Using artificial intelligence for forecasting offenses and crimes in transport: theory and methodology

Kirill Zlokazov; Grant Sarkhoshyan

VAK Russia

5.1.4

USING ARTIFICIAL INTELLIGENCE FOR FORECASTING OFFENSES AND CRIMES IN TRANSPORT: THEORY AND METHODOLOGY Using artificial intelligence for forecasting offenses and crimes in transport: theory and methodology

АА Аа

Published in Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia · Pages 98–105 · Rubric: CRIMINAL LAW SCIENCES

Received: 22.05.2025 Accepted: 07.10.2025 Published: 25.12.2025 Language of publication: RUS

Authors

Kirill Zlokazov

ORCID Scopus ResearcherID Scholar SPIN

Grant Sarkhoshyan

ORCID

Kirill Zlokazov

Russian Federation

ORCID Scopus ResearcherID Scholar SPIN

Grant Sarkhoshyan

Transport Directorate of the Ministry of Internal Affairs of Russia for the Northwestern Federal District Russian Federation

ORCID

ABSTRACT

Introduction. The relevance of the study is driven by the challenges and threats to transport facilities and infrastructure, as well as the opportunities for digital transformation of the operational and service activities of the Russian Ministry of Internal Affairs. The increase in unauthorized interference and the public danger of encroachments necessitate the optimization of transport police operations, which can be effectively achieved using artificial intelligence methods, specifically neural network forecasting. The aim is to systematize the theory and methodology of applying neural network technologies to forecast offenses and crimes at transport infrastructure facilities, in order to enhance the efficiency of transport police. Research methods: general scientific methods of analysis, systematization, and concretization, applied to information on the use of artificial intelligence and neural network methods for forecasting transport offenses and crimes. Results. Domestic and foreign AI technologies used in forecasting offenses and crimes were analyzed; neural network forecasting methods suitable for modeling offenses and crimes were systematized; an algorithm for forecasting offenses and crimes in transport and at transport infrastructure facilities using neural network AI technology was specified. It is shown that Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Temporal Convolutional Network (TCN), and Graph Neural Network (GNN) can be applied for both operational (real-time) and strategic (criminological) forecasting. Examples of neural network models for these tasks are provided. Based on the analysis, an algorithm for developing a neural network forecasting model is formulated, describing its four implementation stages to enable practical model development.

KEYWORDS

crime intelligence forecasting, artificial neural network, neural network crime prediction, neural network offense analysis, intelligent crime prediction, criminological forecasting, IT for offense prediction

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GOST

Zlokazov K. V., Sarkhoshyan G. R. Using artificial intelligence for forecasting offenses and crimes in transport: theory and methodology // . 2026. pp. 98-105. (Date of access 19.08.2026).

MLA

Kirill Zlokazov, Sarkhoshyan G. R.. "Using artificial intelligence for forecasting offenses and crimes in transport: theory and methodology." (2026): 98-105.

APA

Zlokazov K. V., Sarkhoshyan G. R. (2026). Using artificial intelligence for forecasting offenses and crimes in transport: theory and methodology. , 98-105.

Chicago

Kirill Zlokazov, Sarkhoshyan G. R.. "Using artificial intelligence for forecasting offenses and crimes in transport: theory and methodology." , no. (2026): 98-105.

WIKI

{{cite journal | author = Zlokazov K. V., Sarkhoshyan G. R. | title = Using artificial intelligence for forecasting offenses and crimes in transport: theory and methodology | journal = | year = 2026 | pages = 98-105 }}

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USING ARTIFICIAL INTELLIGENCE FOR FORECASTING OFFENSES AND CRIMES IN TRANSPORT: THEORY AND METHODOLOGY Using artificial intelligence for forecasting offenses and crimes in transport: theory and methodology

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