Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches.<\/p>\n
<\/p>\n
This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi- actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper pro- poses a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases\u2019 duration. The performance of a Linear Regression (LR), Random Forest (RF), a light gradient-boosting machine (LightGBM), a bidirectional Long-Short-Term-Memory neural network (BiLSTM) and a Temporal Convolutional Network (TCOV) are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that state of the art machine learning models outperform conventional prediction methods.<\/p>\n
Citation: Genser, Alexander, et al. \u00abTime-to-Green predictions for fully-actuated signal control systems with supervised learning.\u00bb IEEE Transactions on Intelligent Transportation Systems (2024).<\/p>\n","protected":false},"excerpt":{"rendered":"
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