Combining Virtual Reality and Machine Learning for Intelligent Sustainable Traffic Management

Route choice models form the basis of traffic management systems. High Fidelity models that are based on rapidly evolving contextual conditions can have a huge impact on smart and energy efficient transportation. Existing route choice models are generic and are calibrated using static contextual conditions. These models do not take into account dynamic contextual conditions such as dynamic travel time, accessibility to nearest freeways, traffic incidents, and road closure due to an emergency. As a result, they can only make predictions at an aggregate level and for a generic set of contextual factors. There is a clear need to develop route choice models that take into account local contexts and are closer to ground reality to provide government agencies the ability to make well-informed model-based decisions and policies. Hence, the objective of this study is to develop a novel context-aware framework that combines virtual reality with machine learning to improve understanding about driver’s decision-making with respect to route selection and prediction of roadway congestion in extreme events. This study aims to develop a powerful computation and analytic framework that integrates machine learning-based models with an immersive virtual environment, to improve the predictive power of existing models for traffic routing and resource allocation and deployment of resources (sensors, personnel, etc.). This will be achieved by taking into account contextual factors affecting human interaction with highway infrastructure.

• Supplemental Notes:
  • 18ITSLSU09

Language

• English

Project

• Status: Completed
• Funding: $60000
• Contract Numbers:

  69A3551747106

• Sponsor Organizations:

  Department of Transportation

  Intelligent Transportation Systems Joint Program Office
  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Department of Transportation

  Intelligent Transportation Systems Joint Program Office
  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590

  Transportation Consortium of South-Central States (Tran-SET)

  Louisiana State University
  Baton Rouge, LA  United States  70803
• Project Managers:

  Hassan, Marwa

• Performing Organizations:

  Louisiana State University and A&M College

  202 Himes Hall
  Baton Rouge, LA  United States  70803
• Principal Investigators:

  Mukhopadhyay, Supratik

  Zhu, Yimin

  Gudishala, Ravindra

• Start Date: 20180315
• Expected Completion Date: 20190915
• Actual Completion Date: 20190915
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Drivers; Emergency management; Machine learning; Route choice; Traffic congestion; Virtual reality
• Identifier Terms: Context Sensitive Solutions
• Subject Areas: Data and Information Technology; Energy; Highways; Operations and Traffic Management; Security and Emergencies;

Filing Info

• Accession Number: 01664056
• Record Type: Research project
• Source Agency: Transportation Consortium of South-Central States (Tran-SET)
• Contract Numbers: 69A3551747106
• Files: UTC, RIP
• Created Date: Mar 22 2018 10:24PM