
11
11
2020
medline:
14
5
2021
entrez:
10
11
2020
Statut:
ppublish
Résumé
Given the ever present threat of vehicular accident occurrence endangering the lives of most people, preventative measures need to be taken to combat vehicle accident occurrence. From dangerous weather to hazardous roadway conditions, there are a high number of factors to consider when studying accident occurrence. To combat this issue, we propose a method using a multilayer perceptron model to predict where accident hotspots are for any given day in the city of Chattanooga, TN. This model analyzes accidents and their associated weather and roadway geometrics to understand the causes of accident occurrence. The model is offered as a live service to local law enforcement and emergency response services to better allocate resources and reduce response times for accident occurrence. Multiple models were made, each having different variables present, and each yielding varying results.
Identifiants
pubmed: 33171397
pii: S0001-4575(20)31680-8
doi: 10.1016/j.aap.2020.105860
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
105860Informations de copyright
Copyright © 2020 Elsevier Ltd. All rights reserved.