Vehicle Trajectory Similarity: Models, Methods, and Applications

Abstract

The increasing availability of vehicular trajectory data is at the core of smart mobility solutions. Such data offer us unprecedented information for the development of trajectory data mining-based applications. An essential task of trajectory analysis is the employment of efficient and accurate methods to compare trajectories. This work presents a systematic survey of vehicular trajectory similarity measures and provides a panorama of the research field. First, we show an overview of vehicle trajectory data, including the models and some preprocessing techniques. Then, we give a comprehensive review of methods to compare trajectories and their intrinsic properties. We classify the methods according to the trajectory representation and features such as metricity, computational complexity, and robustness to noise and local time shift. Last, we discuss the applications of vehicular trajectory similarity measures and some open research problems.