Crowdsourcing Counts in Pedestrian and Cyclist Data

Cycling and walking mean healthier communities, reduced transportation expenses, and fewer greenhouse gas emissions. Obtaining accurate bicyclist and pedestrian counts is critical to better design active transportation-related facilities and empower people who walk and cycle. The latest Mineta Transportation Institute (MTI) research, Comprehensive Performance Assessment of Passive Crowdsourcing for Counting Pedestrians and Bikes, examines the consistency between crowdsourced and traditionally collected count data.  

In recent years, crowdsourcing—the practice of obtaining information by enlisting the services of a large number of people, typically via the internet—has risen in popularity due to the relative ease of collecting data versus traditional methods. But crowdsourced data has been applied in fewer studies, and the reliability and performance of crowdsourced data relative to other conventional methods are rarely documented. This research examined data accuracy and developed the adjustment factor between the crowdsourced and permanent counter data. The research team then estimated 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. 

Major findings from this pioneering research include:

• StreetLight (SL) count data for pedestrians and bicyclists appear to be a viable alternative to the permanent counters in specific various circumstances where the data outliers were removed. 
• The discrepancy between StreetLight and permanent counter data is much smaller after the SL data are adjusted by applying the developed factors using the different count models. 

“This research can lead to better evaluation of the effects of new infrastructure on pedestrian and bicycle activity; reliable tracking of changes in pedestrian and bicycle activity over time, and enhanced prioritization of pedestrian and bicycle projects,” explain the authors. 

Evaluating the counting performance of emerging technology (SL crowdsourcing as a data collection method) sheds light on the usefulness of these technologies in transportation research and planning. Researching and utilizing effective technology can improve safety and sustainability in California communities. 

ABOUT THE MINETA TRANSPORTATION INSTITUTE
At the Mineta Transportation Institute (MTI) at San Jose State University (SJSU) our mission is to increase mobility for all by improving the safety, efficiency, accessibility, and convenience of our nations’ transportation system. Through research, education, workforce development and technology transfer, we help create a connected world. Founded in 1991, MTI is funded through the US Departments of Transportation and Homeland Security, the California Department of Transportation, and public and private grants, including those made available by the Road Repair and Accountability Act of 2017 (SB1). MTI is affiliated with SJSU’s Lucas College and Graduate School of Business.

ABOUT THE AUTHORS
This research was conducted under the MTI-led California State University Transportation Consortium. Dr. Wen Cheng is a professor from the civil engineering department at Cal Poly Pomona. His specialty areas include statistical modeling and traffic safety. Dr. Yongping Zhang is an associate professor from the civil engineering department at Cal Poly Pomona. His specialty areas include transportation planning models and policy development. Edward Clay is a research assistant from the civil engineering department at Cal Poly Pomona. His specialty areas include statistical modeling, data mining, and computer vision. 

Media Contact:

Irma Garcia, 

MTI Communications and Operations Manager

O: 408-924-7560

E: Irma.garcia@sjsu.edu