Contributions to trajectory analysis and prediction: statistical and deep learning techniques

• Autores: Abdulrahman Qasem Al Molegi
• Directores de la Tesis: Antoni Martínez Ballesté (dir. tes.), Agusti Solanas (dir. tes.)
• Lectura: En la Universitat Rovira i Virgili ( España ) en 2019
• Idioma: español
• Tribunal Calificador de la Tesis: Karina Gibert Oliveras (presid.), Sergio Gómez Jiménez (secret.), Francisco José Casino Cembellin (voc.)
• Programa de doctorado: Programa de Doctorado en Ingeniería Informática y Matemáticas de la Seguridad por la Universidad Rovira i Virgili
• Materias:
  • Matemáticas
    • Ciencia de los ordenadores
      • Informática
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • Due to the relationship between people’s daily life and specific geographic locations, the historical trajectory data of a person contains lots of valuable information that can be used to discover their lifestyle and regularity. The generalisation in the use of mobile devices with location capabilities has fueled trajectory mining: the research area that focuses on manipulating, processing and analysing trajectory data to aid the extraction of higher level knowledge from the trajectory history of a user. Based on this analysis, even the person’s next probable location can be predicted.

    Predicting people's next location has attracted the attention of both scientists and large Internet companies, for a variety of reasons. The analysis of location data collected from smart mobile devices paves the way for the improvement of current Location-Based Services (LBSs) and the rise of new business models, based on rich notifications related to the right prediction of users' next location.

    The main utilized methods are: 1. The first approach is related to using statistical techniques such as Markov Model.

    2. The second approach is related to the exploration of using deep learning models such as Recurrent Neural Networks and Convolutional Neural Network.

    This thesis proposes various models to predict the future state of people movement, based on different machine learning techniques (such as Markov Chains, Recurrent Neural Networks and Convolutional Neural Networks) and considering different input representation methods (embedding learning and one-hot vector). Moreover, the attention technique is used in the prediction model, aiming at aligning time intervals in people’s trajectories that are relevant to a specific location. Furthermore, the thesis proposes a time encoding scheme to capture movement behavior characteristics. In addition to that, it analyses the impact of Space-Time representation learning through evaluating different architectural configurations. Finally, trajectory analysis and location prediction is applied to real-time smartphone-based monitoring system for seniors.