Several techniques aim to classify human activity using data from sensors e.g., GPS, accelerometer, Wi-Fi and GSM. The sensor data allow inferring transportation modes as car, bus, walk, and bike. Despite some techniques show improvements in accuracy, the researchers constantly deal with issues such as over-segmentation and low precision in trip reporting. Journeys are over-segmented due to the ambiguous situations, for instance: traffic lights, traffic jam, bus stops and weak signal reception. Thereby, current techniques report high misclassification errors. We present a method for detecting changes of transportation mode on a multimodal journey, where the input data regard to the classification of human activities. We use a space transformation for extracting features that identify a transition between two transportation modes. The data is collected from the Google API for Human Activity Classification through a crowdsourcing-based application for smartphones. Results show improvements on precision and accuracy in comparison to initial classification data outcomes. Therefore, our approachreduces the over-segmentation for multimodal journeys.