Road Accident Severity Prediction - A Comparative Analysis of Machine Learning Algorithms

Sumbal Malik, Hesham El Sayed, Manzoor Ahmed Khan, Muhammad Jalal Khan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Crash severity prediction models enable various agencies to predict the severity of a crash to gain insights into the factors that affect or are associated with crash severity. One of the potential ways to predict the crash severity is to leverage machine learning (ML) algorithms. With the help of accident data, ML algorithms find hidden patterns to predict whether the severity of the crash is fatal, serious, or slight. In this research, we develop a prediction framework and implemented six different machine learning algorithms, namely: Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, Bagging, and AdaBoost to predict the severity of the crash. Experimental results procured for the crash dataset published by the UK shows that Random Forest, Decision Tree, and Bagging significantly outperformed other algorithms in terms of all performance metrics. Furthermore, we analyze the huge; traffic data and extract insightful crash patterns to figure out the significant factors that have a clear effect on road accidents and provide beneficial suggestions regarding this issue. We strongly believe that the proposed prediction framework and the extracted pattern analysis would be helpful in improving the traffic safety system and assist the road authorities to establish proactive strategies to prevent traffic accidents.

Original languageEnglish
Title of host publication2021 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages69-74
Number of pages6
ISBN (Electronic)9781665438414
DOIs
Publication statusPublished - 2021
Event2021 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2021 - Dubai, United Arab Emirates
Duration: Dec 12 2021Dec 16 2021

Publication series

Name2021 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2021

Conference

Conference2021 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2021
Country/TerritoryUnited Arab Emirates
CityDubai
Period12/12/2112/16/21

Keywords

  • crash severity
  • logistic regression
  • machine learning
  • prediction
  • random forest

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems and Management

Fingerprint

Dive into the research topics of 'Road Accident Severity Prediction - A Comparative Analysis of Machine Learning Algorithms'. Together they form a unique fingerprint.

Cite this