Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities

Abstract

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 low-cost 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 weekend/weekday trends. In this paper, we propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data’s 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 (AI-HMM) 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.

Special Issue on “Smart City and Smart Infrastructure”

“…Environmental sensors (e.g., thermometers, humidity sensors, anemometers, and light sensors) have also been utilized to account for environmental effects in target applications [5,6]. Position information, which is typically collected by the global position system (GPS), enables understanding the behavior of GPS-equipped entities, such as human beings and vehicles [1]. Wireless smart sensors can significantly reduce the installation and maintenance costs of the monitoring system, in addition to allowing multi-sensor data to be collected from a dense network of sensors [7][8][9].…”

“…The results indicated that the method helps to choose the computation methods used in simulating sludge bulking with minimized measurement costs. Qarout et al [1] used GPS data in probabilistic modeling to understand human behavior in urban areas. The proposed approach, the adaptive input hidden Markov model (AI-HMM), was shown to be capable of grouping different categories of human behavior trends and identifying time-specific anomalies.…”