Automatic classification of transportation modes using smartphone sensors: addressing imbalanced data and enhancing training with focal loss and artificial bee colony algorithm

The growing interest in utilizing smartphone sensors to differentiate various transportation modes stems from its potential benefits across multiple sectors, including health monitoring, transportation planning, and geo-specific utilities. This research presents a model that uses smartphone accelerometers, magnetometers, and gyroscope sensor data to classify transportation and vehicular modes. To tackle the issue posed by imbalanced data, we suggest a training approach incorporating focal loss, which selectively samples minority class examples and enables the model to focus on more complex instances. Our model surpasses other machine learning models and achieves impressive results on an imbalanced dataset obtained from the HTC company. This dataset includes data from 224 volunteers collected over two years, comprising 8311 h and 100 GB of data. We suggest using the artificial bee colony (ABC) algorithm to improve the training process further. This algorithm is adept at comprehensively exploring the search space, thereby assisting in determining appropriate initial weights. Such an approach helps accelerate the convergence process during training and mitigates the issue of initialization sensitivity often linked with gradient-dependent training techniques such as backpropagation. In our research, we have conducted tests on the dataset to pinpoint the most effective values for critical parameters. Furthermore, we perform ablation studies to evaluate the effects of focal loss and the ABC algorithm on the model’s efficacy, illustrating their individual and combined beneficial impacts. This research holds significant implications for the practical application of mobile sensing in transportation, offering a robust tool for enhancing various services and systems related to mobility and urban planning.