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.
Digital Object Identifier
Mineta Transportation Institute URL
Transportation, Traffic Congestion, Data Analysis, Predictive Models, Machine Learning
Data Science | Theory and Algorithms | Transportation
Hongrui Liu and Rahul Ramachandra Shetty. "Analytical Models for Traffic Congestion and Accident Analysis" Mineta Transportation Institute Publications (2021). https://doi.org/10.31979/mti.2021.2102