Transportation Infrastructure Flooding: Sensing Water Levels and Clearing and Rerouting Traffic out of Danger

Flooding in urban areas, driven by both precipitation and high tide events, can have a devastating effect on a region’s transportation system and economic viability. In the City of Virginia Beach, the problem is acute as nuisance flooding in heavily populated areas impacts both communities and transportation infrastructure. The critical needs to identify the magnitude of floods are to measure and model precipitation intensity with a short lead time and relate to high tide events to plan proper protective measures for and diversion from problem areas. This study adopts a multi-disciplinary approach (hydrology, regional climate and precipitation forecasting, and transportation engineering) to predict roadway flooding and mitigate travelers’ danger from the flood and delays. The project team will study two flood-prone locations in Virginia Beach. From the hydrology/precipitation perspective, the research addresses flooding due to a complex relationship between tide levels and rainfall events. The project team hypothesizes that a data-driven approach whereby patterns of tidal levels and rainfall intensities and durations that cause flooding can be identified. Then forecasted rainfalls and tide levels can be used to forecast periods when roadways may be flooded. From the transportation perspective, the project team is concerned about two types of drivers: those who are on the road as the flood occurs and those who have not yet entered that particular road and must be re-routed. For the first group, warning and road closures must be provided in time to remove these drivers from the impact area. The amount of time required to clear the link depends on network traffic conditions and potentially other flooded areas. The second group must be re-routed so as not to enter the affected link(s) and place the drivers in danger from flooding. The research project consists of 7 main tasks. In Task 1, rainfall and tidal gauge data will be obtained from the City of Virginia Beach and other organizations and then analyzed using standard data mining approaches to identify relationships and patterns. In Task 2, this data and identified influential factors will be used in conjunction with weather forecasts in independent simulation using the Weather Research and Forecasting (WRF) Model to develop rainfall hyetograph forecasts. This will then be used within the models developed through Task 1 to project if the roadway will flood for the forecasted rainfall event. Task 3 takes the outputs from Tasks 1 and 2 and provides a protocol for communicating predicted flooding events and a decision support tool for the traffic management center (TMC) to put out advisories through variable message signs (VMS) and 511 systems for immediate deployment. In future deployment, when connected vehicles are more common, these advisories will also be sent to travelers, as in Task 6. In Task 4, the project team develops a small to medium sized network around the two study locations that is used in Tasks 5 and 6. In Task 5, the project team conducts microscopic traffic simulations under a variety of scenarios based on the conditions and timing related to key factors identified in Tasks 1 and 2, weather conditions, seasons (including tourism and tidal effects), times of day, and other incidents that would involve a Fire/EMS response. These simulations will provide the clearance time(s) of the soon-to-be-flooded link(s); a distribution of the clearance times will be developed for comparison with the flood warnings from Task 2. Task 6 involves developing routing recommendations for drivers who are en-route but have not yet entered the flooded or soon-to-be-flooded links through a hyperpath generating algorithm while considering load balancing. Finally, in Task 7, the project team addresses uncertainty concerns by evaluating the trade-offs between providing a warning and road closure unnecessarily and failing to issue a warning/road closure when one is needed. Costs associated with this task include property damage and rescue, among others, for failing to issue a warning when it is needed and delay costs (Tasks 5 and 6) when issuing a warning that is not needed.

Language

  • English

Project

  • Status: Completed
  • Funding: $398285
  • Contract Numbers:

    DTRT13-G-UTC33

  • Sponsor Organizations:

    United States Department of Transportation - FHWA - LTAP

    1200 New Jersey Avenue, SE
    Washington, DC    20590

    Virginia Tech Transportation Institute

    3500 Transportation Research Plaza
    Blacksburg, Virginia  United States  24061

    University of Virginia, Charlottesville

    Center for Transportation Studies
    P.O. Box 400742, Thornton Hall, D228
    Charlottesville, VA  United States  22903

    Office of the Assistant Secretary for Research and Technology

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

    Virginia Tech Transportation Institute

    3500 Transportation Research Plaza
    Blacksburg, Virginia  United States  24061

    University of Virginia, Charlottesville

    Center for Transportation Studies
    P.O. Box 400742, Thornton Hall, D228
    Charlottesville, VA  United States  22903
  • Principal Investigators:

    Murray-Tuite, Pamela

    Heaslip, Kevin

    Sridhar, Venkataramana

    Goodall, Jonathan

  • Start Date: 20160510
  • Expected Completion Date: 20170831
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01593658
  • Record Type: Research project
  • Source Agency: Mid-Atlantic Transportation Sustainability Center
  • Contract Numbers: DTRT13-G-UTC33
  • Files: UTC, RiP
  • Created Date: Mar 15 2016 2:39PM