Adaptive Time-Based Clustering for Optimizing Bus Fleet Management: A Case Study on İzmir’s Public Transportation System

Yıl 2025, Cilt: 10 Sayı: 2, 362 - 387, 11.11.2025

Nurhan Dudaklı , Behice Meltem Kayhan , Ümit Kuvvetli

https://doi.org/10.26650/JTL.2025.1609360

Öz

This study introduces an adaptive time-based clustering strategy to optimize bus fleet requirements in public transportation systems by leveraging passenger boarding data from İzmir's network. Addressing key challenges in scheduling and fleet sizing, the proposed method uses the K-Means clustering algorithm to segment boarding densities into optimally determined time intervals specific to each bus line and direction. By adapting the number and boundaries of time intervals to actual demand patterns across weekdays and weekends, the model offers a more responsive and efficient allocation of fleet resources. The results demonstrate that the adaptive clustering approach significantly outperforms the conventional fixed-interval strategy, reducing both maximum and average bus requirements while maintaining service quality. This improvement is especially notable for high-demand or highly variable lines, where resource flexibility is critical. While the study shows promising results, it also acknowledges limitations such as the exclusion of passenger waiting times and the diversity of the fleet composition. Future research may include integrating alternative clustering algorithms, incorporating alighting data, and developing multi-criteria operational planning models. These enhancements will further support the evolution of data-driven, adaptive planning tools for more efficient and sustainable urban transport systems.

Anahtar Kelimeler

Public Transportation Optimization , Adaptive Clustering , Frequency setting , Bus Fleet Management

Etik Beyan

This study does not require the approval of an ethics committee.

Destekleyen Kurum

The authors declared no financial support.

Teşekkür

The authors thank ESHOT Genel Müdürlüğü, Ulaşım Planlama Dairesi Başkanlığı, and İstatistik Şube Müdürlüğü for their support and data provision.

Kaynakça

• Aditi, A. D., Dureja, A., Abrol, S., & Dureja, A. (2020). Prediction of ticket prices for public transport using linear regression and random forest regression methods: A practical approach using machine learning. In U. Batra, N. Roy, & B. Panda (Eds.), Data science and analytics (pp. 140–150). Springer. https://doi.org/10.1007/978-981-15-5827-6_12 google scholar
• Aksoy, İ. C., & Alver, Y. (2024). Addressing electric transit network design frequency setting problem with dynamic transit assignment. Transportmetrica B: Transport Dynamics, 12(1), 2318566. https://doi.org/10.1080/21680566.2023.2318566 google scholar
• Aksoy, İ. C., & Mutlu, M. M. (2024). Comparing the performance of metaheuristics on the transit network frequency setting problem. Journal of Intelligent Transportation Systems, 1–20. google scholar
• Bagheri, K., Samany, N. N., Toomanian, A., Jelokhani‐Niaraki, M., & Hajibabai, L. (2024). A planar graph cluster‐routing approach for optimizing medical waste collection based on spatial constraint. Transactions in GIS. google scholar
• Benli, A., & Akgün, İ. (2023). A multi-objective mathematical programming model for transit network design and frequency setting problem. Mathematics, 11(21), 4488. https://doi.org/10.3390/math11214488 google scholar
• Bertsimas, D., Ng, Y. S., & Yan, J. (2020). Joint frequency-setting and pricing optimization on multimodal transit networks at scale. Transportation Science, 54(3), 839–853. https://doi.org/10.1287/trsc.2019.0939 google scholar
• Branda, F., Marozzo, F., & Talia, D. (2020). Ticket sales prediction and dynamic pricing strategies in public transport. Big Data and Cognitive Computing, 4(4), 36. https://doi.org/10.3390/bdcc4040036 google scholar
• Carrel, A., Lau, P. S., Mishalani, R. G., Sengupta, R., & Walker, J. L. (2015). Quantifying transit travel experiences from the users’ perspective with high-resolution smartphone and vehicle location data: Methodologies, validation, and example analyses. Transportation Research Part C: Emerging Technologies, 58, 224–239. google scholar
• Ceder, A. (2007). Public transit planning and operation: Theory, modeling and practice. Elsevier, Butterworth-Heinemann. google scholar
• Charris, E. L. S., Montoya-Torres, J. R., & Guerrero-Rueda, W. (2019). A decision support system for technician routing with time windows: A case study of a Colombian public utility company. Academia Revista Latinoamericana de Administración, 32(2), 138–158. google scholar
• Chen, Y., & Shan, T. (2021). Research on flexible public transportation planning based on node importance clustering. In CICTP 2021 (pp. 1800–1807). google scholar
• dell’Olio, L., Ibeas, A., de Oña, J., & de Oña, R. (2018). Chapter 9 – Data mining approaches. In L. dell’Olio, A. Ibeas, J. de Oña, & R. de Oña (Eds.), Public transportation quality of service (pp. 155–179). Elsevier. http://dx.doi.org/10.1016/B978-0-08-102080-7.00009-4 google scholar
• Gadepalli, R., Bansal, P., Tiwari, G., & Bolia, N. (2024). A tactical planning framework to integrate paratransit with formal public transport systems. Transportation Research Part D: Transport and Environment, 136, 104438. google scholar
• Garrido, C., de Oña, R., & de Oña, J. (2014). Neural networks for analyzing service quality in public transportation. Expert Systems with Applications, 41, 6830–6838. http://dx.doi.org/10.1016/j.eswa.2014.04.045 google scholar
• Gkiotsalitis, K., Schmidt, M., & van der Hurk, E. (2022). Subline frequency setting for autonomous minibusses under demand uncertainty. Transportation Research Part C: Emerging Technologies, 135, 103492. google scholar
• Ibarra-Rojas, O. J., Delgado, F., Giesen, R., & Muñoz, J. C. (2015). Planning, operation, and control of bus transport systems: A literature review. Transportation Research Part B: Methodological, 77, 38–75. google scholar
• Ibarra-Rojas, O. J., Muñoz, J. C., Giesen, R., & Knapp, P. (2019). Integrating frequency setting, timetabling, and route assignment to synchronize transit lines. Journal of Advanced Transportation, 2019(1), 9408595. google scholar
• Jara-Díaz, S., Fielbaum, A., & Gschwender, A. (2017). Optimal fleet size, frequencies and vehicle capacities considering peak and off-peak periods in public transport. Transportation Research Part A: Policy and Practice, 106, 65–74. google scholar
• Jing, D., Yao, E., Chen, R., & Sun, X. (2023). Optimal design method of public transit network considering transfer efficiency. IET Intelligent Transport Systems, 17(6), 1118–1136. google scholar
• Kim, M., Kho, S. Y., & Kim, D. K. (2019). A transit route network design problem considering equity. Sustainability, 11(13), 3527. google scholar
• Lee, I., Cho, S. H., Kim, K., Kho, S. Y., & Kim, D. K. (2022). Travel pattern-based bus trip origin-destination estimation using smart card data. PLOS ONE, 17(6), e0270346. google scholar
• Ma, X., Wu, Y. J., Wang, Y., Chen, F., & Liu, J. (2013). Mining smart card data for transit riders’ travel patterns. Transportation Research Part C: Emerging Technologies, 36, 1–12. google scholar
• Mayaud, J. R., Tran, M., & Nuttall, R. (2019). An urban data framework for assessing equity in cities: Comparing accessibility to healthcare facilities in Cascadia. Computers, Environment and Urban Systems, 78, 101401. https://doi.org/10.1016/j.compenvurbsys.2019. 101401 google scholar
• Mete, S., Çelik, E., & Gül, M. (2022). Predicting the time of bus arrival for public transportation by time series models. Journal of Transportation and Logistics, 7(2), 541–555. google scholar
• Mirza, A. M., & Jain, R. K. (2025). Review of public transportation integration and modeling strategies: Toward seamless urban mobility. Multidisciplinary Reviews, 8(1), 2025018–2025018. google scholar
• Mutlu, M. M., Aksoy, İ. C., & Alver, Y. (2021). COVID-19 transmission risk minimization at public transportation stops using Differential Evolution algorithm. [Manuscript submitted for publication]. google scholar
• Mutlu, M. M., Aksoy, İ. C., & Alver, Y. (2022). Transit frequency optimization in bi-modal networks using differential evolution algorithm. Teknik Dergi, 33(5), 12601–12616. google scholar
• Oliveira, J. L. A., Aquino, A. L., Pinheiro, R. G., & Nogueira, B. (2024). Optimizing public transport system using biased random-key genetic algorithm. Applied Soft Computing, 158, 111578. google scholar
• Pei, J., Zhong, K., Li, J., & Yu, Z. (2022). PAC: Partial area clustering for re-adjusting the layout of traffic stations in city’s public transport. IEEE Transactions on Intelligent Transportation Systems, 24(1), 1251–1260. google scholar
• Pelletier, M. P., Trépanier, M., & Morency, C. (2011). Smart card data use in public transit: A literature review. Transportation Research Part C: Emerging Technologies, 19(4), 557–568. google scholar
• Pojani, D., & Stead, D. (2015). Sustainable urban transport in the developing world: Beyond megacities. Sustainability, 7(6), 7784–7805. google scholar
• Purdy, M., & Daugherty, P. (2017). How AI boosts industry profits and innovation. Accenture Ltd. google scholar
• Shalit, N., Fire, M., & Ben-Elia, E. (2020). Imputation of missing boarding stop information in smart card data with machine learning methods. In Intelligent Data Engineering and Automated Learning – IDEAL 2020 (pp. 17–27). https://doi.org/10.1007/978-3-030-62362-3_3 google scholar
• Sosnowska, J., & Skibski, O. (2018). Path evaluation and centralities in weighted graphs—An axiomatic approach. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (pp. 3856–3862). google scholar
• Tian, Q., Wang, D. Z., & Lin, Y. H. (2021). Service operation design in a transit network with congested common lines. Transportation Research Part B: Methodological, 144, 81–102. google scholar
• Tran, L., Mun, M., Lim, M., Yamato, J., Huh, N., & Shahabi, C. (2020). DeepTRANS: A deep learning system for public bus travel time estimation using traffic forecasting. Proceedings of the VLDB Endowment, 13, 2957–2960. https://doi.org/10.14778/3415478.3415518 google scholar
• UITP Asia Pacific Centre for Transport Excellence (CTE). (2020). Artificial intelligence in mass public transport: Executive summary. https:// cms.uitp.org/wp/wp-content/uploads/2020/08/UITP-AP-CTE-AI-in-PT-Executive-Summary-Dec-2018_0.pdf google scholar
• Vlachopanagiotis, T., Grizos, K., Georgiadis, G., & Politis, I. (2021). Public transportation network design and frequency setting: Pareto optimality through alternating-objective genetic algorithms. Future Transportation, 1(2), 248–267. google scholar
• Wei, M., Liu, T., Sun, B., & Jing, B. (2020). Optimal integrated model for feeder transit route design and frequency‐setting problem with stop selection. Journal of Advanced Transportation, 2020(1), 6517248. google scholar
• Xie, Y., Guo, Y., Zhou, T., Mi, Z., Yang, Y., Sadoun, B., & Obaidat, M. S. (2020). A strategy to alleviate rush hour traffics in urban areas based on school-trip commute information. IEEE Systems Journal, 15(2), 2630–2641. google scholar
• Yuan, Y., Shao, C., Cao, Z., He, Z., Zhu, C., Wang, Y., & Jang, V. (2020). Bus dynamic travel time prediction: Using a deep feature extraction framework based on RNN and DNN. Electronics, 9(11), 1876. https://doi.org/10.3390/electronics9111876 google scholar