MTI Research Shows Importance of Crowdsourcing in Pedestrian and Cyclist Data

The latest Mineta Transportation Institute (MTI) research, Comprehensive Performance Assessment of Passive Crowdsourcing for Counting Pedestrians and Bikes, examined the consistency between crowdsourced and traditionally collected count data. 

Obtaining accurate bicyclist and pedestrian counts is critical to better design active transportation-related facilities and empower people who walk and cycle, according to MTI.

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.

MTI said crowdsourced data has been applied in fewer studies, and the reliability and performance of crowdsourced data relative to other conventional methods are rarely documented.

The recent research examined data accuracy and developed the adjustment factor between the crowdsourced and permanent counter data. The MTI 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. 

Findings from the 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,” explained the authors. 

MTI said 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.