Exploring the Capabilities of Deep Learning Models for Transport and Human Activity Recognition

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This thesis explores the transformative potential of deep learning smartphone-based transportation mode detection systems in enhancing urban planning in the city of Barcelona. The core of this thesis is the development and comparison of algorithms, coupled with extensive data analysis and preprocessing techniques, aimed at reliable transport mode detection. We delve into creating a real-time system capable of predicting transport usage patterns. For that, a dataset of smartphone sensor data has been created with examples of journeys using multiple modes of transportation in the metropolitan area of Barcelona. In the deep learning model, we have experimented with architectures combining convolutional networks and LSTMs to finally create a hierarchical model that combines the use of CNNs for feature extraction, with the ability to process time series from the LSTM layers using skip connections. For robust and battery-efficient detection, we have combined this model with statistical techniques, which allow us to detect at an early stage whether the user is moving, standing or walking. This allows not to make excessive use of the deep learning model, which can be costly in mobile devices. Following, an android application is presented which implements the mentioned techniques and presents a simple way to collect mobility data, which can be useful for future studies on urban mobility in the city. Finally, the various ethical, social and environmental issues that such systems may have are studied, describing the privacy and interpretability factors that this tools must comply with.