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Publications

The page lists the publications related to MOSS and its related projects.

  • Zhang, J., Ao, W., Yan, J., Jin, D., & Li, Y. (2024). A GPU-accelerated Large-scale Simulator for Transportation System Optimization Benchmarking. arXiv preprint arXiv:2406.10661.
  • Zhang, J., Ao, W., Yan, J., Rong, C., Jin, D., Wu, W., & Li, Y. (2024). MOSS: A Large-scale Open Microscopic Traffic Simulation System. arXiv preprint arXiv:2405.12520.
  • Zhang, J., Ao, W., Jin, D., Liu, L., & Li, Y. (2023, November). A City-level High-performance Spatio-temporal Mobility Simulation System. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Sustainable Mobility (pp. 23-32).
  • Zhang, J., Jin, D., & Li, Y. (2022, November). Mirage: an efficient and extensible city simulation framework (systems paper). In Proceedings of the 30th International Conference on Advances in Geographic Information Systems (pp. 1-4).
  • Feng, J., Zhang, J., Yan, J., Zhang, X., Ouyang, T., Liu, T., ... & Li, Y. (2024). CityBench: Evaluating the Capabilities of Large Language Model as World Model. arXiv preprint arXiv:2406.13945.
  • Feng, J., Du, Y., Liu, T., Guo, S., Lin, Y., & Li, Y. (2024). CityGPT: Empowering Urban Spatial Cognition of Large Language Models. arXiv preprint arXiv:2406.13948.
  • Rong, C., Ding, J., Liu, Z., & Li, Y. (2023). City-wide origin-destination matrix generation via graph denoising diffusion. arXiv preprint arXiv:2306.04873, 1.
  • Zhang, X., Liu, Y., Lin, Y., Liao, Q., & Li, Y. (2024, March). Uv-sam: Adapting segment anything model for urban village identification. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 20, pp. 22520-22528).
  • Yuan, Y., Ding, J., Shao, C., Jin, D., & Li, Y. (2023, August). Spatio-temporal diffusion point processes. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3173-3184).
  • Yuan, Y., Ding, J., Wang, H., Jin, D., & Li, Y. (2022, August). Activity trajectory generation via modeling spatiotemporal dynamics. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4752-4762).
  • Zhang, G., Yu, Z., Jin, D., & Li, Y. (2022, August). Physics-infused machine learning for crowd simulation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2439-2449).