Toplu Taşıma Araçları Sefer Sıklığı Belirleme ve Çizelgeleme Problemi

Yıl 2023, Cilt: 16 Sayı: 2, 1310 - 1333, 15.06.2023

Kısmet Cingöz , Tevfik Aytemiz

https://doi.org/10.35674/kent.1134312

Cited By: 1

Öz

Günümüzde dünya nüfusunun artması, kentleşme oranının artması ve kıt kaynakların azalması çevresel farkındalığın artmasına neden olmaktadır. İnsanlar her gün evlerine, işlerine, okullarına gitmek ve sosyal aktivitelerde bulunmak için hareket etmektedirler yani şehir içinde yer değiştirmektedirler. Bu da kişisel araç, taksi, otobüs, minibüs kullanımının artmasına sebep olmaktadır. Yetkililer ise bu hareket veya dolaşım esnasında oluşan çevre kirliliğinin, gürültünün, karbon salınımının ve kazaların azalmasını sağlamak amacıyla toplu taşıma hizmetlerinin daha kullanılabilir hale gelmesi için çaba sarf etmektedirler. Toplu taşıma hizmetlerinin kullanımını arttırmak için kullanıcılara yani vatandaşlara daha sık, daha düzenli ve daha güvenilir hizmet verilmesi gerekmektedir. Toplu taşıma hizmetlerinin düzenlenmesi ile ilgili yapılan faaliyetler literatürde toplu taşıma ağ planlama süreci olarak adlandırılmaktadır. Toplu taşıma ağ planlama süreci, sırasıyla ağ planlama, sıklık oluşturma ve zaman çizelgesi geliştirme, araç çizelgeleme ve sürücü çizelgeleme faaliyetlerinden oluşmaktadır ve ağ planlama sürecinin her bir aşaması kendisinden sonra gelen aşamanın girdisi olmaktadır. Ağ tasarım faaliyetleri oldukça maliyetli olduğu ve alt yapı çalışmaları gerektirdiği için sefer sıklıklarını değiştirmek hizmet sağlayıcıları için daha kolaydır. Sefer sıklığı oluşturma ve zaman çizelgesi geliştirme aşaması kullanıcıların konforunu dikkate alan ve hizmet kalitesi ile ilgili olan planlama aşamasıdır. Bu sebeple, toplu taşıma kullanımının arttırılması üzerinde önemle durulması gereken bir konudur. Bu çalışmanın amacı, toplu taşıma sefer sıklığı ayarlama ve zaman çizelgesi oluşturma problemlerini çözmeye yönelik metodolojik genel bir bakış ve örnekler sunmaktır.

Anahtar Kelimeler

Toplu taşıma, Toplu taşıma ağ planlama süreci, Sıklık ayarlama ve zaman çizelgesi geliştirme

Frequency Determination and Timetable Development Problem of Public Transit Vehicles

Öz

Today, the increase in the world population and the rate of urbanization as well as the decrease in scarce resources cause an increase in environmental awareness. People constantly move for different purposes such as going to their house, school, work and performing social activities. It causes usage of private car, taxi, bus, minibuses increase. Authorities are trying to make services of public transport vehicles more available on the purpose of reducing the environmental pollution, noise pollution, carbon emission and accidents which are caused during the movements in the city. It is necessary to provide more frequent, more regular and more reliable services to users (i.e., citizens) in order to increase the use of public transport services. The activities committed relating to the arrangement of public transportation services are named as public transportation network planning process in the literature. As the literature assesses, the public transit planning process is usually divided in a sequence of four steps: network design, frequencies setting and timetable development, the bus scheduling and the driver scheduling. The output of each activity positioned higher in the sequence becomes an important input for lower-level decisions. Since network design activities are very costly and require infrastructure investments, it is easier for service providers to change the frequency of busses The stage of creating a bus frequency and developing a timetable is the planning stage that takes into account the comfort of the users and is related to the service quality. For this reason, increasing the use of public transport is an issue that should be emphasized. In this direction, the purpose of this study is to provide an overview and examples of certain practical methodologies aimed at solving the public transit frequency setting and scheduling problems.

Anahtar Kelimeler

Public transportation, Public transit network planning process, Frequency setting and timetable development

Kaynakça

• AlKheder, S., AlRukaibi, F., & Zaqzouq, A. (2018). Optimal bus frequency for Kuwait public transportation company: A cost view. Sustainable cities and society, 41, 312-319.
• Bagloee, S. A., & Ceder, A. A. (2011). Transit-network design methodology for actual-size road networks. Transportation Research Part B: Methodological, 45(10), 1787-1804.
• Bertsimas, D., Sian Ng, Y., & Yan, J. (2020). Joint frequency-setting and pricing optimization on multimodal transit networks at scale. Transportation Science, 54(3), 839-853.
• Bunte, S., & Kliewer, N. (2009). An overview on vehicle scheduling models. Public Transport, 1(4), 299-317.
• Carraresi, P., & Gallo, G. (1984). Network models for vehicle and crew scheduling. European Journal of Operational Research, 16(2), 139-151.
• Ceder, A. (1984). Bus frequency determination using passenger count data. Transportation Research Part A: General, 18(5-6), 439-453.
• Ceder, A. (1986). Methods for creating bus timetables. Transportation Research Part A: General, 21(1), 59-83.
• Ceder, A. (2001). Operational objective functions in designing public transport routes. Journal of advanced transportation, 35(2), 125-144.
• Ceder, A. (2002). Urban transit scheduling: framework, review and examples. Journal of urban planning and development, 128(4), 225-244.
• Ceder, A. (2007). Public transit planning and operation: Modeling, practice and behavior. Oxford: CRC press.
• Ceder, A., & Israeli, Y. (1992). Scheduling considerations in designing transit routes at the network level. In Computer-Aided Transit Scheduling İçinde (pp. 113-136). Springer, Berlin, Heidelberg.
• Ceder, A. & Wilson, N. H. M. (1986). Bus network design. Transportation Research Part B 20, 331–344.
• Chakroborty, P. (2003). Genetic algorithms for optimal urban transit network design. Computer‐Aided Civil and Infrastructure Engineering, 18(3), 184-200.
• Chen, H. (2007). Stochastic optimization in computing multiple headways for a single bus line. Journal of the Chinese Institute of Industrial Engineers, 24(5), 351-359.
• Constantin, I., & Florian, M. (1995). Optimizing frequencies in a transit network: a nonlinear bi-level programming approach. International Transactions in Operational Research, 2(2), 149-164.
• Dantzig, G. B., Harvey, R. P., Lansdowne, Z. F., Robinson, D. W., & Maier, S. F. (1979). Formulating and solving the network design problem by decomposition. Transportation Research Part B: Methodological, 13(1), 5-17.
• de Weert, Y., & Gkiotsalitis, K. (2021). A covid-19 public transport frequency setting model that includes short-turning options. Future Transportation, 1(1), 3-20.
• Dell’Olio, L., Ibeas, A., & Ruisánchez, F. (2012). Optimizing bus-size and headway in transit networks. Transportation, 39(2), 449-464.
• Demirkollu, M. (2017). Hedef programlama yöntemi ile otobüs sefer sayılarının tespit edilmesi. Yayımlanmamış Yüksek Lisans Tezi, Pamukkale Üniversitesi: Denizli.
• Deri, A. (2012). Akıllı kart verileri kullanılarak toplu ulaşım yolculuk talebinin belirlenmesi ve sefer çizelgeleme optimizasyonu, Yayımlanmamış Yüksek Lisans Tezi, Dokuz Eylül Üniversitesi: İzmir.
• Desale, S., Rasool, A., Andhale, S., & Rane, P. (2015). Heuristic and meta-heuristic algorithms and their relevance to the real world: a survey. International Journal of Computer Engineering in Ressearch Trends, 2(5), 296-304.
• Desaulniers, G. & Hickman, M. D. (2007). Public transit. (Barnhart, C. & Laporte, G.). Handbooks in Operations Research and Management Science: Transportation İçinde(ss. 69-127). North Holland: Elsevier.
• Fan, W., & Machemehl, R. B. (2006). Optimal transit route network design problem with variable transit demand: genetic algorithm approach. Journal of Transportation Engineering, 132(1), 40-51.
• Farahani, R. Z., Miandoabchi, E., Szeto, W. Y., & Rashidi, H. (2013). A review of urban transportation network design problems. European Journal of Operational Research, 229(2), 281-302.
• Friesz, T. L. (1985). Transportation network equilibrium, design and aggregation: key developments and research opportunities. Transportation Research Part A: General, 19(5-6), 413-427.
• Furth, P. G., & Wilson, N. H. (1981). Setting frequencies on bus routes: Theory and practice. Transportation Research Record, 818(1981), 1-7.
• Gkiotsalitis, K., & Cats, O. (2017). Exact optimization of bus frequency settings considering demand and trip time variations. In 96th Transportation research board annual meeting.
• Grosfeld-Nir, A., & Bookbinder, J. H. (1995). The planning of headways in urban public transit. Annals of Operations Research, 60(1), 145-160.
• Guihaire, V., & Hao, J. K. (2008). Transit network design and scheduling: A global review. Transportation Research Part A: Policy and Practice, 42(10), 1251-1273.
• Hadas, Y., & Shnaiderman, M. (2012). Public-transit frequency setting using minimum-cost approach with stochastic demand and travel time. Transportation Research Part B: Methodological, 46(8), 1068-1084.
• Huang, Z., Ren, G., & Liu, H. (2013). Optimizing bus frequencies under uncertain demand: case study of the transit network in a developing city. Mathematical problems in Engineering, 1-10.
• 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.
• Ibarra-Rojas, O. J., Giesen, R., & Rios-Solis, Y. A. (2014). An integrated approach for timetabling and vehicle scheduling problems to analyze the trade-off between level of service and operating costs of transit networks. Transportation Research Part B: Methodological, 70, 35-46.
• Ibeas, A., Alonso, B., dell’Olio, L., & Moura, J. L. (2013). Bus size and headways optimization model considering elastic demand. Journal of Transportation Engineering, 140(4), 04013021.
• Jiang, Y. (2022). Reliability-based equitable transit frequency design. Transportmetrica A: Transport Science, 18(3), 879-909.
• Kepaptsoglou, K., & Karlaftis, M. (2009). Transit route network design problem: review. Journal of transportation engineering, 135(8), 491-505.
• Kokash, N. (2005). An introduction to heuristic algorithms. Department of Informatics and Telecommunications, 1-8.
• Li, Y., Xu, W., & He, S. (2013). Expected value model for optimizing the multiple bus headways. Applied Mathematics and Computation, 219(11), 5849-5861.
• Luhua, S., Yin, H., & Xinkai, J. (2011). Study on method of bus service frequency optimal model based on genetic algoritm. Procedia Environmental Sciences, 10, 869-874.
• Martínez, H., Mauttone, A., & Urquhart, M. E. (2014). Frequency optimization in public transportation systems: Formulation and metaheuristic approach. European Journal of Operational Research, 236(1), 27-36.
• Mohaymany, A. S., & Amiripour, S. M. (2009). Creating bus timetables under stochastic demand. International Journal of Industrial Engineering, 20(3), 83-91.
• Osman, I. H., & Laporte, G. (1996). Metaheuristics: A bibliography. Annals of Operational Research, 63, 513-628.
• Owais, M., Moussa, G., Abbas, Y., & El-Shabrawy, M. (2013). Optimal frequency setting for circular bus routes in Urban Areas. Journal of Engineering Sciences, 41(5), 1796-1811.
• Owais, M., Osman, M. K., & Moussa, G. (2015). Multi-objective transit route network design as set covering problem. IEEE Transactions on Intelligent Transportation Systems, 17(3), 670-679.
• Özcan, T. (2018). Kentiçi toplu taşıma sistemlerinde sefer sıklığı optimizasyonu. Yayımlanmamış Yüksek Lisans Tezi, Pamukkale Üniversitesi: Denizli.
• Park, S. J. (2005). Bus network scheduling with genetic algorithms and simulation (Doctoral dissertation). University of Maryland, Maryland.
• Pathak, P., Agrawal, K., Suman, H. K., & Bolia, N. B. (2020). Frequency optimization-based approach for reducing crowding discomfort in Delhi bus system. Procedia Computer Science, 170, 265-272.
• Polat, U., Sağbaş, A., & Dermenci, M. S. Sürdürülebilir ulaşım planlaması için şehir içi otobüs hatlarında sefer çizelgeleme optimizasyonu. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 27(3), 1099-1116.
• Ruiz, M., Segui-Pons, J. M., & Mateu-LLadó, J. (2017). Improving Bus Service Levels and social equity through bus frequency modelling. Journal of Transport Geography, 58, 220-233.
• Salzborn, F. J. (1972). Optimum bus scheduling. Transportation Science, 6(2), 137-148.
• Schéele, S. (1980). A supply model for public transit services. Transportation Research Part B: Methodological, 14(1-2), 133-146.
• Stegherr, H., Heider, M., & Hähner, J. (2020). Classifying metaheuristics: Towards a unified multi-level classification system. Natural Computing, 1-17.
• Tom, V. M., & Mohan, S. (2003). Transit route network design using frequency coded genetic algorithm. Journal of transportation engineering, 129(2), 186-195.
• Uludağ, N. (2010). Bulanık optimizasyon ve doğrusal hedef programlama yaklaşımları ile otobüs hatlarının modellenmesi. Yayımlanmamış Doktora Tezi, Pamukkale Üniversitesi: Denizli.
• Van Oudheusden, D. L., & Zhu, W. (1995). Trip frequency scheduling for bus route management in Bangkok. European Journal of Operational Research, 83(3), 439-451.
• Verbas, İ. Ö., & Mahmassani, H. S. (2015). Integrated frequency allocation and user assignment in multimodal transit networks: methodology and application to large-scale urban systems. Transportation Research Record, 2498(1), 37-45.
• Verbas, İ. Ö., Frei, C., Mahmassani, H. S., & Chan, R. (2015). Stretching resources: sensitivity of optimal bus frequency allocation to stop-level demand elasticities. Public Transport, 7(1), 1-20.
• Vuchic, V. R. (2005). Urban transit: operations, planning and economics. New Jersey: John Wiley & Sons.,
• Yoo, G. S., Kim, D. K., & Chon, K. S. (2010). Frequency design in urban transit networks with variable demand: model and algorithm. KSCE Journal of Civil Engineering, 14(3), 403-411.
• Yu, B., Yang, Z., & Yao, J. (2009). Genetic algorithm for bus frequency optimization. Journal of Transportation Engineering, 136(6), 576-583.
• Zuo, X., Chen, C., Tan, W., & Zhou, M. (2014). Vehicle scheduling of an urban bus line via an improved multiobjective genetic algorithm. IEEE Transactions on intelligent transportation systems, 16(2),1030-104.