# MOSS: A Large-scale Open Microscopic Traffic Simulation System

arXiv: https://arxiv.org/abs/2405.12520 · HTML: https://mlsanity.com/paper/2405.12520/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2024.

## Built on by (most cited first, 6 of 6)

- [Up-sampling-only and Adaptive Mesh-based GNN for Simulating Physical Systems](https://mlsanity.com/paper/2409.04740/) (2024)
- [UrbanWorld2.0: A Multimodal Agentic Framework for Reality-Aligned 3D World Generation at City-Scale](https://mlsanity.com/paper/2511.18005/) (2025)
- [TrafficSimAgent: A Hierarchical Agent Framework for Autonomous Traffic Simulation with MCP Control](https://mlsanity.com/paper/2512.20996/) (2025)
- [Gradient-based Trajectory Optimization with Parallelized Differentiable Traffic Simulation](https://mlsanity.com/paper/2412.16750/) (2024)
- [CityLight: A Neighborhood-inclusive Universal Model for Coordinated City-scale Traffic Signal Control](https://mlsanity.com/paper/2406.02126/) (2024)
- [Empirical Grounding Improves the Realism of LLM Agents Simulating Human Behavior During Disruptions](https://mlsanity.com/paper/2607.17437/) (2026)

## Abstract (arXiv)

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.