  In the research of Intelligent Transportation Systems (ITS), traffic
simulation is a key procedure for the evaluation of new methods and
optimization of strategies. However, existing traffic simulation systems face
two challenges. First, how to balance simulation scale with realism is a
dilemma. Second, it is hard to simulate realistic results, which requires
realistic travel demand data and simulator. These problems limit computer-aided
optimization of traffic management strategies for large-scale road networks and
reduce the usability of traffic simulations in areas where real-world travel
demand data are lacking. To address these problems, we design and implement
MObility Simulation System (MOSS). MOSS adopts GPU acceleration to
significantly improve the efficiency and scale of microscopic traffic
simulation, which enables realistic and fast simulations for large-scale road
networks. It provides realistic travel Origin-Destination (OD) matrices
generation through a pre-trained generative neural network model based on
publicly available data on a global scale, such as satellite imagery, to help
researchers build meaningful travel demand data. It also provides a complete
open toolchain to help users with road network construction, demand generation,
simulation, and result analysis. The whole toolchain including the simulator
can be accessed at https://moss.fiblab.net and the codes are open-source for
community collaboration.
