Collaborative Research Project to coordinate the data from the CRASH Predictive Analytics program between TDOT and TDOSHS

The ultimate goal of the proposed work is to deploy a predictive analytics tool that will enhance the current CRASH Predictive Analytics application for highway safety patrol vehicles deployment. Towards this goal, the objectives of this research are to (i) identify the best practices for data storage, integration, and maintenance infrastructure for predictive modeling, (ii) develop state-of-the-art machine learning algorithms for predicting the risk of highway incidents, and (iii) collaborate with TDOT and THP to identify best practices for model integration with existing programs. The following are expected outcomes of this research project: • the ability to forecast the varying categories of incidents across space and time and allocate emergency response resources accordingly. • Implementing the algorithm with existing operational tools and applications • New innovation in predictive analytics algorithms to reduce response time to incidents

• Record URL:
  https://www.tn.gov/content/dam/tn/tdot/long-range-planning/research/researchsummary/RES2019-02%20CRASH%20-%20One%20Page%20Summary.pdf

Project

• Status: Active
• Funding: $174,998.00
• Contract Numbers:

  RES2019-02

• Sponsor Organizations:

  Tennessee Department of Transportation

  James K. Polk Building
  Fifth and Deaderick Street
  Nashville, TN  United States  37243-0349

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Managing Organizations:

  Tennessee Department of Transportation

  James K. Polk Building
  Fifth and Deaderick Street
  Nashville, TN  United States  37243-0349
• Project Managers:

  Freeze, Brad

• Performing Organizations:

  Vanderbilt University

  Box 96-B
  Nashville, TN  United States  37235
• Principal Investigators:

  Baroud, Hiba

• Start Date: 20181201
• Expected Completion Date: 20201130
• Actual Completion Date: 0
• USDOT Program: Transportation, Planning, Research, and Development

Subject/Index Terms

• TRT Terms: Crash analysis; Highway safety; Incident management; Machine learning; Risk assessment
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01709923
• Record Type: Research project
• Source Agency: Tennessee Department of Transportation
• Contract Numbers: RES2019-02
• Files: RiP
• Created Date: Jun 24 2019 3:57PM