Analytical Models for Traffic Congestion and Accident Analysis

Details

• Creators:
  Liu, Hongrui ; Shetty, Rahul Ramachandra
• Corporate Creators:
  Mineta Transportation Institute ; San Jose State University. College of Business
• Corporate Contributors:
  California State University Transportation Consortium (CSUTC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Transportation
  • Traffic congestion
  • Data analysis
  • Predictive models
  • Machine learning
  • Crash rates
  • Forecasting
  • Logistic regression analysis
• Publication/ Report Number:
  2102
• DOI:
  https://doi.org/10.31979/mti.2021.2102
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Mineta Transportation Institute
• Abstract:
  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.
• Format:
  PDF
• Funding:
  69A3551747127
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/59796/dot_59796_DS1.pdf
• File Type:
  [PDF - 2.51 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:70e770deeb0f9d54c46a847307ae748fbb7875b77586b97b1efd7a37f6a929bf983a233b35dc67003cc5b75b5666f36e9426b6b196b8e1d4e87d5ba69c52d289