Comprehensive Performance Assessment of Passive Crowdsourcing for Counting Pedestrians and Bikes
Individuals who walk and cycle experience a variety of health and economic benefits while simultaneously benefiting their local environments and communities. It is essential to correctly obtain pedestrian and bicyclist counts for better design and planning of active transportation-related facilities. In recent years, crowdsourcing has seen a rise in popularity due to the multiple advantages relative to traditional methods. Nevertheless, crowdsourced data have been applied in fewer studies, and their reliability and performance relative to other conventional methods are rarely documented. To this end, this research examines the consistency between crowdsourced and traditionally collected count data. Additionally, the research aims to develop the adjustment factor between the crowdsourced and permanent counter data, and to estimate the annual average daily traffic (AADT) data based on hourly volume and other predictor variables such as time, day, weather, land use, and facility type. With some caveats, the results demonstrate that the StreetLight crowdsourcing count data for pedestrians and bicyclists appear to be a promising alternative to the permanent counters.
- Record URL:
- Summary URL:
- Record URL:
-
Corporate Authors:
Mineta Transportation Institute
College of Business
San José State University
San Jose, CA United States 95192-0219State of California
Trustees of the California State University
Long Beach, CA United StatesCalifornia Department of Transportation
Sacramento, CA United States 95819Department of Transportation
1200 New Jersey Avenue, SE
Washington, DC United States 20590 -
Authors:
- Cheng, Wen
-
0000-0001-7225-6169
- Zhang, Yongping
-
0000-0002-5935-3834
- Clay, Edward
-
0000-0002-7096-2323
- Publication Date: 2022-1
Language
- English
Media Info
- Media Type: Digital/other
- Features: Appendices; Figures; Photos; References; Tables;
- Pagination: 64p
Subject/Index Terms
- TRT Terms: Annual average daily traffic; Bicycle counts; Crowdsourcing; Data collection; Pedestrian counts
- Subject Areas: Data and Information Technology; Pedestrians and Bicyclists;
Filing Info
- Accession Number: 01836142
- Record Type: Publication
- Report/Paper Numbers: 22-03, CA-MTI-2025
- Contract Numbers: ZSB12017-SJAUX
- Files: NTL, TRIS, ATRI, USDOT, STATEDOT
- Created Date: Feb 22 2022 10:27AM