{"id":324234,"date":"2021-08-12T22:21:59","date_gmt":"2021-08-12T19:21:59","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/using-deep-learning-algorithms-to-give-bicyclists-the-green-wave-at-traffic-signals\/"},"modified":"2021-08-12T22:21:59","modified_gmt":"2021-08-12T19:21:59","slug":"using-deep-learning-algorithms-to-give-bicyclists-the-green-wave-at-traffic-signals","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/using-deep-learning-algorithms-to-give-bicyclists-the-green-wave-at-traffic-signals\/","title":{"rendered":"#Using deep learning algorithms to give bicyclists the ‘green wave’ at traffic signals"},"content":{"rendered":"

“#Using deep learning algorithms to give bicyclists the ‘green wave’ at traffic signals<\/strong>”<\/p>\n

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Led by Dr. Stephen Fickas of the University of Oregon (UO), transportation researchers are working to give bicyclists smoother rides by allowing them to communicate with traffic signals via a mobile app<\/a>.<\/p>\n

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.<\/p>\n

“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<\/a> forecasting. The goal is to predict the next phase of an upcoming, actuated traffic signal given a history of its prior phases in time-series format. We’re encouraged by the results,” Fickas said.<\/p>\n

Their latest work builds on two prior projects, also funded by the National Institute for Transportation and Communities, in which Fickas and his team successfully built and deployed a hardware and software product called “Bike Connect” that allowed people on bikes to give hands-free advance information to an upcoming traffic signal, using their speed and direction of travel<\/a> to increase the likelihood the signal would be green upon arrival.<\/p>\n

The project V2X: Bringing Bikes into the Mix, completed in 2018, 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 anactuated signal on the study corridor. The Fast Track: Allowing Bikes To Participate In A Smart-Transportation System<\/a> project, completed in 2019 (featured in the May 2019 ITE Journal<\/i>), developed a real-time display for non-actuated signals showing GLOSA (Green Light Optimized Speed Advisory) information\u2014more often referred to as a “green wave.” While a common technology<\/a> 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.
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The 2021 project builds upon the prior studies:<\/p>\n

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  1. 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.<\/li>\n
  2. 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.<\/li>\n<\/ol>\n

    Incorporating machine learning<\/b><\/p>\n

    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). <\/p>\n

    To measure the effectiveness of each algorithm, they used three metrics: <\/p>\n