Open AccessAnalytical Models for Traffic Congestion and Accident Analysis
Author(s) -
Hongrui Liu,
Rahul Ramachandra Shetty
Publication year - 2021
Language(s) - English
Resource type - Reports
DOI - 10.31979/mti.2021.2102
Subject(s) - decision tree , random forest , logistic regression , transport engineering , road accident , computer science , traffic congestion , road traffic accident , decision tree learning , gradient boosting , road traffic , engineering , machine learning
In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as 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 the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.