Evaluating mobility impacts of construction workzones on Utah transportation system using machine learning techniques

Construction work zones are inevitable parts of daily operations at roadway systems. They have a significant impact on traffic conditions and mobility of roadway systems. The traffic impacts of work zones could significantly vary due to several interacting factors such as work zone factors (work zone location and layout, length of the closure, work zone speed, intensity, and daily active hours), traffic factors (percentage of heavy vehicles, highway speed limit, capacity, mobility, flow, density, congestion, occupancy etc.), Road factors (number of total lanes, number of open lanes, and pavement grade and condition), temporal factors (e.g., year, season, month, weekday, daytime, and darkness), weather conditions (rainy, sunny, and snowy), spatial factors (road lane width, proximity and number of ramps), etc. Utah Department of Transportation (UDOT) is continuously collecting and storing the aforementioned data and is interested in evaluating the impacts of those factors on mobility and traffic conditions of roadway systems within the state of Utah. Such an analysis will help the UDOT personnel to better understand and plan for more efficient work zone operations, select the most effective traffic management systems for work zones, and also assess the hidden costs of construction operations at work zones. To help UDOT to address this problem, the research team proposes a robust deep Recurrent Neural Network (RNN) model, capable of evaluating the impacts of the above-mentioned factors on mobility conditions of Utah roadway systems. Compared to traditional machine learning algorithms, deep RNN models contain robust features enabling them to predict results more efficiently in complex environments such as this project. A comparison between results obtained from implementing a deep RNN model, as well as traditional machine learning techniques (e.g. ANN and decision trees) will reveal the capabilities of the proposed system in accurately predicting the traffic conditions based on various work zone factors.

• Record URL:
  https://nitc.trec.pdx.edu/research/project/1362

Language

• English

Project

• Status: Completed
• Funding: $41753
• Contract Numbers:

  NITC-1362

  69A3551747112

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  Dept. of Civil and Environmental Engineering

  University of Utah
  Salt Lake City, UT  United States 
• Managing Organizations:

  TREC at Portland State University

  1900 SW Fourth Ave, Suite 175
  P.O. Box 751
  Portland, Oregon  United States  97201
• Performing Organizations:

  Dept. of Civil and Environmental Engineering

  University of Utah
  Salt Lake City, UT  United States 
• Principal Investigators:

  Rashidi, Abbas

• Start Date: 20191201
• Expected Completion Date: 20201130
• Actual Completion Date: 20210908
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Impacts; Machine learning; Mobility; Neural networks; Work zone traffic control; Work zones
• Geographic Terms: Utah
• Subject Areas: Construction; Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01723230
• Record Type: Research project
• Source Agency: National Institute for Transportation and Communities
• Contract Numbers: NITC-1362, 69A3551747112
• Files: UTC, RIP
• Created Date: Nov 21 2019 7:35AM