  Spatio-temporal trajectory analytics is at the core of smart mobility
solutions, which offers unprecedented information for diversified applications
such as urban planning, infrastructure development, and vehicular networks.
Trajectory similarity measure, which aims to evaluate the distance between two
trajectories, is a fundamental functionality of trajectory analytics. In this
paper, we propose a comprehensive survey that investigates all the most common
and representative spatio-temporal trajectory measures. First, we provide an
overview of spatio-temporal trajectory measures in terms of three hierarchical
perspectives: Non-learning vs. Learning, Free Space vs. Road Network, and
Standalone vs. Distributed. Next, we present an evaluation benchmark by
designing five real-world transformation scenarios. Based on this benchmark,
extensive experiments are conducted to study the effectiveness,
robustness,nefficiency, and scalability of each measure, which offers
guidelines for trajectory measure selection among multiple techniques and
applications such as trajectory data mining, deep learning, and distributed
processing.
