On Urban Data Analytics and Applications in the Big Data Era

The proliferation of smart technologies has provided significant advances in the way people receive information and interact in smart cities. For instance, smart traffic monitoring systems allow citizens and city operators to receive in real-time alerts about traffic conditions. At the same time, alternative means of transport, such as bike sharing systems, have enjoyed tremendous success in many major cities around the world today and can provide real-time information regarding the mobility of the users. It is quite clear that smart cities systems generate vast amounts of data during their daily operation. Researchers typically focus on analyzing such urban data in order to identify mobility patterns or infer user preferences to provide services to the crowd, such as traffic warning applications. In this thesis, we aimed at analyzing the vast amounts of urban data generated in these systems, that are large in volume, and typically heterogeneous, to identify key insights in their operation and develop a suite of crowdsourcing techniques to improve the working of smart city applications and ultimately enhance the sustainability of smart cities.