Travel Time Prediction for Urban Road Based on Machine Learning: Review and Prospect

Abstract

Travel time prediction is an important issue of the development and application of ITS techniques and Advanced Transportation Management Systems. It is important for transportation managers to develop active traffic management policies, for traffic system users to plan reasonable and efficient travel routes, and for the development of theoretical research on traffic flow theory. However, travel time on urban road segments is characterized by drastic fluctuations and randomness due to signal control at intersections, dynamic and uncertainty of traffic demand and a large number of traffic incidents, which greatly increases the difficulty of accurate and robust prediction. At the same time, with the theoretical innovation in machine learning, the rise of high-performance computing and big data technologies, deep learning has gradually evolved from an unachievable concept to complex machine learning models that can surpass many models to achieve accurate prediction and large-scale application deployment. Therefore, more and more researchers are concerned on deep learning theory and applications. The existing shallow learning prediction methods have the characteristics of vulnerability, shallowness, and finiteness. Many shallow learning models are difficult to accurately model sudden traffic events, and incapable of extracting rich features. They often perform well for predictions within a certain period of time, and therefore cannot be utilized to successfully solved the problem of travel time prediction of urban roads. Deep learning methods, however, are good at multi-level feature extraction and can model time-dependent data precisely. However, deep learning also faces the challenges of great training costs and difficulty in hyper-parameter optimization. This paper deeply analyzes the scientific connotation of urban road segments travel time prediction problem and a series of research methods and their applicability, and finally proposes the future research content and development direction.