Cycling and walking are sustainable modes of transportation which improve community livability, but these modes have not been studied with the quantitative rigor applied to motor vehicle travel. This research aims to change that by improving bicycle and pedestrian traffic monitoring data quality. This research will address the question, how can erroneous data best be identified through automated processes? The research team will answer this question by analyzing continuous bicycle and pedestrian count data stored in Bike-Ped Portal, a NITC funded data archive of bicycle and pedestrian count data which currently contains over four million count records from five states. Techniques such as directional distribution, interquartile range checks, continuous annual average daily traffic (AADT) percent difference checks, continuous monthly average daily traffic (MADT) percent difference checks, and number of consecutive zeros have been identified by others and will be explored to identify known errors while avoiding flagging normal variation.
The goal of this research is to create a practical method to quality check bicycle and pedestrian traffic counts. Researchers around the country are struggling with the question of how to check data efficiently without removing correct data. Thus, this research is valuable to researchers and agencies around the nation. Matching funds provide by Oregon Department of Transportation will allow us to implement our findings as part of the data quality assurance process in NITC’s Bike-Ped Portal. Quality data are needed to accurately estimate bicycling and walking, data needed to inform research, practice and policy for livable communities.