Understanding Bicyclists’ Behaviors Through Learning from Big Trip Data

This research aims to understand the behaviors of bicyclists on the road under various scenarios through applying deep learning techniques on trip data collected. Specifically, the project will explore if deep learning models can help identify key factors (e.g., fast-moving vehicles, road conditions and infrastructure, weather conditions) in the sight of the bike riders and automatically learn the relationship between the presence of such factors and the decisions made by the rider (e.g., turns, route choice, speed change, hazard avoidance).

Project

  • Status: Active
  • Funding: $60000
  • Contract Numbers:

    69A3551747131

  • 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:

    University of Iowa, Iowa City

    National Advanced Driving Simulator, 2401 Oakdale Blvd
    Iowa City, IA  United States  52242-5003
  • Performing Organizations:

    University of Iowa, Tippie College of Business

    108 John Pappajohn Business Building
    Iowa City, Iowa  United States  52242
  • Principal Investigators:

    Zhou, Xun

    Hamann, Cara

    Spears, Steven

  • Start Date: 20190701
  • Expected Completion Date: 20201231
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01699001
  • Record Type: Research project
  • Source Agency: Safety Research Using Simulation University Transportation Center (SaferSim)
  • Contract Numbers: 69A3551747131
  • Files: UTC, RiP
  • Created Date: Mar 19 2019 3:59PM