IMPLEMENTATION AND ADAPTATION OF AUTOMATED PASSENGER FLOW MONITORING SYSTEMS IN SUBURBAN RAILWAY TRANSPORTATION

Abstract

Purpose. The aim of the research is to review and analyze the operation of automated passenger flow monitoring and management systems in suburban rail transport in other countries. To develop and adapt a phased algorithm for the implementation of automated passenger counting and flow assessment systems for suburban railway transport. To structure the tasks of each stage, implement, and comprehensively improve the systems’ operation to enhance the accuracy of data collection and analysis. To ensure effective management of passenger flows, as well as to increase the efficiency of railway infrastructure utilization. Methodology. During the research, methodologies for monitoring passenger flows and the processing of obtained results, which are used in other countries, were analyzed. Based on the analysis of scientific publications and literary sources, an optimal approach for implementing automated passenger flow assessment was selected, and methods for improving the accuracy of the obtained results were proposed. Results. Monitoring passenger flows in the modern passenger transportation model should be carried out and analyzed continuously in real-time. To achieve this, most countries are transitioning to modern methods of data collection and processing, often utilizing Petri net principles and artificial intelligence assistance. The use of these technologies allows for structured analysis of passenger flow data, making it possible to generate reliable forecasts of both the number of passengers transported and the necessary infrastructure elements. The results of the research described in this article provide a theoretical foundation for optimizing train schedules and selecting effective rolling stock configurations. Equally important is the research direction aimed at identifying seasonal variations in passenger flows, as peak periods often lead to disruptions in transportation system operations. Scientific novelty. An adapted algorithm for the automated collection and analysis of passenger flow data has been proposed for the first time; unlike existing algorithms, it additionally takes into account the characteristics of the settlements within which stops are located, as well as the requests and needs of transport service users regarding the organization of passenger transport services Practical significance. The use of more accurate passenger flow data will help reduce operating costs, minimize rolling stock downtime and improve operational performance. Adjusting forecasts will enable the calculation of sufficient rolling stock to meet transport demand, whilst identifying seasonal periods will enable adaptation to sudden changes in passenger volumes, avoiding significant overcrowding of rolling stock during peak periods or, conversely, preventing the use of high-capacity rolling stock during periods of declining passenger volumes.