Combining Virtual Reality and Machine Learning for Enhancing the Resiliency of Transportation Infrastructure in Extreme Events

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. The 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. The objective of this study is to develop a novel context-aware framework that combines virtual reality with causal machine learning to improve understanding about driver’s decision-making with respect to route selection and prediction of roadway congestion in extreme events. The overarching goal of this project is to develop a powerful computation and analytic framework that integrates causal machine learning-based models with immersive virtual environment to improve the predictive power of existing models for traffic routing and resource allocation and deployment of resources (sensors, personnel, etc.) by taking into account contextual factors affecting human interaction with highway infrastructure. The proposal brings together an interdisciplinary team that will collect time and context-sensitive traffic data and use it to develop and test a new class of context-aware parameterized models for smarter, resilient and energy-efficient traffic management.

• Record URL:
  https://transet.lsu.edu/research-in-progress/

Language

• English

Project

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

  20ITSLSU16

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

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

  Transportation Consortium of South-Central States

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

  Mousa, Momen

• Performing Organizations:

  Louisiana State University

  3660G Patrick F. Taylor Hall
  Civil and Environmental Engineering
  Baton Rouge, LA  United States  70803
• Principal Investigators:

  Mukhopadhyay, Supratik

• Start Date: 20200801
• Expected Completion Date: 20220201
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Decision making; Disasters and emergency operations; Drivers; Machine learning; Route choice; Routing; Traffic control; Virtual reality
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Safety and Human Factors; Security and Emergencies;

Filing Info

• Accession Number: 01757512
• Record Type: Research project
• Source Agency: Transportation Consortium of South-Central States
• Contract Numbers: 20ITSLSU16
• Files: UTC, RiP
• Created Date: Nov 10 2020 8:35AM