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20-2 Current Status of Transportation Data Analytics and A Pilot Case Study Using Artificial Intelligence (AI)

Project Objectives

The objectives of this research include: (1) reviewing the practices of all DOTs in the New England area and developing a comprehensive inventory of data, data needs, and data analysis practices related to traffic operations; (2) providing recommendations on how to better collect, reduce, achieve, and analyze the data to support DOT decision making and how DOTs should prepare for future transportation data collection and analytics considering emerging sensing and analytical technologies such as connected vehicles, loT, Machine Learning (ML) and AI; and (3) conducting a pilot case study of using AI techniques to automatically analyze specifically-identified operations data and perform a thorough evaluation of its capability to generate useful traffic operation and safety performance measures. This will demonstrate how advanced analytical techniques may extend the capability of traditional traffic sensors and produce additional useful information without expensive infrastructure investment.

Technical Committee Members

  • TC Chair, Susan Klasen: New Hampshire Department of Transportation
  • Ed Block, Connecticut Department of Transportation
  • Colby Fortier-Brown, Maine Department of Transportation
  • Chet Osborne, Massachusetts Department of Transportation
  • Chris Falcos, Massachusetts Department of Transportation
  • Russ Holt, Rhode Island Department of Transportation
  • Ian Degutis, Vermont Agency of Transportation
  • Mandy White, Vermont Agency of Transportation

AC Liaison: Emily Parkany, Vermont Agency of Transportation

Project Coordinator, Kirsten Seeber, CTC & Associates

Primary Sidebar

Key Details

Project Number: 20-2

Start Date: 5/5/2021

Completion Date: 3/31/2023

Status: Active

Principal Investigator: Yuanchang Xie, UMass Lowell

Final Products

Task Reports

  • Task 3 Report 
  • Task 2 Report 
  • Task 1 Report 

Quarterly Reports

  • 2022 Q4 Report (Oct 1 - Dec 31) 
  • 2022 Q3 Report (Jul 1 - Sep 30) 
  • 2022 Q2 Report (Apr 1 - Jun 30) 
  • 2022 Q1 Report (Jan 1 - Mar 31) 
  • 2021 Q4 Report (Oct 1 - Dec 31) 
  • 2021 Q3 Report (Jul 1 - Sep 30) 
  • 2021 Q2 Report (Apr 1 – Jun 30) 

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