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University of Oregon
A team at the University of Oregon is 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.
University of Oregon
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 a time-series format. We’re encouraged by the results—Stephen Fickas, Principal Investigator
Their latest work builds on two prior projects, also funded by the National Institute for Transportation a Communities: V2X: Bringing Bikes into the Mix, completed in 2018, and Fast Track: Allowing Bikes To Participate In A Smart-Transportation System, completed in 2019.
University of Oregon
In those first two projects, 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 information to an upcoming traffic signal, using their speed and direction of travel to increase the likelihood the signal would be green upon arrival.
The V2X project focused on giving bicyclists a virtual call button that they could use on their phones. During that project, researchers collected detailed real-time data from an actuated signal on the study corridor. The FastTrack project developed a real-time display for non-actuated signals showing GLOSA (Green Light Optimized Speed Advisory) information–more often referred to as a “green wave”.
While a technology is available to drivers, GLOSA is not widely available for bicyclists. This real-time display (ideally mounted on handlebars for hands-free viewing) offered bicyclists real-time information on whether to slow down, speed up, or maintain speed in order to make a green light.
University of Oregon – The latest project uses ideas from both prior studies:
It uses the data collected from the actuated signal in the first phase to train and test two machine-learning algorithms to forecast the signal phases. It sets the groundwork to extend the FastTrack app to include both non-actuated and actuated signals, as bicyclists are likely to encounter both of these types of infrastructure while riding. Researchers chose to explore two separate machine-learning algorithms.
Both have a good track record with time-series forecasting: One-Dimensional Convolutional Neural Nets (1D CNN for short) and Long Short-Term Memory models (LSTM for short). To measure the effectiveness of each algorithm, they used three metrics: “Precision” is concerned with “when the model does predict that the rider will arrive at a green light, how often is it correct?”
A high Precision score says that the model is not prone to have the rider slamming on brakes, mistakenly told to expect a green. “Recall” asks “for all the actual green lights the rider encountered, how many did the model get correct?” A high Recall score says that the rider is not missing many greens.
University of Oregon – “Accuracy” is the number of correct predictions.
The LSTM and 1D CNN scored nearly identical results on all three metrics. Researchers were able to predict the next phase with roughly 85% accuracy, for each of the time-series forecasting algorithms. Both algorithms were able to predict a single sample within one second—reasonable for inclusion in the FastTrack app, the researchers said. We believe we are in the ballpark of being acceptable in terms of adding a prediction component to our existing FastTrack app
University of Oregon
This would open up green-wave capability for non-fixed-time intersections. Based on what they learned, the researchers’ plans for the next steps are:
Gain access to a dataset with a larger range of days, perhaps an entire season. (Currently, the team has its eyes on “Better Naito” Parkway in Portland, Oregon, a bike-friendly corridor that contains multiple actuated intersections to draw data from.) Typically, more data leads to stronger results when looking at machine-learning algorithms. Move to a multivariate dataset that includes date and time, and perhaps weather as well.
This would not be a huge change to data preparation and may allow a single model that covers all four seasons. The FastTrack app requires a real-time feed from upcoming traffic signals on the bicyclist’s path. Cities with older equipment or with older Traffic Management Systems (TMS) may not be able to provide this feed. However, as cities replace older equipment and bring on a modern TMS, they will be fully capable of using a FastTrack app that is effective with both fixed and actuated intersections, giving their biking community green-wave opportunities.
The researchers have made their code available in a Colab Jupyter notebook
for those interested in replicating the work or exploring further. Resources Fickas, Stephen. Green Waves, Machine Learning, and Predictive Analytics: Making Streets Better for People on Bikes,
NITC-1299. Portland, OR: Transportation Research and Education Center (TREC), 2021.
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