Time-to-Green predictions for fully-actuated signal control systems with supervised learning
Abstract
Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. 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 proposes 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' duration. The performance of a Linear Regression (LR), a Random Forest (RF), and a Long-Short-Term-Memory (LSTM) neural network 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 machine learning models outperform conventional prediction methods. Furthermore, tree-based decision models such as the RF perform best with an accuracy that meets requirements for practical applications. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000567460Publication status
publishedJournal / series
arXivPages / Article No.
Publisher
Cornell UniversitySubject
Signal Phase and Timing (SPaT); Time series forecasting; Supervised learning; Actuated traffic signal controlOrganisational unit
08686 - Gruppe Strassenverkehrstechnik
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
Funding
ETH-27 16-1 - SPEED - Rethinking speed limits in urban networks (ETHZ)
Related publications and datasets
Is previous version of: http://hdl.handle.net/20.500.11850/653585
Notes
Funding: NYUAD Research Institute Award CG001.More
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