Evaluating the Potential Use of Crowdsourced Bicycle Data in North Carolina
Bicycle volume data can be very helpful in making informed decisions about bike facility investment, bike planning and operations, and can also be used to develop bicycle crash risk models. The methods of data collection can be diverse. The traditional manual counts and travel surveys, comparing to the promising crowdsourced data from third party, are expensive, time consuming, and lack of spatial and temporal coverage. As crowdsourced bicycle data (including Strava, CycleMaps, Moves and Map My Ride) becomes more common and increasingly available, it can greatly help address data gaps and be readily used for efficient and effective decision making as well as performance measures. This research will focus on evaluating the potential use of crowdsourced bike data, and compare it with the traditional bike counting and travel survey data in North Carolina (if available). Using the bike data from the smartphone cycling apps, the predictive models and software of city-wide and/or state-wide bike volumes can be potentially developed to support decision making and performance monitoring.
Language
- English
Project
- Status: Completed
- Funding: $90006
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Contract Numbers:
69A3551747133
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Sponsor Organizations:
Center for Advanced Multimodal Mobility Solutions and Education
University of North Carolina, Charlotte
Charlotte, NC United States 28223Research and Innovative Technology Administration
University Transportation Centers Program
Washington, DC United States 20590 -
Managing Organizations:
University of North Carolina - Charlotte
9201 University City Blvd
Charlotte, North Carolina United States 28223-0001 -
Project Managers:
Fan, Wei
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Performing Organizations:
Center for Advanced Multimodal Mobility Solutions and Education
University of North Carolina, Charlotte
Charlotte, NC United States 28223 -
Principal Investigators:
Fan, Wei
- Start Date: 20171001
- Expected Completion Date: 20190930
- Actual Completion Date: 20190930
Subject/Index Terms
- TRT Terms: Bicycle travel; Crowdsourcing; Data analysis; Data collection; Mobile applications; Origin and destination; Time periods; Traffic volume; Travel behavior; Travel demand
- Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; Planning and Forecasting;
Filing Info
- Accession Number: 01724131
- Record Type: Research project
- Source Agency: Center for Advanced Multimodal Mobility Solutions and Education
- Contract Numbers: 69A3551747133
- Files: UTC, RiP
- Created Date: Nov 29 2019 3:25PM