Mobile-device data, non-motorized traffic monitoring, and estimation of annual average daily bicyclist and pedestrian flows
Principal Investigator
- Raphael Stern, Associate Professor, Civil, Environmental and Geo-Engineering
Summary
People who walk and bike are the most vulnerable road users. However, understanding where they walk and bike requires continual data monitoring. Traditional methods rely on physical sensors in the infrastructure to detect the presence of pedestrians and bicyclists. However, these are expensive to deploy and only detect road users at the specific locations they are deployed. Instead, this study develops methods to use mobile phone based GPS data to estimate the number of bicyclists and pedestrians, and applies this methodology to the Twin Cities Metro area in Minnesota. The developed methodology is able to estimate average pedestrian and bicyclist volumes with relatively high accuracy.Project Details
- Project number: 2022006
- Start date: 06/2021
- Project status: Completed
- Research area: Safety and Mobility
- Topics: Bicycling, Data and modeling, Pedestrian
Research Reports
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Mobile-device data, non-motorized traffic monitoring, and estimation of annual average daily bicyclist and pedestrian flows (2024)
Author(s): Simanta Barman, Michael Levin, Greg Lindsey, Michael Petesch, Suzy Scotty, Raphael Stern
Related Materials
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'Big data' helps count pedestrian and bike traffic in Minnesota
Catalyst (November 7, 2024) -
Crowdsourcing meets transportation planning with bike and pedestrian data counts
Catalyst (July 2, 2024) -
The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review
Journal of Transport and Land Use (February 1, 2024)