A brief overview of machine learning methods for short-term traffic forecasting and future directions

Abstract

Short-term traffic forecasting is a vital part of intelligent transportation systems. Recently, the combination of unprecedented data availability and the repaid development of machine learning techniques have brought on immense advancement in this field. In this paper, we aim to provide a brief overview of machine learning approaches for short-term traffic forecasting to facilitate research in related fields. We first introduce traffic forecasting and the challenges, and then introduce different approaches for modeling the temporal and/or spatial dependencies. Finally, we discuss several important directions for the future research.