  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.
