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Forecasting Ro-Ro Freight Transportation Demand at Samsun Port: A Hybrid Method Approach

Year 2024, In Press Articles, 1 - 18

Tayfun Şimşek Fırat Sivri Özkan Uğurlu Mehmet Aydın

https://doi.org/10.52998/trjmms.1383848

Abstract

ABSTRACT

Türkiye's extensive coastline and geopolitics position necessitates the importance of Ro-Ro
transportation with neighbouring countries. Türkiye's rapidly growing Ro-Ro transportation
significantly contributes to imports and exports, which is of great importance to the national economy.
Samsun Port is one of the most active ports in Türkiye's Ro-Ro transportation sector, operating in the
Black Sea region. This study examined Ro-Ro transportation at Samsun Port, and future cargo
forecasting was conducted. For this purpose, artificial neural networks and time series analysis
methods were combined. Input variables used in the study included the number of Ro-Ro ships
arriving at the port between 2009 and 2021, population figures, a specialized CPI indicator (fresh
fruits and vegetables), and export values. The output variable was the amount of cargo carried by RoRo ships. According to the results obtained, it was observed that Samsun Port would have sufficient
capacity for Ro-Ro transportation in the next 27 months in terms of wharf, port area, and operational
space.

Keywords: Samsun Port, Ro-Ro transportation, Port capacity, Forecasting

Keywords

Samsun Port Ro-Ro transportation Port capacity Forecasting

References

  • Aksoy, S. (2011). Simulation modeling for Ro-Ro terminals (in Turkish). Master Thesis. İstanbul Technical University, Institute of Science and Technology, İstanbul.
  • Atlantis, (2022). Maritime Statistics, Ro-Ro Vehicle Statistics. Accessed Date: 13.12.2022, http://atlantis.udhb.gov.tr/istatistik/istatistik_roro.aspx is retrieved.
  • Başar, E., Erol, S., Yılmaz, H. (2015). Karadeniz Limanlarında Ro-Ro Taşımacılığı ve Gelişimi: Türk Deniz Ticareti Tarihi. Ordu Üniversitesi Sosyal Bilimler Araştırmaları Dergisi, 5(12), 71-82.
  • Box, G. E., Jenkins, G. M., Reinsel, G. C., Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
  • Czermański, E. (2017). Baltic shipping development trends in maritime spatial planning aspect. Studia i Materiały Instytutu Transportu i Handlu Morskiego, (14).
  • De Monie, G., Rodrigue, J. P., Notteboom, T. (2011). Economic cycles in maritime shipping and ports: the path to the crisis of 2008. Integrating seaports and trade corridors, 13-30.
  • Dragan, D., Keshavarzsaleh, A., Intihar, M., Popović, V., Kramberger, T. (2021). Throughput forecasting of different types of cargo in the adriatic seaport Koper. Maritime Policy & Management, 48(1), 19-45.
  • Eluyode, O. S., Akomolafe, D. T. (2013). Comparative study of biological and artificial neural networks. European Journal of Applied Engineering and Scientific Research, 2(1), 36-46.
  • Eskafi, M., Kowsari, M., Dastgheib, A., Ulfarsson, G. F., Stefansson, G., Taneja, P., Thorarinsdottir, R. I. (2021). A model for port throughput forecasting using Bayesian estimation. Maritime Economics & Logistics, 23, 348-368.
  • Guo, L., Yang, Z. (2019). Relationship between shipping accessibility and maritime transport demand: the case of mainland China. Networks and Spatial Economics, 19, 149-175.
  • Güzey, H. (2019). Capacity availability analysis for a seaport company (in Turkish). Master Thesis, Bursa Uludag University, Institute of Science and Technology, Bursa.
  • Gosasang, V., Yip, T. L., Chandraprakaikul, W. (2018). Long-term container throughput forecast and equipment planning: the case of Bangkok Port. Maritime Business Review, 3(1), 53-69.
  • Gökkuş, Ü., Yıldırım, M. S., Aydin, M. M. (2017). Estimation of container traffic at seaports by using several soft computing methods: a case of Turkish Seaports. Discrete Dynamics in Nature and Society, 2017.
  • Görçün, Ö. F., Görçün, Ö. (2018). Lojistik Maliyetler Çerçevesinde Karadeniz Limanlarının Multimodal Taşımacılığa Uygunluklarının Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (21), 65-80.
  • Jiang, C., Wan, Y., Zhang, A. (2017). Internalization of port congestion: strategic effect behind shipping line delays and implications for terminal charges and investment. Maritime Policy & Management, 44(1), 112-130.
  • Kabalcı, E. (2014). Artificial neural networks. Lecture Notes. Accessed Date: 01.12.2021, https://ekblc.files.wordpress.com/2013/09/ysa.pdf is retrieved.
  • Kadılar, C. (2005). SPSS Uygulamalı Zaman Serileri Analizine Giriş, Hacettepe University, Bizim Büro Basımevi, Ankara, 299s.
  • Kahveci, S. (2021). Ro-ro transport fleet optimization in the Black Sea Region: A sample application (in Turkish). Ph.D. Thesis, Karadeniz Technical University, Institute of Science and Technology, Trabzon.
  • Köstem, S. (2018). The political economy of Turkish-Russian relations: Dynamics of asymmetric interdependence. Perceptions: Journal of International Affairs, 23(2), 10-32.
  • Krenker, A., Bešter, J., Kos, A. (2011). Introduction to the artificial neural networks. Artificial Neural Networks: Methodological Advances and Biomedical Applications. InTech, 1-18.
  • Montgomery, D. C., Jennings, C. L., Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
  • Morales-Fusco, P., Saurí, S., Spuch, B. (2010). Quality indicators and capacity calculation for RoRo terminals. Transportation Planning and Technology, 33(8), 695-717.
  • Moscoso-López, J. A., Ruiz-Aguilar, J. J., Urda, D., González-Enrique, J., Turias, I. J. (2019). Ro-Ro Freight Forecasting Based on an ANN-SVR Hybrid Approach. Case of the Strait of Gibraltar. In Advances in Computational Intelligence: 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Gran Canaria, Spain, June 12-14, 2019, Proceedings, Part I 15 (pp. 818-831). Springer International Publishing.
  • Oğhan, S. (2010). Comparison of time series analysis methods (in Turkish). Master Thesis, Ege University, Institute of Science and Technology, İzmir.
  • Öntemel, Ş. (2016). The development of decision support models for the prediction of performance and production of ring yarn having different properties (in Turkish). Master Thesis, Kahramanmaraş Sütçü İmam University, Institute of Science and Technology, Kahramanmaraş.
  • Özdemir, İ. (1993). Deniz Tasımacılık Sektöründe Ro/Ro Tasımacılıgının Yeri İle Dünya ve Türkiye’de Ro/Ro İsletmeciliginin Durumu. Master Thesis, İstanbul University, Faculty of Management, İstanbul.
  • Özdemir, Ü., Deniz, T. (2013). Zonguldak Liman’ında Ro-Ro Taşımacılığ. Doğu Coğrafya Dergisi, 18 (30), 103-114.
  • Pang, G., Gebka, B. (2017). Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia. International Journal of Production Research, 55(9), 2454-2469.
  • Rashed, Y., Meersman, H., Sys, C., Van de Voorde, E., Vanelslander, T. (2018). A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports. Transportation Research Part A: Policy and Practice, 117, 127-141.
  • Samsun Port Authority, (2021). Statistics Unit, Ro-Ro Ship Numbers and Cargo Amounts between 2009-2021
  • Samsun Port Authority, (2023). Statistics Unit, Ro-Ro Ship Numbers and Cargo Amounts between 2022-2023
  • Şenalp, F. M. (2017). Kısa Dönem Enerji Talep Tahmini ve Yük Dağıtımı. Master Thesis, Selçuk University, Institute of Science and Technology, Konya.
  • Türkiye Exporters Assembly (TIM), Samsun Province Export Values (2021). Accessed Date: 10.11.2021, www.tim.org.tr is retrieved.
  • Turkish Statistical Institute (TURKSTAT), CPI Values (2021a). Accessed Date: 10.11.2021, www.tuik.gov.tr is retrieved.
  • Turkish Statistical Institute (TURKSTAT), Population Values (2021b). Accessed Date: 10.11.2021, www.tuik.gov.tr is retrieved.
  • Port Operators Association of Turkey (TURKLIM), Samsunport International, Port Features (2021). Accessed Date: 01.12.2021, http://www.turklim.org/uye-limanlar/samsunport/# is retrieved.
  • Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages, Management Science, 6 (3), 324-342.
  • Yıldırım, S. (2006). Proposing a solution for choosing port layout problem in ro-ro transportation and applying it to İstanbul (in Turkish). Master Thesis, Yıldız Technical University, Institute of Science and Technology, İstanbul.
  • Yüksekyıldız, E. (2010). Hinterland analysis of Trabzon, Samsun, Rize and Hopa ports (in Turkish). Master Thesis, Karadeniz Technical University, Institute of Science and Technology, Trabzon.
  • Zis, T., Psaraftis, H. N. (2017). The implications of the new sulphur limits on the European Ro-Ro sector. Transportation Research Part D. Transport and Environment, 52, 185-201.

Samsun Limanı'nda Ro-Ro Yük Taşımacılığı Talebinin Tahmini: Hibrit Bir Yöntem Yaklaşımı

Year 2024, In Press Articles, 1 - 18

Tayfun Şimşek Fırat Sivri Özkan Uğurlu Mehmet Aydın

https://doi.org/10.52998/trjmms.1383848

Abstract

ÖZET

Türkiye'nin geniş kıyı şeridi ve jeopolitik konumu, komşu ülkelerle Ro-Ro taşımacılığının önemini
gerektirmektedir. Türkiye’nin hızla büyüyen Ro-Ro taşımacılığı, ülke ekonomisi için büyük önem
taşıyan ithalat ve ihracata önemli katkı sağlamaktadır. Samsun Limanı, Karadeniz bölgesinde faaliyet
gösteren, Türkiye Ro-Ro taşımacılığı sektörünün en aktif limanlarından biridir. Bu çalışmada Samsun
Limanı'ndaki Ro-Ro taşımacılığı incelenmiş ve geleceğe yönelik kargo miktarı tahminlemesi
yapılmıştır. Bu amaçla yapay sinir ağları ve zaman serisi analiz yöntemleri birleştirilmiştir. Çalışmada
kullanılan girdi değişkenleri arasında 2009-2021 yılları arasında Samsun Limanı’na gelen Ro-Ro
gemilerinin sayısı, nüfus rakamları, özel tanımlı TÜFE göstergesi (taze meyve ve sebze) ve ihracat
değerleri yer almaktadır. Çıktı değişkeni ise Ro-Ro gemilerinin taşıdığı kargo miktarıdır. Elde edilen
sonuçlara göre Samsun Limanı'nın önümüzdeki 27 ay içerisinde iskele, liman alanı ve operasyonel
alan açısından Ro-Ro taşımacılığı için yeterli kapasiteye sahip olacağı görülmüştür.

Anahtar sözcükler: Samsun Limanı, Ro-Ro taşımacılığı, Liman kapasitesi, Tahminleme

Keywords

Samsun Limanı Ro-Ro taşımacılığı Liman kapasitesi Tahminleme

References

  • Aksoy, S. (2011). Simulation modeling for Ro-Ro terminals (in Turkish). Master Thesis. İstanbul Technical University, Institute of Science and Technology, İstanbul.
  • Atlantis, (2022). Maritime Statistics, Ro-Ro Vehicle Statistics. Accessed Date: 13.12.2022, http://atlantis.udhb.gov.tr/istatistik/istatistik_roro.aspx is retrieved.
  • Başar, E., Erol, S., Yılmaz, H. (2015). Karadeniz Limanlarında Ro-Ro Taşımacılığı ve Gelişimi: Türk Deniz Ticareti Tarihi. Ordu Üniversitesi Sosyal Bilimler Araştırmaları Dergisi, 5(12), 71-82.
  • Box, G. E., Jenkins, G. M., Reinsel, G. C., Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
  • Czermański, E. (2017). Baltic shipping development trends in maritime spatial planning aspect. Studia i Materiały Instytutu Transportu i Handlu Morskiego, (14).
  • De Monie, G., Rodrigue, J. P., Notteboom, T. (2011). Economic cycles in maritime shipping and ports: the path to the crisis of 2008. Integrating seaports and trade corridors, 13-30.
  • Dragan, D., Keshavarzsaleh, A., Intihar, M., Popović, V., Kramberger, T. (2021). Throughput forecasting of different types of cargo in the adriatic seaport Koper. Maritime Policy & Management, 48(1), 19-45.
  • Eluyode, O. S., Akomolafe, D. T. (2013). Comparative study of biological and artificial neural networks. European Journal of Applied Engineering and Scientific Research, 2(1), 36-46.
  • Eskafi, M., Kowsari, M., Dastgheib, A., Ulfarsson, G. F., Stefansson, G., Taneja, P., Thorarinsdottir, R. I. (2021). A model for port throughput forecasting using Bayesian estimation. Maritime Economics & Logistics, 23, 348-368.
  • Guo, L., Yang, Z. (2019). Relationship between shipping accessibility and maritime transport demand: the case of mainland China. Networks and Spatial Economics, 19, 149-175.
  • Güzey, H. (2019). Capacity availability analysis for a seaport company (in Turkish). Master Thesis, Bursa Uludag University, Institute of Science and Technology, Bursa.
  • Gosasang, V., Yip, T. L., Chandraprakaikul, W. (2018). Long-term container throughput forecast and equipment planning: the case of Bangkok Port. Maritime Business Review, 3(1), 53-69.
  • Gökkuş, Ü., Yıldırım, M. S., Aydin, M. M. (2017). Estimation of container traffic at seaports by using several soft computing methods: a case of Turkish Seaports. Discrete Dynamics in Nature and Society, 2017.
  • Görçün, Ö. F., Görçün, Ö. (2018). Lojistik Maliyetler Çerçevesinde Karadeniz Limanlarının Multimodal Taşımacılığa Uygunluklarının Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (21), 65-80.
  • Jiang, C., Wan, Y., Zhang, A. (2017). Internalization of port congestion: strategic effect behind shipping line delays and implications for terminal charges and investment. Maritime Policy & Management, 44(1), 112-130.
  • Kabalcı, E. (2014). Artificial neural networks. Lecture Notes. Accessed Date: 01.12.2021, https://ekblc.files.wordpress.com/2013/09/ysa.pdf is retrieved.
  • Kadılar, C. (2005). SPSS Uygulamalı Zaman Serileri Analizine Giriş, Hacettepe University, Bizim Büro Basımevi, Ankara, 299s.
  • Kahveci, S. (2021). Ro-ro transport fleet optimization in the Black Sea Region: A sample application (in Turkish). Ph.D. Thesis, Karadeniz Technical University, Institute of Science and Technology, Trabzon.
  • Köstem, S. (2018). The political economy of Turkish-Russian relations: Dynamics of asymmetric interdependence. Perceptions: Journal of International Affairs, 23(2), 10-32.
  • Krenker, A., Bešter, J., Kos, A. (2011). Introduction to the artificial neural networks. Artificial Neural Networks: Methodological Advances and Biomedical Applications. InTech, 1-18.
  • Montgomery, D. C., Jennings, C. L., Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
  • Morales-Fusco, P., Saurí, S., Spuch, B. (2010). Quality indicators and capacity calculation for RoRo terminals. Transportation Planning and Technology, 33(8), 695-717.
  • Moscoso-López, J. A., Ruiz-Aguilar, J. J., Urda, D., González-Enrique, J., Turias, I. J. (2019). Ro-Ro Freight Forecasting Based on an ANN-SVR Hybrid Approach. Case of the Strait of Gibraltar. In Advances in Computational Intelligence: 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Gran Canaria, Spain, June 12-14, 2019, Proceedings, Part I 15 (pp. 818-831). Springer International Publishing.
  • Oğhan, S. (2010). Comparison of time series analysis methods (in Turkish). Master Thesis, Ege University, Institute of Science and Technology, İzmir.
  • Öntemel, Ş. (2016). The development of decision support models for the prediction of performance and production of ring yarn having different properties (in Turkish). Master Thesis, Kahramanmaraş Sütçü İmam University, Institute of Science and Technology, Kahramanmaraş.
  • Özdemir, İ. (1993). Deniz Tasımacılık Sektöründe Ro/Ro Tasımacılıgının Yeri İle Dünya ve Türkiye’de Ro/Ro İsletmeciliginin Durumu. Master Thesis, İstanbul University, Faculty of Management, İstanbul.
  • Özdemir, Ü., Deniz, T. (2013). Zonguldak Liman’ında Ro-Ro Taşımacılığ. Doğu Coğrafya Dergisi, 18 (30), 103-114.
  • Pang, G., Gebka, B. (2017). Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia. International Journal of Production Research, 55(9), 2454-2469.
  • Rashed, Y., Meersman, H., Sys, C., Van de Voorde, E., Vanelslander, T. (2018). A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports. Transportation Research Part A: Policy and Practice, 117, 127-141.
  • Samsun Port Authority, (2021). Statistics Unit, Ro-Ro Ship Numbers and Cargo Amounts between 2009-2021
  • Samsun Port Authority, (2023). Statistics Unit, Ro-Ro Ship Numbers and Cargo Amounts between 2022-2023
  • Şenalp, F. M. (2017). Kısa Dönem Enerji Talep Tahmini ve Yük Dağıtımı. Master Thesis, Selçuk University, Institute of Science and Technology, Konya.
  • Türkiye Exporters Assembly (TIM), Samsun Province Export Values (2021). Accessed Date: 10.11.2021, www.tim.org.tr is retrieved.
  • Turkish Statistical Institute (TURKSTAT), CPI Values (2021a). Accessed Date: 10.11.2021, www.tuik.gov.tr is retrieved.
  • Turkish Statistical Institute (TURKSTAT), Population Values (2021b). Accessed Date: 10.11.2021, www.tuik.gov.tr is retrieved.
  • Port Operators Association of Turkey (TURKLIM), Samsunport International, Port Features (2021). Accessed Date: 01.12.2021, http://www.turklim.org/uye-limanlar/samsunport/# is retrieved.
  • Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages, Management Science, 6 (3), 324-342.
  • Yıldırım, S. (2006). Proposing a solution for choosing port layout problem in ro-ro transportation and applying it to İstanbul (in Turkish). Master Thesis, Yıldız Technical University, Institute of Science and Technology, İstanbul.
  • Yüksekyıldız, E. (2010). Hinterland analysis of Trabzon, Samsun, Rize and Hopa ports (in Turkish). Master Thesis, Karadeniz Technical University, Institute of Science and Technology, Trabzon.
  • Zis, T., Psaraftis, H. N. (2017). The implications of the new sulphur limits on the European Ro-Ro sector. Transportation Research Part D. Transport and Environment, 52, 185-201.
There are 40 citations in total.

Details

Primary Language English
Subjects Maritime Business Administration, Marine Transportation
Journal Section Research Article
Authors

Tayfun Şimşek 0000-0001-9104-5770

Fırat Sivri 0000-0002-3666-0284

Özkan Uğurlu 0000-0002-3788-1759

Mehmet Aydın 0000-0003-1163-6461

Early Pub Date January 16, 2024
Publication Date
Submission Date October 31, 2023
Acceptance Date January 5, 2024
Published in Issue Year 2024 In Press Articles

Cite

APA Şimşek, T., Sivri, F., Uğurlu, Ö., Aydın, M. (2024). Forecasting Ro-Ro Freight Transportation Demand at Samsun Port: A Hybrid Method Approach. Turkish Journal of Maritime and Marine Sciences1-18. https://doi.org/10.52998/trjmms.1383848

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