In the last four decades several methods have been used to model occupants' presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents is systematically constructed, and, using bibliometric analysis techniques, the scientific production and landscape are described. The initial literature screening identified more than 750 studies, out of which 278 publications were selected. They provide an overarching view of the development of OPA modeling methods. The research field has evolved from longitudinal collaborative efforts since the late 1970s and, so far, covers diverse building typologies mostly concentrated in a few climate zones. The modeling approaches in the selected literature are grouped into three categories (rule-based models, stochastic OPA modeling, and data-driven methods) for modeling occupancy-related target functions and a set of occupants' actions (window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use). The explanatory modeling is conventionally based on the model-based paradigm where occupant behavior is assumed to be stochastic, while the Revised Manuscript with No Changes MarkedClick here to view linked References data-driven paradigm has found wide applications for the predictive modeling of OPA, applicable to control systems. The lack of established standard evaluation protocols was identified as a scientifically important yet rarely addressed research question. In addition, machine learning and deep learning are emerging in recent years as promising methods to address OPA modeling in real-world applications.

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Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation, and time-series data imputation. While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this article, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data. We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs. Finally, we point out future research directions to benefit researchers in this area.

Existing journey planners and route recommenders mainly focus on calculating the shortest path with minimum distance or travel time. However, elderly people and those with special needs (i.e. those in wheelchairs or walking with sticks) often prefer a safer and more gentle journey. Given that their route options are affected by accessibility issues such as climbing a steep slope, it is important to design a journey planner that takes in to account the accessibility of the route, as well as the standard metrics, such as travel time and distance. Accessibility has not been explored widely in path finding problems. There are two key challenges for computing accessibility. First, the accessibility of a route is not well-defined. Second, the accessibility of a route varies from user to user. In this paper, a new algorithm is designed to tackle the above two challenges. Two metrics are defined to reflect the accessibility of a route, in terms of the total vertical distance and the maximum slope. Then, a multi-objective A* search algorithm is designed to obtain a set of Pareto-optimal routes in terms of the total distance covered and the two accessibility metrics. The user can then choose from the routes provided by the new algorithm, the most suitable one according to their own preferences. The experimental results show that the proposed algorithm is able to