Time-to-Green predictions for fully-actuated signal control systems with supervised learning
This work addresses the problem of efficient traffic flow prediction for urban planners and transportation systems, but it is incremental as it builds on existing methods for semi-actuated systems.
The paper tackled the challenge of predicting signal phase timings for fully-actuated traffic control systems by proposing a time series prediction framework using aggregated traffic data, and found that machine learning models, especially Random Forest, outperformed conventional methods with accuracy meeting practical requirements.
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.