  With the development of artificial intelligence techniques, transportation
system optimization is evolving from traditional methods relying on expert
experience to simulation and learning-based decision optimization methods.
Learning-based optimization methods require extensive interaction with highly
realistic microscopic traffic simulators for optimization. However, existing
microscopic traffic simulators are computationally inefficient in large-scale
scenarios and therefore significantly reduce the efficiency of the data
sampling process of optimization algorithms. In addition, the optimization
scenarios supported by existing simulators are limited, mainly focusing on the
traffic signal control. To address these challenges and limitations, we propose
the first open-source GPU-accelerated large-scale microscopic simulator for
transportation system simulation. The simulator is able to iterate at 84.09Hz,
which achieves 88.92 times computational acceleration in the large-scale
scenario with more than a million vehicles compared to the best baseline. Based
on the simulator, we implement a set of microscopic and macroscopic
controllable objects and metrics to support most typical transportation system
optimization scenarios. These controllable objects and metrics are all provided
by Python API for ease of use. We choose five important and representative
transportation system optimization scenarios and benchmark classical rule-based
algorithms, reinforcement learning, and black-box optimization in four cities.
The codes are available at
\url{https://github.com/tsinghua-fib-lab/moss-benchmark} with the MIT License.
