Travel-time prediction methods: A review

Abstract

Near-future Travel-time information is helpful to implement Intelligent Transportation Systems (ITS). Travel-time prediction refers to predicting future travel-time. Researchers have developed various methods to predict travel-time in the past decades. This paper conducts a review focusing on literatures, including techniques proposed recently. These methods are categorized as model-based and data-driven methods. We elaborate two common model-based methods, namely queuing theory and cell transmission model. Data-driven methods are categorized as parametric models (linear regression, autoregressive integrated moving average model and Kalman filter) and non-parametric models (neural network, support vector regression, nearest neighbors and ensemble learning). These methods are compared from data, prediction range and accuracy. In addition, we discuss several solutions to overcome shortcomings of existing methods, and highlight significant future research challenges.