Synchronization of city public transport timetables at a transfer node using genetic algorithms
Abstract
The research is devoted to the actual problem of optimizing the operation of urban public transport by synchronizing public transport schedules at transfer nodes. The relevance of the study is due to the growing need to improve the efficiency and quality of service to passengers of city transport.
Timetable synchronization is one of the key factors affecting passenger satisfaction and the overall efficiency of the transport system. Firstly, the coordination of schedules for various types of transportation enables passengers to make quick transfers, thereby reducing waiting times. This is especially significant in cases of transfers between different modes of transportation, such as buses, trams, subways, or commuter trains. Secondly, timetable synchronization facilitates the rational use of transportation resources, allowing for the avoidance of route duplication and increasing vehicle occupancy. This, in turn, contributes to lower operational costs and more efficient management of transportation infrastructure. Thirdly, a rational and efficiently functioning transportation system helps to reduce harmful emissions into the atmosphere. This occurs through a decrease in the need for personal vehicles and a reduction in the number of vehicles on the roads.
The transplant node in the Verkhnyodniprovsk, Dnipropetrovsk region of Ukraine, was chosen as the object of the study. The purpose of the study is to improve the efficiency of passenger service in the city bus network by synchronizing the schedules for the “Autovokzal” transport node.
To achieve the goal of the research, field studies were conducted in the selected transport node, a simulation model of the operation of the transport node was developed, and schedules were optimized using a genetic algorithm based on the results of simulations.
The results of the study demonstrated the effectiveness of the proposed approach. The improved traffic schedule was obtained, which allows to reduce the average waiting time of passengers for a transfer in the morning peak by 3.2%.
Keywords: schedule synchronization, transfer node, simulation modeling, genetic algorithm, optimization, urban public transport.