Unfortunately, these are both expensive, and (currently) have to be placed at a fixed location. In this project we propose the use of data available via social media which constitutes a cost-effective and flexible solution. The widespread use of social media nowadays provides an interesting new angle on monitoring large crowds. Social media are an interesting source of qualitative information and by analysing this information it should be possible to predict potential behaviour. Information posted on social media is available at low cost and analysing such data is a cost-effective means to get insight in what is exactly going on in a crowd, before, during and after large mass events.
Using this input, SPACES will make a prediction of the development of a scenario in the near future. The main technique for making this prediction is agent-based simulation. Specifically, an agent-based simulation model will be developed that contains knowledge about the intra- and interpersonal dynamics of the (mental) states and actions of individuals in a crowd, such as emotion contagion, and (group) decision making. Based on this model, the system will be able to predict, for example, the emergence of aggressive outbursts, panicking behaviour, or congested areas in parts of a crowd.
The proposed project envisions development and testing of a combination of innovative techniques, including dedicated methods to extract relevant information from the dataset in real-time; to match data patterns that are found to psychological states (e.g. fear, stress, anger); and to perform simulation and reasoning about these states to derive predictions about potential future developments. The intelligent system will use this output to provide support for police officers and (formal) guardians in both detecting, decreasing and avoiding (the number of) incidents in large-scale events.