Selecting the Most Feasible Construction Phasing Plans for Urban Highway Rehabilitation

Approximately one-fifth of the U.S. highway system is under construction, resulting in more than 3,000 construction work zones (CWZ) across cities and states. Since CWZ disrupt traffic flow, daily commuters, and business interests are facing a pressing need to improve mobility around work zones. The primary problem is a lack of standardized methods and analytical tools for proactively assessing the level of mobility disruption that is caused by a CWZ. To tackle this immediate concern, the main objective of this study is to create and test a novel data-driven decision-support model that predicts the level of mobility disruption of a CWZ under arbitrary and user-defined construction and lane closure alternatives. This aim will be achieved by conducting a three-stage methodology that articulates a new spatiotemporal big-data modeling framework where the level of mobility disruption is assessed, and the model’s prediction accuracy fused from a machine-learning algorithm is validated. The central hypothesis is that use of machine-learning techniques will inform the development of reliable mobility indicators for use in selecting the most feasible construction phasing plans. The proposed decision-support system will provide a theoretical basis for comparatively analyzing what-if lane closure scenarios of critical highway projects in urban corridors.

Language

  • English

Project

  • Status: Active
  • Funding: $150000
  • Contract Numbers:

    19ITSLSU07

  • 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:

    Melson, Christopher

  • Performing Organizations:

    Texas A&M University, College Station

    318 Jack K. Williams Administration Building
    College Station, TX  United States  77843
  • Principal Investigators:

    Choi, Kunchee

    Jeong, H. David

    Lee, Yong-Cheol

  • Start Date: 20190815
  • Expected Completion Date: 20210215
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01713225
  • Record Type: Research project
  • Source Agency: Transportation Consortium of South-Central States
  • Contract Numbers: 19ITSLSU07
  • Files: UTC, RiP
  • Created Date: Aug 5 2019 4:07PM