Multi-Modal Network Traffic Forecasting with Large Language Models and Edge Intelligence in Smart Cities
Keywords:
Multi-modal traffic forecasting; large language models; edge intelligence; smart cities; spatiotemporal prediction; sustainability; fairness; governanceAbstract
The rapid proliferation of connected sensing infrastructure across urban environments has given rise to unprecedented volumes of multi-modal traffic data, encompassing vehicle counts, video feeds, weather measurements, social media signals, and more. Simultaneously, the emergence of large language models (LLMs) with strong generalization capabilities has begun to reshape spatiotemporal forecasting, moving beyond task-specific architectures toward models that leverage rich semantic representations across modalities. This paper presents an interdisciplinary systems analysis of multi-modal network traffic forecasting that integrates LLMs with edge intelligence in smart cities. We dissect the architectural trade-offs involved in placing forecasting intelligence across cloud, edge, and device tiers, examining how multi-modal data fusion, LLM fine-tuning, and edge-offloading strategies jointly determine latency, accuracy, privacy, and energy consumption. The discussion extends beyond algorithmic performance to address structural considerations of interoperability, resource governance, fairness across urban populations, robustness to distributional shifts, and sustainability in large-scale deployments. By synthesizing recent advances in graph neural forecasting, federated learning, and foundation models, the paper identifies systemic tensions between centralization and decentralization, between model expressiveness and inference cost, and between predictive accuracy and equitable service delivery. We argue that a successful deployment paradigm must treat the forecasting pipeline as a socio-technical infrastructure, embedding lifecycle management, continuous monitoring, and regulatory compliance from the outset. The analysis offers design principles and policy implications for city authorities and system architects seeking to harness LLM-driven multi-modal traffic forecasting in a responsible and resilient manner.
References
1. Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for smart cities. IEEE Internet of Things Journal, 1(1), 22–32.
2. Williams, B. M., & Hoel, L. A. (2003). Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of Transportation Engineering, 129(6), 664–672.
3. Ma, X., Tao, Z., Wang, Y., Yu, H., & Wang, Y. (2015). Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54, 187–197.
4. Yu, B., Yin, H., & Zhu, Z. (2018). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (pp. 3634–3640). AAAI Press.
5. Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In International Conference on Learning Representations.
6. Wu, Z., Pan, S., Long, G., Jiang, J., & Zhang, C. (2019). Graph WaveNet for deep spatial-temporal graph modeling. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (pp. 1907–1913). AAAI Press.
7. Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H. (2019). Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, 33, 922–929.
8. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.
9. McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (pp. 1273–1282). PMLR.
10. Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J. Y., Shi, X., Chen, P., Liang, Y., Li, Y., Pan, S., & Wen, Q. (2024). Time-LLM: Time series forecasting by reprogramming large language models. In International Conference on Learning Representations.
11. Li, Z., Xia, L., Xu, Y., Wu, L., & Zheng, Y. (2024). UrbanGPT: Spatio-temporal large language models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM.
12. Tedjopurnomo, D. A., Bao, Z., Zheng, B., Choudhury, F. M., & Qin, A. K. (2022). A survey on modern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Transactions on Knowledge and Data Engineering, 34(4), 1544–1561.
13. Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y. C., Yang, Q., Niyato, D., & Miao, C. (2020). Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(3), 2031–2063.
14. Zhang, C., Zhang, H., Qiao, J., Li, Z., & Alouini, M. S. (2025). TIDES: Traffic Intelligence with DeepSeek Enhanced Spatial Temporal Prediction. IEEE Journal on Selected Areas in Communications.
15. Liu, W., Zheng, Z., Zhong, J., & Yang, Y. (2024). ST-LLM: Spatial-temporal large language model for traffic prediction. arXiv preprint arXiv:2401.10134.
16. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738–1762.
17. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (pp. 149–159). PMLR.
18. Patterson, D., Gonzalez, J., Hölzle, U., Le, Q., Liang, C., Munguia, L. M., Rothchild, D., So, D., Texier, M., & Dean, J. (2022). The carbon footprint of machine learning training will plateau, then shrink. Computer, 55(7), 18–28.
19. Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., Lee, T., Chi, E. A., Bui, T., Bahl, A., Sen, P., Beam, A., Stojanov, P., Maharaj, T., Yurochkin, M., … Liang, P. (2021). WILDS: A benchmark of in-the-wild distribution shifts. In Proceedings of the 38th International Conference on Machine Learning (pp. 5637–5664). PMLR.
20. Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine, 38(3), 50–57.
21. Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014). Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 5(3), 1–55.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
