A fuzzy logic based transport mode detection framework in urban environment

Transport mode detection is an emerging research area in different domains such as urban planning, context-aware mobile computing, and intelligent transportation systems. Current approaches are mostly data-driven, based on machine learning approaches. However, machine learning models require substantial training data and cannot explain the reasoning procedure. Data-driven approaches also fall short while interpreting trajectories where ground truth information is limited. Therefore, this paper develops a novel knowledge-based approach for interpreting smartphone global positioning system trajectories by detecting various transport modes used during travel. The proposed model is based on an expert system that can work without any training, based solely on expert knowledge. Core is a fuzzy multiple-input multiple-output expert system using kinematic and spatial information with a well explained fuzzy reasoning scheme through a fuzzy rule base. The model can provide alternate predictions with varied certainty factors. Different membership function combinations have been evaluated in terms of accuracy and ambiguity, and the result demonstrates that the model performs best using a Gaussian–Gaussian combination, comparable to the existing machine learning approaches.