R. Das and S. Winter, “A Neuro-Fuzzy based Hybrid Intelligent Framework for Transport Mode Detection,” in Proceedings of the 6th International Workshop on Mobile Entity
Localization, Tracking and Analysis (MELT), 2016.
Understanding transport mode detection, is important to transport planning and movement behavior analysis. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classication. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format with more exibility, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel multi-layered hybrid intelligent framework is developed by integrating a fuzzy logic and a machine learning approach to complement each other’s limitations. Thus in this paper an intelligent fuzzy logic-based near-real time mode detection framework is presented that can perform comparable to state-of-the-art machine learning approaches by bridging the trade-off between explanatory power and prediction accuracy of the models. The results demonstrate that the proposed framework has achieved the aim in the context of near-real time transport mode detection.