Information Theory-Based Feature Extraction for Transportation Mode Detection using Federated Ensemble Learning

• Edvar M. S. Neto UFAL
• David H. S. Lima UFAL / IFAL
• Douglas L. L. Moura UFAL / UFMG
• Andre L. L. Aquino UFAL

DOI: https://doi.org/10.5753/sbcup.2025.9099

Resumo

Transportation mode detection is a key enabler of intelligent mobility systems, supporting real-time traffic management, personalized travel services, and urban planning. However, achieving high accuracy while balancing computational efficiency and user privacy remains a critical challenge, known as the efficiency-privacy-accuracy trilemma. This work introduces a novel information-theoretic approach integrating entropy and complexity metrics into centralized and federated learning architectures. Our approach addresses performance issues in traditional federated learning by leveraging the structural properties and patterns within time-series data. The results demonstrate that the proposed approach outperforms conventional methods in MLP, Federated MLP, and federated ensemble learning, achieving accuracy improvements of 3.8%, 4.31%, and 5.54%, respectively. These results enable practical applications such as privacy-preserving transportation services and data-efficient urban planning.

Referências

Alam, M. G. R., Haque, M., Hassan, M. R., Huda, S., Hassan, M. M., Strickland, F. L., and AlQahtani, S. A. (2023a). Feature cloning and feature fusion based transportation mode detection using convolutional neural network. IEEE Transactions on Intelligent Transportation Systems, 24(4):4671–4681.

Alam, M. M., Ahmed, T., Hossain, M., Emo, M. H., Bidhan, M. K. I., Reza, M. T., Alam, M. G. R., Hassan, M. M., Pupo, F., and Fortino, G. (2023b). Federated ensemble-learning for transport mode detection in vehicular edge network. Future Generation Computer Systems, 149:89–104.

Bandt, C. and Pompe, B. (2002). Permutation entropy: A natural complexity measure for time series. Phys. Rev. Lett., 88:174102.

Carpineti, C., Lomonaco, V., Bedogni, L., Felice, M. D., and Bononi, L. (2018). Custom dual transportation mode detection by smartphone devices exploiting sensor diversity. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 367–372.

Elbir, A. M., Soner, B., Çöleri, S., Gündüz, D., and Bennis, M. (2022). Federated learning in vehicular networks. In 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), pages 72–77.

Qureshi, K. N. and Abdullah, A. H. (2013). A survey on intelligent transportation systems. Middle-East Journal of Scientific Research, 15(5):629–642.

Rosso, O. A., Larrondo, H. A., Martin, M. T., Plastino, A., and Fuentes, M. A. (2007). Distinguishing noise from chaos. Phys. Rev. Lett., 99:154102.

Santos, M., Santos, G., and Aquino, A. (2024). Identificação do comportamento de motoristas: Uma abordagem baseada em teoria da informação. In Anais do XVI Simpósio Brasileiro de Computação Ubíqua e Pervasiva, pages 31–40, Porto Alegre, RS, Brasil. SBC.

Xiao, Z., Wang, Y., Fu, K., and Wu, F. (2017). Identifying different transportation modes from trajectory data using tree-based ensemble classifiers. ISPRS International Journal of Geo-Information, 6(2).

Yan, G. and Qin, Q. (2020). Retracted: The application of edge computing technology in the collaborative optimization of intelligent transportation system based on information physical fusion. IEEE Access, 8:153264–153272.

Yurdem, B., Kuzlu, M., Gullu, M. K., Catak, F. O., and Tabassum, M. (2024). Federated learning: Overview, strategies, applications, tools and future directions. Heliyon, 10(19):e38137.

Zhang, H., Bosch, J., and Olsson, H. H. (2021). End-to-end federated learning for autonomous driving vehicles. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1–8.

Zhang, S., Li, J., Shi, L., Ding, M., Nguyen, D. C., Tan, W., Weng, J., and Han, Z. (2024). Federated learning in intelligent transportation systems: Recent applications and open problems. IEEE Transactions on Intelligent Transportation Systems, 25(5):3259–3285.