Online travel mode detection method using automated machine learning and feature engineering
Abstract Online travel mode detection provides context information useful for location based services, in order to deliver a customized user experience. In the last years, many smartphone based travel mode detection techniques have been proposed, but few explored the usage of dimensionality reduction in conjunction with hyperparameter optimization to improve accuracy with a reduced cost. In this paper, we propose a method to improve the accuracy and computational cost trade off of travel mode detection, in which use state of the art Feature Engineering and Automated Machine Learning techniques. In addition, we apply the proposed method in a real mobility dataset using different features and parameters. Our experiments showed that the combination of these techniques can greatly improve online detection performance.
Future Gener. Comput. Syst.