Spatial partitioning of road traffic networks and their temporal evolution

Urban areas generally attract people from all interior areas. According to the current global trend, people are rapidly migrating from rural towards urban areas for several reasons that include availing better livelihood services and seeking better employment opportunities. Consequently, the population of cities all over the world is increasing significantly, and thereby raising the mobility demands manyfold. This strongly motivates the research areas of urban planning and urban computing to develop innovative technologies and move towards smart and more sustainable cities. As most of the urban population travel daily or frequently for their work or studies, traffic congestion has become a very important practical problem. It is affecting the urban population directly by incurring extra cost on the fuel and extra time spent, and indirectly in many ways. An important concern in smart urbanization of our societies is the avoidance of such congestions and maintenance of a smooth transportation. While the infrastructure development is one direction to deal with this problem, the analysis of spatial traffic data to discover the congestion formation and propagation patterns, and apply them to optimize the traffic flow is another direction. The research on road traffic networks data analysis is growing with the problems like fastest route computation, traffic clustering, traffic prediction, emerging event detection, anomaly detection and bottleneck identification. To discover the congestion patterns, the continuous tracking of the spatiotemporal evolution of the traffic load leading to congestions is an important problem. The research on development of methods to identify the congested partitions effectively and track their evolution efficiently has been very limited so far. In this thesis, we aim to capture the spatiotemporal evolution of urban road traffic networks. To this end, we propose technical methods to effectively partition road traffic networks in order to obtain the differently congested partitions at a point of time, and incrementally update those partitions in an efficient manner in order to track their evolution in real time. We firstly present a scalable method for traffic-based spatial partitioning of urban road traffic networks. It is based on a spectral theory based novel graph cut (referred as α-Cut)