Intelligent system of passenger flows dynamiC 2D-visualization for public transport routes

: pp. 79 - 119
Lviv Polytechnic National University, Information Systems and Networks Department
Lviv Politechnic National University, Department of Management and International Entrepreneurship
Lviv Polytechnic National University, Osnabrück University, Institute of Computer Science

In order to increase the attractiveness of public transport for urban residents, a software product has been created for transport companies that, by visualizing passenger traffic, helps to improve the quality of public transport services provided within the city. The paper analyses existing and current scientific developments and literature sources, which show the advantages and disadvantages of a large number of different algorithms and methods, approaches, and methods for solving problems of 2D- visualization of passenger flows on public routes. As a result of the research, stable connections have been established between the factors and criteria involved in assessing the quality of passenger transport services. The system analysis of the designed system is executed, and examples of the structure of an intelligent system of 2D visualization of passenger flows are created. The connections of the system with the essential elements of the external world are analysed. For a visual representation, diagrams of usage variants, classes, sequences, states, and activities are created according to UML notation. Our own unique algorithms have been created for displaying visualizations in two different modes: schematic and “on the map”. In the “on the map” mode, a method of calculating data on the movement of transport units on the route was successfully applied for 2D visualization on the screen, taking into account the absolute values of geographical coordinates in  the world. This avoids unnecessary errors and inaccuracies in  the calculations. An artificial neural network has been developed that operates using the RMSprop learning algorithm. The artificial neural network predicts how the values of passenger traffic will change when adjusting the schedule of the transport unit on the route. The obtained results make it possible to form and substantiate the expediency of changing the schedule of the vehicle running on the route in order to make more efficient use of races during peak times.

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