  A realistic long-term microscopic traffic simulator is necessary for
understanding how microscopic changes affect traffic patterns at a larger
scale. Traditional simulators that model human driving behavior with heuristic
rules often fail to achieve accurate simulations due to real-world traffic
complexity. To overcome this challenge, researchers have turned to neural
networks, which are trained through imitation learning from human driver
demonstrations. However, existing learning-based microscopic simulators often
fail to generate stable long-term simulations due to the \textit{covariate
shift} issue. To address this, we propose a history-masked multi-agent
imitation learning method that removes all vehicles' historical trajectory
information and applies perturbation to their current positions during
learning. We apply our approach specifically to the urban traffic simulation
problem and evaluate it on the real-world large-scale pNEUMA dataset, achieving
better short-term microscopic and long-term macroscopic similarity to
real-world data than state-of-the-art baselines.
