  Autonomous vehicles (AVs) have the potential to significantly revolutionize
society by providing a secure and efficient mode of transportation. Recent
years have witnessed notable advancements in autonomous driving perception and
prediction, but the challenge of validating the performance of AVs remains
largely unresolved. Data-driven microscopic traffic simulation has become an
important tool for autonomous driving testing due to 1) availability of
high-fidelity traffic data; 2) its advantages of enabling large-scale testing
and scenario reproducibility; and 3) its potential in reactive and realistic
traffic simulation. However, a comprehensive review of this topic is currently
lacking. This paper aims to fill this gap by summarizing relevant studies. The
primary objective of this paper is to review current research efforts and
provide a futuristic perspective that will benefit future developments in the
field. It introduces the general issues of data-driven traffic simulation and
outlines key concepts and terms. After overviewing traffic simulation, various
datasets and evaluation metrics commonly used are reviewed. The paper then
offers a comprehensive evaluation of imitation learning, reinforcement
learning, deep generative and deep learning methods, summarizing each and
analyzing their advantages and disadvantages in detail. Moreover, it evaluates
the state-of-the-art, existing challenges, and future research directions.
