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 bike corridor in Eugene, Oregon. Researchers hope to eventually make their app available in other cities.
"The overall goal is to give bicyclists a safer and more efficient use of a city’s signaled intersections. The current project attempts to use two deep-learning algorithms, LSTM and 1D CNN, to tackle time-series forecasting. The goal is to predict the next phase of an upcoming, actuated traffic signal given a history of its prior phases in time-series format. We're encouraged by the results," Fickas said.
Their latest work builds on two prior projects, also funded by the National Institute for Transportation a Communities: in which Fickas and his team successfully built and deployed a hardware and software product called ‘Bike Connect’ which allowed people on bikes to give hands-free advance...Read more