Transportation Mode Detection Using Feature Selection and Hyperparameter Optimization

As mobile internet technology rapidly advances, the importance of smartphones outfitted with diverse sensors has grown, particularly for transportation mode detection (TMD). Information regarding transportation modes is essential for improving urban development, traffic control, and route planning. Despite extensive research in TMD utilizing conventional machine learning techniques, the results often fall below expectations, largely due to their dependence on handcrafted features. Moreover, while long short‐term memory (LSTM) methods are employed for TMD, they generally fail to capture specific local nuances in complex time‐series data and encounter difficulties optimizing feature selection. To address these issues, we propose a refined TMD model incorporating a sophisticated spatial attention‐based transductive LSTM (TLSTM) network, further improved by reinforcement learning (RL) with an innovative scope loss function for efficient feature selection. The TLSTM framework is adept at identifying minor temporal variations in data, advancing beyond traditional LSTM models by applying transductive learning (TL) to concentrate on data points proximate to the test instance. The scope loss function balances the utilization of known data with the exploration of new data vistas. Additionally, our model integrates an upgraded differential evolution (DE) algorithm for intricate hyperparameter optimization, which includes a novel mutation strategy coupled with k‐means clustering to pinpoint crucial clusters. Extensive evaluations on the SHL, HTC, and US‐TMD datasets demonstrate that our advanced TMD algorithm outperforms existing competitive models. The F‐measure recorded by our system on these datasets stands at 90.140%, 88.315%, and 89.110%, respectively. This state‐of‐the‐art approach highlights the efficacy of TLSTM amalgamated with spatial attention and RL techniques in boosting the precision and dependability of TMD.