A Machine Learning Method for Predicting Traffic Signal Timing from Probe Vehicle Data

TL;DR

This paper introduces a machine learning approach using XGBoost and neural networks to estimate traffic signal timings from probe vehicle data, enabling better traffic management and routing.

Contribution

The work is the first to apply ML techniques specifically for estimating traffic signal timing parameters from vehicle probe data.

Findings

Cycle length prediction error less than 0.56 seconds

Red time predictions within 7.2 seconds on average

Effective estimation of signal phases from probe data

Abstract

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.