Toplu Taşımada Yolcu Yoğunluğuna Dayalı Uyarlanabilir Zaman Planlaması: Trabzon Kampüs Hatları Üzerine Bir Uygulama

Abstract

Bu çalışma, Karadeniz Teknik Üniversitesi Kanuni Kampüsü'ne hizmet veren toplu taşıma sisteminde operasyonel verimliliği artırmayı amaçlamakta ve yolcu yoğunluğuna dayalı olarak sefer planlamasının yeniden yapılandırılmasını önermektedir. Trabzon'da kentsel ulaşım verilerinin sistematik biçimde analiz edilmemesi ve sınıflandırılmaması, etkili hizmet tasarımını zorlaştırmaktadır. Bu sorunu ele almak amacıyla, yolcu biniş verileri analiz edilerek, gerçek talebe uygun şekilde sefer sıklığı ve otobüs tahsisi optimize edilmiştir. Özellikle ders saatleri çevresinde yaşanan yoğunluk değişimlerini yansıtacak şekilde, zaman dilimleri K-Means kümeleme algoritması kullanılarak dinamik olarak bölümlendirilmiştir. Her bir tanımlanan küme için gerekli sefer sayısı ve otobüs ihtiyacı hesaplanmıştır. Geleneksel sabit zamanlı planlama yaklaşımlarının aksine, bu yöntem her bir otobüs hattı ve yönünün özgün özelliklerine göre zaman dilimlerini uyarlayarak esnek ve yanıt verebilen bir planlama imkânı sunmaktadır. Elde edilen sonuçlar, önerilen kümeleme tabanlı yaklaşımın, hizmet kalitesinden ödün vermeksizin hem maksimum hem de ortalama otobüs sayısını etkili bir şekilde azalttığını göstermektedir. Bu esnek planlama stratejisi operasyonel verimliliği artırmakta ve benzer kentsel ulaşım bağlamlarına uyarlanabilecek pratik bir çerçeve sunmaktadır.

Keywords

• Veri analizi
• Sefer sıklığı optimizasyonu
• Uyarlanabilir zaman planlaması
• K-means kümeleme algoritması

Adaptive Time Scheduling Based on Passenger Density in Public Transportation: A Case Study on Trabzon Campus Routes

Abstract

This study aims to improve operational efficiency in public transportation services to the Kanuni Campus of Karadeniz Technical University by restructuring trip planning based on passenger density. Currently, the lack of systematic analysis and classification of urban transportation data in Trabzon hinders effective service design. To address this issue, passenger boarding data were analyzed to optimize trip frequency and bus allocation in response to actual demand. The K-Means clustering algorithm was employed to segment time intervals dynamically according to boarding densities, particularly reflecting fluctuations around class hours. For each identified cluster, the required number of trips and buses was calculated. Unlike traditional fixed scheduling approaches, this method enables flexible and responsive planning by adapting time segments to the specific characteristics of each bus line and direction. The results show that the proposed clustering-based approach effectively reduces both the maximum and average number of buses needed while maintaining service quality. This flexible scheduling strategy enhances operational efficiency and offers a practical framework adaptable to other urban transit contexts.

Keywords

• Data analysis
• Frequency optimization
• Adaptive time scheduling
• K-means clustering algorithm

References

1. Aggelis, V., & Christodoulakis, D. (2005). RFM analysis for decision support in e-banking area. WSEAS Transactions on Computers, 4(8), 943–950.
2. 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/10.1080/21680566.2024.2318566
3. Alçura, G. A. (2024). On the road to inclusion: A multifaceted examination of transportation challenges faced by individuals with disabilities. Sustainability, 17(1), 81. https://doi.org/10.3390/su17010081
4. 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.
5. Benli, A., & Akgün, İ. (2023). A multi-objective mathematical programming model for transit network design and frequency setting problem. Mathematics, 11(21), 4488.
6. 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.
7. Caetano, J. A., Clímaco, F. G. N., Ribeiro, G. M., & Bahiense, L. (2025a). Frequency-setting models: A literature review of costs and sustainability on bus routes. Public Transport, 19(1), 1–24.
8. Caetano, J. A., Clímaco, F. G. N., Ribeiro, G. M., & Bahiense, L. (2025b). New mathematical modeling to optimize the frequency of bus routes. IEEE Access.

9. 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.
10. Ceder, A. (2007). Public transit planning and operation: Theory, modeling, and practice. Elsevier/Butterworth-Heinemann.
11. 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.
12. Chen, Y., & Shan, T. (2021). Research on flexible public transportation planning based on node importance clustering. In CICTP 2021 (pp. 1800–1807).
13. Çeçen, M. Y., & Tosun, H. B. (2024). Diyarbakır toplu taşıma sisteminde otobüs kullanımının incelenmesi ve iyileştirme önerileri. Black Sea Journal of Engineering and Science, 7(2), 316–322.
14. Durán-Micco, J., & Vansteenwegen, P. (2022). Transit network design considering link capacities. Transport Policy, 127, 148–157.
15. 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.
16. Gkiotsalitis, K., Schmidt, M., & van der Hurk, E. (2022). Subline frequency setting for autonomous minibuses under demand uncertainty. Transportation Research Part C: Emerging Technologies, 135, 103492.
17. Hajizadeh, E., & Shahrabi, J. (2010). Application of data mining techniques in stock markets: A survey. Journal of Economics and International Finance, 7, 109–118.
18. 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.
19. 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.
20. Kim, M., Kho, S. Y., & Kim, D. K. (2019). A transit route network design problem considering equity. Sustainability, 11(13), 3527.
21. 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.
22. 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.
23. 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.
24. Mirza, A. M., & Jain, R. K. (2025). Review of public transportation integration and modeling strategies: Toward seamless urban mobility. Multidisciplinary Reviews, 8(1), 2025018.
25. Mutlu, M. M., Aksoy, İ. C., & Alver, Y. (2021). COVID-19 transmission risk minimization at public transportation stops using differential evolution algorithm.
26. 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.
27. Orange Data Mining. (2025, September 9). Orange Data Mining. https://orangedatamining.com/
28. 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.
29. 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.
30. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
31. Su, Y., & Yang, H. (2025). Enhancing feeder bus service coverage with Multi-Agent Reinforcement Learning: A case study in Hong Kong. Transportation Research Part E: Logistics and Transportation Review, 196, 103997.
32. Tekin, B. (2018). Ward, k-ortalamalar ve iki adımlı kümeleme analizi yöntemleri ile finansal göstergeler temelinde hisse senedi tercihi. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 21(40), 401-436.
33. 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.
34. Urbano, V. M., Arena, M., & Azzone, G. (2025). Big data for decision-making in public transport management: A comparison of different data sources. Research in Transportation Business & Management, 59, 101298.
35. Verbas, İ. Ö., & Mahmassani, H. S. (2015). Exploring trade-offs in frequency allocation in a transit network using bus route patterns: Methodology and application to large-scale urban systems. Transportation Research Part B: Methodological, 81, 577–595.
36. 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. https://doi.org/10.3390/futuretransp1020015
37. Wei, Z., Hu, Y., Chen, Y., & Wang, T. (2025). Optimized design of cultural space in Wuhan Metro: Analysis and reflection based on multi-source data. Buildings, 15(13), 2201.
38. 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.