Integrating Crowdsourced Data with Traditionally Collected Data to Enhance Estimation of Bicycle Exposure Measure

The primary goal of the proposed study is to explore opportunities and document limitations associated with integration of crowdsourced cycling data with data collected using traditional methods to accurately estimate the bicyclists’ exposure measure. The benefits of cycling go beyond moving people from origin to destination to improving riders’ health and reduce motor vehicle congestion and greenhouse emissions. Although many transportation agencies have put more efforts on improving cycling environments, limitations on methodologies used to estimate bicyclists exposure (i.e., volume) impact the decision process. Traditional methods for measuring traffic volume have been proven to be challenging and costly. With limited data collected manually or using sensors, a number of researchers have attempted to develop models for estimating bicycle volume. However, such models are less accurate due to limitations in spatial coverage and detail of the data collected manually or using sensors. Crowdsourced data of cycling activities can be a good source of bicycle exposure measure. Data collected using fitness apps have the potential to supplement other data collected through traditional methods to provide spatially detailed data for estimating bicycle exposure. However, comprehensive research on how to integrate crowdsourced data with traditional data is needed. Understanding opportunities and limitations associated with crowdsourced data is necessary to guide integration of the data. This study is designed to examine opportunities for integrating crowdsourced cycling data with data collected using traditional methods to accurately estimate the bicyclists’ exposure measure. To achieve this, a number of tasks will be performed. These will include, literature review, data collection, analysis of results and development of a tool to assist practitioners in estimating bicycle volumes. Data to be collected include, but are not limited to: Crowdsourced data, survey data, traditional cycling data, demographic data, and infrastructure data. With data collected, the research team will: (1) Correlating crowdsourced data with traditional bicycle data, and (2) Identifying socio-demographics, network and infrastructure factors affecting bicycle volume. This project is expected to improve the existing methodologies for estimating bicycle exposure measure by incorporating crowdsourced data. As crowdsourced data is becoming increasingly available, utilizing it in estimating bicycle volume for transportation planning purposes. The bicycle volume data is also important in evaluating bicycle risk as part of safety analyses. It is also applied when prioritizing transportation projects and developing and validating multimodal travel demand models. This study’s objective is directly related to the Transportation Research Center for Livable Communities’ (TRCLC) research focus areas, which include System and network, planning, design, and simulation for improving transportation services; and decision making models and policies that address competing transportation priorities and needs. Accurately estimated bicycle volumes will improve safety analyses and support prioritization of bicycle projects.


  • English


  • Status: Active
  • Funding: $87233
  • Contract Numbers:

    DTRT13-G-UTC60 TRC 17-03

  • 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:

    Transportation Research Center for Livable Communities

    Western Michigan University
    Kalamazoo, MI  United States  49009-5316
  • Project Managers:

    Dunn, Denise

  • Performing Organizations:

    Western Michigan University

    1903 W. Michigan Avenue
    Kalamazoo, MI  United States  49008-5241
  • Principal Investigators:

    Kwigizile, Valerian

    Oh, Jun-Seok

  • Start Date: 20170815
  • Expected Completion Date: 20190630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers
  • Subprogram: Research

Subject/Index Terms

Filing Info

  • Accession Number: 01646245
  • Record Type: Research project
  • Source Agency: Transportation Research Center for Livable Communities
  • Contract Numbers: DTRT13-G-UTC60 TRC 17-03
  • Files: UTC, RiP
  • Created Date: Sep 21 2017 10:01AM