Accidents Analysis and Severity Prediction Using Machine Learning Algorithms

Rahul Ramachandra Shetty and Hongrui Liu ()
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Rahul Ramachandra Shetty: San Jose State University
Hongrui Liu: San Jose State University

A chapter in AI and Analytics for Smart Cities and Service Systems, 2021, pp 173-183 from Springer

Abstract: Abstract Traffic congestion and road accidents have been important public challenges that impose a big burden on society. It is important to understand the factors that contribute to traffic congestions and road accidents so that effective strategies can be implemented to improve the road condition. The analysis on traffic congestion and road accidents is very complex as they not only affect each other but are also affected by many other factors. In this research, we use the US Accidents data from Kaggle that consists of 4.2 million accident records from February 2016 to December 2020 with 49 variables for the study. We propose to use statistical techniques and machine learning algorithms that include Logistic Regression, Tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train a large amount of data to obtain predictive models for traffic congestion and road accidents. The proposed predictive models are expected to be more accurate by incorporating the impact of multiple environmental parameters. The proposed models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.

Keywords: Traffic congestion; Road accidents; Predictive model; Machine learning (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-030-90275-9_15

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DOI: 10.1007/978-3-030-90275-9_15

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