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

Fickas, Stephen; University of Oregon

doi:10.15760/trec.264

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

[PDF-1.87 MB]

English

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Creators:

Fickas, Stephen
Corporate Creators:

University of Oregon
Corporate Contributors:

National Institute for Transportation and Communities (NITC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program

National Institute for Transportation and Communities (NITC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program Less -
Subject/TRT Terms:

[+]

Actuated Signals Advanced Traveler Information Systems Bicyclists Cyclists Green Wave Phone App Machine Learning Mobile Applications Precision-recall Tradeoff Signalized Intersections Time-series Forecasting
Publication/ Report Number:

NITC-RR-1299
DOI:

https://dx.doi.org/10.15760/trec.264
Resource Type:

Tech Report
Geographical Coverage:

Oregon ; United States

Oregon ; United States Less -
Edition:

Final Report
Corporate Publisher:

National Institute for Transportation and Communities (NITC)
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.
Format:

PDF
Funding:

NITC 1299
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:587100fd82d05400e7156238e0a19b29bdb0cbb62ec0f4f0b8fa0c53fa095b5b
Download URL:

https://rosap.ntl.bts.gov/view/dot/60041/dot_60041_DS1.pdf
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Green Waves, Machine Learning, and Predictive Analytics: Making Streets Better for People on Bikes

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