A Machine Learning Method for Predicting Traffic Signal Timing from Probe Vehicle Data
This work addresses the need for accurate traffic signal timing data to optimize vehicle routing and simulation in transportation systems, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of estimating traffic signal timing from vehicle probe data using machine learning, achieving an error of less than 0.56 seconds for cycle length and red times predictions within 7.2 seconds on average.
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.