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

Fickas, Stephen

doi:10.15760/trec.264

i

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

2021-08-01
By Fickas, Stephen

File Language:

English

Details

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
Subject/TRT Terms:

Actuated Signals Advanced Traveler Information Systems Cyclists Green Wave Phone App Machine Learning Mobile Applications Precision-recall Tradeoff Signalized Intersections Bicyclists 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
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:sha-512:25a276025db643c50367b3176475412ed02b8bce9b25072d14506766f7c5b12a77952d9e056b90fa0b30e77a19b0748fc6f03126fe31ecd35fbc7ac929f54dd5
Download URL:

https://rosap.ntl.bts.gov/view/dot/60041/dot_60041_DS1.pdf
File Type:

[PDF - 1.87 MB ]

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

Dataset

The project builds on a prior app that was designed for Green Light Optimized Speed Advisory (GLOSA). This is more colloquially known as keeping a veh ...

Fickas, Stephen

File Language:

English

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