Document Type

Report

Publication Date

8-2021

Subjects

Machine learning, Transportation -- Safey measures, Bicycle transportation

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.

Description

This is a final report, NITC-RR-1299, from the National Institute for Transportation and Communities (NITC) program of the Transportation Research and Education Center (TREC) at Portland State University.

The project page can be found online at: https://nitc.trec.pdx.edu/research/project/1299.

Project Brief associated with this research. https://archives.pdx.edu/ds/psu/36649

Data Files associated with this research: https://doi.org/10.15760/TREC_datasets.15

DOI

10.15760/trec.264

Persistent Identifier

https://archives.pdx.edu/ds/psu/36648

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