MobilitApp: A Deep Learning-Based Tool for Transport Mode Detection to Support Sustainable Urban Mobility

The visible effects of climate change in urban areas are driving a paradigm shift in societal and political priorities. Urban planners, public transport providers, and traffic managers are increasingly focused on redesigning cities to promote sustainable mobility and create green spaces for pedestrians, cyclists, and scooter users. In alignment with these objectives, the European Climate Law mandates a minimum 55% reduction in greenhouse gas emissions by 2030 and climate neutrality by 2050. Achieving these targets requires robust tools to collect and analyze mobility data, enabling the evaluation of citizens’ travel habits and the planning of sustainable urban infrastructure. This study presents MobilitApp, a tool developed based on a deep learning (DL) model for real-time detection of transportation modes using smartphone sensor data. Our approach leverages a hierarchical model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory (LSTM) layers for temporal processing, enhanced by skip connections. To ensure computational efficiency on mobile devices, the system integrates statistical techniques for early motion detection, minimizing reliance on DL models. The model was trained on a dataset of multimodal trips in Barcelona, achieving over 80% accuracy for most transport modes and a weighted average accuracy of 88%. These results highlight the effectiveness of our approach for accurately predicting users’ transport modes during their trips. The MobilitApp tool provides an intuitive platform for collecting and analyzing urban mobility data. By analyzing travel patterns, transport modes, and mode-switching behaviors, it delivers actionable insights to city planners, aiding in the enhancement of urban mobility, promotion of sustainable development, and transition to greener cities.