There is a growing need for coverage of large maritime areas, mainly in the exclusive economic zone (EEZ). Due to the difficulty of accessing such large areas, the use of satellite based sensors is the most efficient and cost-effective way to perform this task. Vessel behavior prediction is a necessary ability for detection of moving vessels with satellite imagery. In this paper we present an algorithm for selection of the best satellite observation window to detect a moving object. First, we describe a model for vessel behavior prediction and compare its performance to two base models. We use real marine traffic data (AIS) to compare their ability to predict vessel behavior in a time frame of between 1–24 hours. Then, we present a KINGFISHER, maritime intelligence system which uses our algorithm to track suspected vessels with satellite sensor. We also present the results of the algorithm in operational scenarios of the KINGFISHER.

Models of crowd dynamics are critically important for urban planning and management. They support analysis, facilitate qualitative and quantitative predictions, and synthesize behaviors for simulations. One promising approach to crowd modeling relies on micro-level agent-based simulations, where the interactions of simulated individual agents in the crowd result in macro-level crowd dynamics which are the object of study. This article reports on an agent-based model of urban pedestrian crowds, where culture is explicitly modeled . We extend an established agent-based social agent model, inspired by social psychology, to account for individual cultural attributes discussed in social science literature. We then embed the model in a simulation of pedestrians and explore the resulting macro-level crowd behaviors, such as pedestrian flow, lane changes rate, and so on. We validate the model by quantitatively comparing the simulation results to the pedestrian dynamics in movies of human crowds in five different countries: Iraq, Israel, England, Canada, and France. We conclude that the model can faithfully replicate urban pedestrians in different cultures. Encouraged by these results, we explore simulations of mixed-culture pedestrian crowds.

The ability to model and reason about the potential violence level of a demonstration is important to the police decision making process. Unfortunately, existing knowledge regarding demonstrations is composed of partial qualitative descriptions without complete and precise numerical information. In this article we describe a first attempt to use qualitative reasoning techniques to model demonstrations. To our knowledge, such techniques have never been applied to modeling and reasoning regarding crowd behaviors, nor in particular demonstrations. We develop qualitative models consistent with the partial, qualitative social science literature, allowing us to model the interactions between different factors that influence violence in demonstrations. We then utilize qualitative simulation to predict the potential eruption of violence, at various levels, based on a description of the demographics, environmental settings, and police responses. We incrementally present and compare three such qualitative models. The results show that while two of these models fail to predict the outcomes of real-world events reported and analyzed in the literature, one model provides good results. We also examine whether a popular machine learning algorithm (decision tree learning) can be used. While the results show that the decision trees provide improved predictions, we show that the QR models can be more sensitive to changes, and can account for what-if scenarios, in contrast to decision trees. Moreover, we introduce a novel analysis algorithm that analyzes the QR simulations, to automatically determine the factors that are most important in influencing the outcome in specific real-world demonstrations. We show that the algorithm identifies factors that correspond to experts' analysis of these events.