• Skip to primary navigation
  • Skip to main content
Logo

Work Zone Safety Information Clearinghouse

Library of Resources to Improve Roadway Work Zone Safety for All Roadway Users

  • About
  • Join Listserv
  • Contact
  • Twitter
  • Facebook
  • LinkedIn
  • Work Zone Data
    • At a Glance
    • National & State Traffic Data
    • Work Zone Traffic Crash Trends and Statistics
    • Worker Fatalities and Injuries at Road Construction Sites
  • Topics of Interest
    • Commercial Motor Vehicle Safety
    • Smart Work Zones
    • Transportation Management Plans
    • Accommodating Pedestrians
    • Worker Safety and Welfare
    • Project Coordination in Work Zones
  • Training
    • Flagger
    • Online Courses
    • Toolboxes
    • FHWA Safety Grant Products
    • Certification and
      Accreditation
  • Work Zone Devices
  • Laws, Standards & Policies
    • COVID-19 Guidance
  • Public Awareness
  • Events
  • About
  • Listserv
  • Contact
  • Search
Publication

Artificial Neural Network Model for Estimating Temporal and Spatial Freeway Work Zone Delay Using Probe-Vehicle Data

Author/Presenter: Du, Bo; Chien, Steven; Lee, Joyoung; Spasovic, Lazar; Mouskos, Kyriacos
Abstract:

Highway lane closures due to road reconstruction and the resulting work zones have been a major source of nonrecurring congestion on freeways. It is extremely important to calculate the safety and cost impacts of work zones: the use of new technologies that track drivers and vehicles make that possible. A multilayer feed-forward artificial neural network (ANN) model is developed in this paper to estimate work zone delay by using the probe-vehicle data. The probe data include the travel speeds under normal and work zone conditions. Unlike previous models, the proposed model estimates temporal and spatial delays, which are applied to a real world case study in New Jersey. The work zone data (i.e., starting time, duration, length, and number of closed lanes) were collected on New Jersey freeways in 2014 together with actual probe-vehicle speeds. A comparative analysis was conducted; the results indicate that the ANN model outperforms the traditional deterministic queuing model in terms of the accuracy in estimating travel delays. The ANN model can be used to calculate contractor penalty in terms of cost overruns as well as incentivize a reward schedule in case of early work competition. The model can assist work zone planners in designing optimal start and end time of work zone as function of time of day. In assessing the performance of work zones, the model can assist transportation engineers to better develop and evaluate traffic mitigation and management plans.

Source: Transportation Research Record: Journal of the Transportation Research Board
Volume: 2573
Issue: 1
Publisher: Transportation Research Board
Publication Date: 2016
Full Text URL: Link to URL
Publication Types: Books, Reports, Papers, and Research Articles
Topics: Mathematical Models; Traffic Delays; Work Zones

Copyright © 2023 American Road & Transportation Builders Association (ARTBA). The National Work Zone Safety Information Clearinghouse is a project of the ARTBA Transportation Development Foundation. It is operated in cooperation with the U.S. Federal Highway Administration and Texas A&M Transportation Institute. | Copyright Statement · Privacy Policy · Disclaimer
American Road and Transportation Builders Association Transportation Development Foundation, American Road and Transportation Builders Association U.S. Department of Transportation Federal Highway Administration Texas A&M Transportation Institute