## 1 Introduction
### Public Urban Transport
Modern transport systems include the control center (CC), data centers, operators, and communication infrastructure to connect vehicles to the CC. Data transfer utilizes Global System for Mobile Communication (GSM) networks, General Packet Radio Service (GPRS), Short Message Service (SMS), and Internet communications. In this way, the system monitors transport process parameters in real time, improving service quality and user satisfaction [9]. [PERSON] [10] proposes a new algorithm for developing initial routes, leveraging intermediate data on operational modes. The study employs genetic algorithms (GAs) to iteratively refine route configurations, using operations like crossover, mutation, and selection to evolve solutions across generations. This approach integrates traditional and heuristic optimization techniques, addressing multifactorial challenges in urban transport planning. High-quality initial data, including transport demand, supply, and passenger flow distributions, are essential parameters for this process. A new proposed model focuses on defining operational areas and dynamic transport lines in low-demand integrated zones. It introduces the concept of \"obliquity\", referring to route deviations from main lines, and calculates coefficients to assess their impact. A case study in Radotin and Cernosice near Prague demonstrates the model's feasibility, suggesting that optimizing routes and timetables can effectively address local transportation challenges [11]. By detecting crowdedness in public transport stations, the study by [PERSON] et al. [12] presents a computer vision solution to assist local authorities and operators in making informed decisions regarding different measures and system responses. Advanced computer vision technology extracts information from images and video streams, improving traditional detection algorithms. Beyond crowdedness detection, applications include people and vehicle count, lost object identification, rail safety monitoring, and illegal crossing detection. The proposed solution addresses the immediate need to manage crowdedness and demonstrates the broader potential of computer vision technology in public transport systems decisions. [PERSON] et al. [13] integrate linear algebra, graph theory, and statistical information processing to address route duplication within the Izhevsk, Udmurt Republic, public transport system. The analysis of 323 stopping points and 55 urban transport routes identifies duplicate routes through correlation analysis. Their findings suggest route synchronization, partially merging routes, and streamlining the network. These improvements contribute to better efficiency in Izhevsk's public transport and offer a model applicable to similar urban systems facing route duplication challenges. [PERSON] et al. [14] develop a data processing pipeline to acquire and refine transportation data, structuring it as a nonlinear graph representing Islamabad's network. Schedules are converted into the General Transit Feed Specification format, enhancing travel time estimation. The dataset identifies mobility patterns and improves intra-city connectivity, supporting urban development efforts focused on route optimization and intelligent journey planning. Centering on sustainable urban mobility, [PERSON] et al. [15] assess city transport conditions, specifically how car traffic flow affects public transport. A pilot study utilizing Google traffic data analyzes traffic characteristics and compares them with actual travel speeds of public transport on specific routes. Their findings aim to inform a broader research initiative to create a method for evaluating public transport operations across urban networks. By leveraging modern data sources like Google Traffic, their study reveals traffic dynamics and their impact on public transport, contributing to improved urban mobility solutions.