A Machine Learning Method for Predicting Traffic Signal Timing from
  Probe Vehicle Data
Juliette Ugirumurera Joseph Severino Erik A. Bensen Qichao Wang Jane Macfarlane
Traffic signals play an important role in transportation by enabling traffic
flow management, and ensuring safety at intersections. In addition, knowing the
traffic signal phase and timing data can allow optimal vehicle routing for time
and energy efficiency, eco-driving, and the accurate simulation of signalized
road networks. In this paper, we present a machine learning (ML) method for
estimating traffic signal timing information from vehicle probe data. To the
authors best knowledge, very few works have presented ML techniques for
determining traffic signal timing parameters from vehicle probe data. In this
work, we develop an Extreme Gradient Boosting (XGBoost) model to estimate
signal cycle lengths and a neural network model to determine the corresponding
red times per phase from probe data. The green times are then be derived from
the cycle length and red times. Our results show an error of less than 0.56 sec
for cycle length, and red times predictions within 7.2 sec error on average.