A Digital Twin for Visualizing, Evaluating and Maintaining Multimodal Transportation Infrastructure

This research project will develop a digital twin that enables visualizing, evaluating and maintaining multimodal transportation infrastructure. The ultimate goal is to provide an opensource software tool and machine learning-based decision-making approaches that assist the relevant stakeholders in improving their information collection and tracking capabilities, and enhancing the resilience of multimodal transportation infrastructure and beyond. Specifically, we will focus on three research aspects: (1) developing an advanced NetLogo-based computer application (i.e., digital twin) with graphical user interface, which can record the states of locks, dams and ports, predict the water levels of interested sections of a navigable inland waterway based on historical data and spatio-temporal models, and track and simulate the movement and activities of vessels, based on the team’s pilot simulation and visualization tool; (2) characterizing the uncertainty associated with infrastructure status in the digital twin and seeking to model and predict unscheduled maintenance with key factors, such as age and usage of locks and dams, climate and weather conditions, traffic volumes, and types of scheduled maintenance; and (3) enhancing the digital twin with decision support models for vessel routing/rerouting and infrastructure maintenance planning. For a full utilization of the nation’s multimodal transportation infrastructures, the improvement of their transparency to stakeholders and users, and their resilience in response to contingencies, a digital twin with a collection of self-learning capabilities to be designed and tested will assist stakeholders in coordinating their operations and evaluating how short-term and long-term decisions will affect economic outcomes. The research findings to be achieved will be broadly disseminated to researchers and practitioners through research publications and presentations. The team will promote real-world applications of the digital twin by working with MarTREC partners and collaborators.

Language

• English

Project

• Status: Completed
• Funding: $225000
• Contract Numbers:

  69A3551747130

• 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:

  Maritime Transportation Research and Education Center

  University of Arkansas
  Fayetteville, AR  United States  72701
• Performing Organizations:

  University of Arkansas, Fayetteville

  Department of Industrial Engineering
  Fayetteville, AR  United States  72701
• Principal Investigators:

  Liao, Haitao

  Zhang, Shengfan

  Nachtmann, Heather

• Start Date: 20210816
• Expected Completion Date: 20231130
• Actual Completion Date: 20231130
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Data collection; Decision making; Infrastructure; Machine learning; Multimodal transportation; Software; Stakeholders
• Subject Areas: Data and Information Technology; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01782064
• Record Type: Research project
• Source Agency: Maritime Transportation Research and Education Center
• Contract Numbers: 69A3551747130
• Files: UTC, RIP
• Created Date: Sep 21 2021 5:18PM