Intelligent Transportation Systems (ITS) are emerging field characterized by complex data model, dynamics and strict time requirements. Ensuring cybersecurity in ITS is a complex task on which the safety and efficiency of transportation depends. The imposition of standards for a comprehensive architecture, as well as specific security standards, is one of the key steps in the evolution of ITS. The article examines the general outlines of the ITS architecture and security issues. The main focus of security approaches is: configuration and initialization of the devices during manufacturing at perception layer; anonymous authentication of nodes in VANET at network layer; defense of fog-based structures at support layer and description and standardization of the complex model of data and metadata and defense of systems, based on AI at application layer. The article oversees some conventional methods as network segmentation and cryptography that should be adapted in order to be applied in ITS cybersecurity. The focus is on innovative approaches that have recently been trying to find their place in ITS security strategies. These approaches includes blockchain, bloom filter, fog computing, artificial intelligence, game theory and ontologies. In conclusion, a correlation is made between the commented methods, the problems they solve and the architectural layers in which they are applied.

Driver behavior models are an important part of road traffic simulation modeling. They encompass characteristics such as mood, fatigue, and response to distracting conditions. The relationships between external factors and the way drivers perform tasks can also be represented in models. This article proposes a methodology for establishing parameters of driver behavior models. The methodology is based on road traffic data and determines the car-following model and routing algorithm and their parameters that best describe driving habits. Sequential and parallel implementation of the methodology through the urban mobility simulator SUMO and Python are proposed. Four car-following models and three routing algorithms and their parameters are investigated. The results of the performed simulations prove the applicability of the methodology. Based on more than 7000 simulations performed, it is concluded that in future experiments of the traffic in Plovdiv it is appropriate to use a Contraction Hierarchies routing algorithm with the default routing step and the Krauss car-following model with the default configuration parameters.

The article presents comparison of two statistical methods of data prediction over transport datasets. Autoregressive integrated moving average and its seasonal modification—seasonal autoregressive integrated moving average—are often applied in timeseries data. In current article their effectiveness is assessed using transport data. The data are acquired from data surveillance traffic system of Technical University of Sofia, branch Plovdiv. The conducted experiment encompasses STL transformation, ADF and KPSS stationarity tests, analysis of ACF and PACF, and comparison of different ARIMA and SARIMA configurations. Comparative analysis of MAE, MAPE, and RMSE confirms that ARIMA outperforms SARIMA in current datasets.

Time series data, characterized by temporal dependencies, seasonality, and noise, are prevalent in domains such as healthcare, finance, energy, and transportation. Effective clustering of time series enables the discovery of patterns, supports forecasting, and facilitates data-driven decision-making. This paper provides a comprehensive review of time series clustering techniques, including conventional methods (e.g., k-means, hierarchical, and fuzzy clustering), similarity-based approaches (e.g., Dynamic Time Warping), feature-based methods, and deep learning models (e.g., autoencoders, convolutional and recurrent neural networks). The review analyzes tasks, application domains, performance outcomes, and key limitations, highlighting common challenges such as computational complexity, sensitivity to noise, and scalability issues. A particular focus is given to transport-related time series, including traffic flow, travel time, and congestion patterns, demonstrating how clustering can support traffic state classification, anomaly detection, and infrastructure planning. The analysis reveals a trade-off between accuracy, interpretability, and computational efficiency, emphasizing the need for scalable, robust, and domain-aware clustering frameworks. Finally, practical directions for future research are discussed, including lightweight hybrid approaches and transport-specific feature engineering to enhance clustering performance in real-world applications.