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).
- Record URL:
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Supplemental Notes:
- Received 6 mo. no-cost extension due to COVID-19 Received second 6 mo. no-cost extension due to effects of COVID-19.
Language
- English
Project
- Status: Active
- Funding: $60000
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Contract Numbers:
69A3551747131
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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: 20211231
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Cyclists; Decision making; Machine learning; Traffic data; Travel behavior
- Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; Safety and Human Factors;
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 20 2019 9:19AM