Russian Federation
UDC 343.851
Introduction. The relevance of this study stems from the challenges and threats facing transport facilities and infrastructure, as well as the opportunities presented by the digital transformation of the operational activities of the internal affairs agencies of the Russian Federation. The rise in unauthorised interference and the public danger posed by such offences necessitate the optimisation of police operations in the transport sector, which, in turn, can be effectively achieved by relying on artificial intelligence methods – specifically, neural network forecasting. Objective: to systematise the theory and methodology of applying neural network technologies for forecasting offences and crimes at transport infrastructure facilities, carried out to enhance the effectiveness of police operations in the transport sector. Research methods: general scientific methods of analysis, systematization and specification, applied to data on the use of artificial intelligence and neural network systems for forecasting offences and crimes in transport. Results. Domestic and foreign artificial intelligence technologies used in the prediction of offences and crimes have been analysed; neural network prediction methods suitable for constructing models of offences and crimes have been systematised; an algorithm for predicting offences and crimes in transport and at transport infrastructure facilities using neural network technology based on artificial intelligence has been specified. It has been demonstrated that a multi-layer perceptron (MLP), a recurrent neural network (RNN), temporal convolutional network (TCN) and graph neural network (GNN) can be applied for operational (in real-time) as well as strategic (criminological) prediction of offences and crimes in transport. Examples are provided of neural network models used to solve the problem of forecasting offences and crimes in transport. Based on the analysis carried out, an algorithm is formulated for developing a neural network model for forecasting offences and crimes. Four stages of its implementation are described, enabling the practical realization (development) of the forecasting model
intelligent crime prediction, artificial neural network, neural network crime prediction, neural network analysis of offences, intelligent prediction of crimes, criminological forecasting, information technologies for offence prediction
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