Understanding urban structures and crowd dynamics leveraging largescale vehicle mobility data A comprehensive understanding of city structures and urban dynamics can greatly improve the efficiency and quality of urban planning and management, while the traditional approaches of which, such as manual surveys, usually incur substantial labor and time. In this paper, we propose a datadriven framework to sense urban structures and dynamics from largescale vehicle mobility data. First, we divide the city into finegrained grids, and cluster the grids with similar mobility features into structured urban areas with a proposed distanceconstrained clustering algorithm (DCCA). Second, we detect irregular mobility traffic patterns in each area leveraging an ARIMAbased anomaly detection algorithm (ADAM), and correlate them to the urban social and emergency events. Finally, we build a visualization system to demonstrate the urban structures and crowd dynamics. We evaluate our framework using realworld datasets collected from Xiamen city, China, and the results show that the proposed framework can sense urban structures and crowd comprehensively and effectively.

Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a lowcost sensor alternative, which enables deep understanding of population behaviour such as the Global Positioning System (GPS) data. However, the automated analysis of such low dimensional sensor data, requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day or the difference between weekendweekday trends. In this paper, we propose a novel time series analysis technique that allows for multiple different transition matrices depending on the dataxe2x80x99s contextual realisations all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AIHMM) is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies.