Bike big data : how GPS route data collected from smartphones can benefit bicycle planning
Meyer, Joel Loren
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In order to determine the most effective ways to increase ridership in their communities, bicycle planners require quality data on bicycling behavior. Traditional bicycle data collection methods, however, are limited by the large amount of time and expertise required to process and analyze the data, by their inability to provide information at the level of detail needed to understand the complexities of bicycling behavior, and by issues related to sampling bias and poor respondent trip recall. Fortunately, a relatively new method for collecting travel data has emerged that has the potential to provide higher quality and lower cost bicycle data to local planning agencies than has previously been possible with traditional data collection methods: the use of global positioning system (GPS) sensors in smartphones. Researchers at The University of Texas recently evaluated the usefulness of one such smartphone application - “CycleTracks” - to collect bicycle route data. Over 3,600 unique trips were collected from around 300 cyclists in Austin, Texas between May and October, 2011. While they found the CycleTracks app to be useful for collecting a large dataset, to this point there has been only limited analysis of the route data in terms of its usefulness in the planning field. This report will explore the ways in which GPS route data collected from smartphones can address some of the limitations of traditional data collection methods. Austin is used as a case study to show how the GPS route data can be used to plan for network connectivity, to identify barriers in the bicycle network, and to analyze cycling behavior before and after the installation of new facilities. The report finds that despite a number of limitations, smartphone-based GPS data collection has the potential to become an important part of local planning agencies’ regular data collection efforts.