Communications - Scientific letters of the University of Zilina X:X | DOI: 10.26552/com.C.2026.051

Synchronizing Public Transport Schedules with Genetic Algorithms: Choosing the Heuristic Parameters

Vitalii Naumov ORCID...1,*, Assyl Tazhmuratova ORCID...2, Ihor Taran ORCID...3, Yuliia Melnikova ORCID...4, Daniel Kapica ORCID...1
1 Cracow University of Technology, Krakow, Poland
2 Mukhamedzhan Tynyshbayev ALT University, Almaty, Kazakhstan
3 Rzeszow University of Technology, Rzeszow, Poland
4 Dnipro University of Technology, Dnipro, Ukraine

Synchronized timetables are crucial for public transportation, yet optimizing them remains complex. While Genetic Algorithms (GAs) address this challenge effectively, their success depends on parameter tuning. In this study was examined how the GA parameters affect schedule synchronization, using a stochastic simulation model to evaluate the fitness of alternative solutions. It is found that the larger population sizes improve solution quality, and higher crossover probabilities enhance overall performance. Conversely, increasing mutation rates or the number of attempted mutations reduces the algorithm’s ability to converge on optimal timetables. Those findings provide researchers and practitioners with data-driven insights for efficiently deploying GAs in public transit optimization.

Keywords: public transport timetables, network synchronization, genetic algorithm, parameter tuning
Grants and funding:

The authors received no financial support for the research, authorship and/or publication of this article.

Conflicts of interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Received: June 1, 2026; Accepted: September 14, 2026; Prepublished online: September 14, 2026 

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