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

Öz

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.

Anahtar Kelimeler

• 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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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