  Vehicle acceleration and deceleration maneuvers at traffic signals results in
significant fuel and energy consumption levels. Green light optimal speed
advisory systems require reliable estimates of signal switching times to
improve vehicle fuel efficiency. Obtaining these estimates is difficult for
actuated signals where the length of each green indication changes to
accommodate varying traffic conditions. This study details a four-step Long
Short-Term Memory deep learning-based methodology that can be used to provide
reasonable switching time estimates from green to red and vice versa while
being robust to missing data. The four steps are data gathering, data
preparation, machine learning model tuning, and model testing and evaluation.
The input to the models included controller logic, signal timing parameters,
time of day, traffic state from detectors, vehicle actuation data, and
pedestrian actuation data. The methodology is applied and evaluated on data
from an intersection in Northern Virginia. A comparative analysis is conducted
between different loss functions including the mean squared error, mean
absolute error, and mean relative error used in LSTM and a new loss function is
proposed. The results show that while the proposed loss function outperforms
conventional loss functions in terms of overall absolute error values, the
choice of the loss function is dependent on the prediction horizon. In
particular, the proposed loss function is outperformed by the mean relative
error for very short prediction horizons and mean squared error for very long
prediction horizons.
