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- W3200117910 abstract "This project focuses on giving bicyclists a safer and more efficient path through a city’s signalized intersections. It builds on a prior NITC project that tested an app for a fixed-time corridor. The goal of this project is to lay the groundwork for extending this earlier app to include actuated signals. Two machine-learning algorithms are introduced that have a good track record with time-series forecasting: LSTM and 1D CNN. The algorithms are tested on data captured from a busy bike corridor on the south end of the University of Oregon campus. A specific actuated intersection is identified on this corridor and real-time data is collected from it. The algorithms are trained on the data and evaluated. The results show that both algorithms can reach 85% accuracy and can predict on a single sample within roughly one second. While these results are encouraging in terms of adding a prediction component to the existing app, a closer look at Precision and Recall is more mixed. A means of computing a Precision-Recall tradeoff is discussed." @default.
- W3200117910 created "2021-09-27" @default.
- W3200117910 creator A5081786900 @default.
- W3200117910 date "2021-01-01" @default.
- W3200117910 modified "2023-09-27" @default.
- W3200117910 title "Green Waves, Machine Learning, and Predictive Analytics: Making Streets Better for People on Bikes" @default.
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- W3200117910 doi "https://doi.org/10.15760/trec.264" @default.
- W3200117910 hasPublicationYear "2021" @default.
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