Analysis the Impact of Parameters on the Effectiveness genetic Algorithm Application in Solving Synchronizing Public Transport Schedules
DOI:
https://doi.org/10.32515/2664-262X.2026.13(44).424-432Keywords:
public transport, transport management, synchronizing public transport schedules, genetic algorithm, GA parametersAbstract
The research is devoted to the study of the genetic algorithm's (GA) functional features as a tool for solving the synchronizing public transport schedules problem.
Timetable synchronization is a complex combinatorial optimization problem characterized by random parameters of public transport demand and the large size of the transport network system. Therefore, heuristic and metaheuristic methods are most often used to solve this problem, one of which is a genetic algorithm. Despite the significant potential of using a genetic algorithm for synchronizing traffic schedules, achieving optimal performance indicators directly depends on the reasonable determination of GA parameters. The use of suboptimal parameters can lead to an unreasonable increase in computation time and premature convergence, which reduces the practical usefulness of synchronizing public transport schedules based on a genetic algorithm.
The purpose of the study is to improving the effectiveness of the genetic algorithm for solving the problem of traffic synchronization at transfer node by GA parameters settings.
The following GAs parameters were selected as the object of study: population size, crossover rate, mutation rate, and selection mechanisms The analysis of the impact of GA parameters on the quality of the schedule was carried out using a simulation model. The Avtovakzal transfer node in the city of Verkhnodniprovsk, Dnipropetrovsk region, Ukraine, was used to apply the simulation model.
Тhe results researchof the analysis the sensitivity of the average passenger waiting time to GA parameters are presented. It was concluded that the quality of synchronizing schedules is influenced by the population size and crossover probability, while the mutation probability and number of mutation attempts do not have a statistically significant impact on the results of the genetic algorithm within the scope of this study.
References
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2. Tuzun, A., & Yılmaz, S. (2014). Transit coordination with heterogeneous headways. Transportation Planning and Technology, 37(5). https://doi.org/10.1080/03081060.2014.912419
3. Naumov, V., & Samchuk, G. (2017). Class library for simulations of passenger transfer nodes as elements of the public transport system . Procedia Engineering, 187, 77-81. https://doi.org/10.1016/j.proeng.2017.04.352
4. Ataeian, S., Solimanpur, M., Amiripour, S., & Shankar, R. (2021). Synchronized timetables for bus rapid transit networks in small and large cities. Scientia Iranica, 28(1), 477-491. https://doi.org/10.24200/sci.2019.51501.2220
5. Naeini, H., Shafahi, Y., & Taherkhani, M. (2022). Optimizing and synchronizing timetable in an urban subway network with stop-skip strategy. Journal of Rail Transport Planning & Management, 22. https://doi.org/10.1016/j.jrtpm.2022.100301
6. Kang, L., Wu, J., Sun, H., Zhu, X., & Wang, B. (2015). A practical model for last train rescheduling with train delay in urban railway transit networks. Omega, 50. https://doi.org/10.1016/j.omega.2014.07.005
7. Cao, Z., Ceder, A., Li, D., & Zhang, S. (2019). Optimal synchronization and coordination of actual passenger-rail timetables. Journal of Intelligent Transportation Systems, 231-249. https://doi.org/10.1080/15472450.2018.1488132
8. Wang, Y., Li, D., & Cao, Z. (2020). Integrated timetable synchronization optimization with capacity constraint under time-dependent demand for a rail transit network. Computers & Industrial Engineering, 142. https://doi.org/10.1016/j.cie.2020.106374
9. Chen, Y., Mao, B., Bai, Y., Ho, T., & Li, Z. (2019). Timetable synchronization of last trains for urban rail networks with maximum accessibility. Transportation Research Part C: Emerging Technologies, 99(2), 110-129. https://doi.org/10.1016/j.trc.2019.01.003
10. Kapica, D., Melnikova, Y., & Naumov, V. (2025). Synchronization in public transportation: A review of challenges and techniques. Future Transportation, 5(1), 6. https://doi.org/10.3390/futuretransp5010006
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