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Research Highlights

Green Waves, Machine Learning, and Predictive Analytics: Making Streets Better for People on Bikes

Stephen Fickas
Marc Schlossberg
Led by Dr. Stephen Fickas of the University of Oregon (UO), transportation researchers are working to give bicyclists smoother rides by allowing them to communicate with traffic signals via a mobile app. The latest report to come out of this multi-project research effort introduces machine-learning algorithms to work with their mobile app FastTrack. Developed and tested in earlier phases of the project, the app allows cyclists to passively communicate with traffic signals along a busy bi... Read More

Biking and Walking Quality Counts: Using “BikePed Portal” Counts to Develop Data Quality Checks

Nathan McNeil
Kristin Tufte
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 pedest... Read More