Estimation of passenger-kilometer and tonne-kilometer values for highway transportation in Turkey using the flower pollination algorithm

• Autorzy: Korkmaz E. , Akgungor A. P.

Identyfikatory

DOI

10.20858/sjsutst.2018.98.5

• Języki publikacji: EN

Abstrakty

EN

Within the scope of this study, intercity passenger and freight movements in Turkey are estimated by using the flower pollination algorithm (FPA), while demand forecasts are performed on transport systems considering possible future scenarios. Since the passenger and freight transport system in Turkey mainly involves road transport, passenger-kilometer and tonne-kilometer values of this system are estimated. By relying on three independent parameters, models were developed in three different forms: linear, force and semi-quadratic. Population (P) between 1990 and 2016, gross domestic product per capita (GDPperC) in US dollars and the number of vehicles were used as input parameters for the development of the models. When the passenger-kilometer models were created, the number of cars, buses and minibuses that are predominantly used for passenger transportation was preferred for the number of vehicles, while the number of trucks and vans used for cargo transportation were taken into consideration in the tonne-kilometer models. The coefficients of the models were determined by FPA optimization, with models developed to estimate passenger-kilometer and tonne-kilometer values. The model results were compared with the observation values and their performance was evaluated. Two different scenarios were created to estimate passenger-kilometer and tonne-kilometer in 2030. Parallel to the increase in population and welfare level, it is predicted that demand for passenger and freight transport will increase. In particular, the higher input parameter values in Scenario 1 significantly affect the increase in demand, leading to a demand increase of around 50%. In addition, the FPA has demonstrated effective performance in predicting the demand for passenger and freight transport and that it can be used in many different areas.

Słowa kluczowe

EN

passenger-kilometer; tonne-kilometer; flower pollination algorithm

PL

osobokilometr; tonokilometr; algorytm zapylania kwiatowego

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2018
• Tom: z. 98
• Strony: 45--52
• Opis fizyczny: Bibliogr. 11 poz.

Twórcy

autor

Korkmaz E.

• Faculty of Engineering, Kırıkkale University, Civil Engineering Department, Yahsihan, Kirikkale, Turkey

autor

Akgungor A. P.

• Faculty of Engineering, Kırıkkale University, Civil Engineering Department, Yahsihan, Kirikkale, Turkey

Bibliografia

• 1. Aydemir H., M.K. Çubuk. 2017. “Karayollarının Türkiye’de Genel Durumunun Araştırılması ile Yaşanan Değişimler ve Gelecek Stratejilerine Dair Tavsiyeler”. [In Turkish: “Changes experienced on highways: an investigation into the general situation in Turkey and recommendations relating to future strategies”.] Gazi Mühendislik Bilimleri Dergisi 2(3): 128-146.
• 2. Garrido R.A., H.S. Mahmassani. 2000. “Forecasting freight transportation demand with space-time multinomial probit model”. Transportation Research Part B 34: 403-418.
• 3. Haldenbilen S., H. Ceylan. 2005. “Şehirler Arası Ulaşım Talebinin Genetik Algoritma ile Modellenmesi”. [From Turkish: “Modelling of intercity transportation demand with a genetic algorithm”.] IMO Teknik Dergi 238: 3599-3618.
• 4. Çelikoğlu H.B., H.K. Cığızoğlu. 2007. “Public transportation trip flow modelling with generalized regression neural networks”. Advances in Engineering Software 38(2): 71-79.
• 5. Semeida A.M. 2014. “Derivation of travel demand forecasting models for low population areas: the case of Port Said Governorate, North East Egypt”. Journal of Traffic and Transportation Engineering 1(3): 196-208.
• 6. Nuzzolo A, A. Comi. 2014. “Urban freight demand forecasting: a mixed quantity/delivery/vehicle-based model”. Transportation Research Part E 65: 84-98.
• 7. Yang Y. 2015. “Development of the regional freight transportation demand prediction models based on the regression analysis methods”. Neurocomputing 158: 42-47.
• 8. Toole J.L., S. Colak, B. Sturt, L.P. Alexander, A. Evsukoff, M.C. Gonzalez. 2015. “The path most traveled: travel demand estimation using big data resources”. Transportation Research Part C 58: 162-177.
• 9. Yang X.S. 2014. Nature-inspired Optimization Algorithms. London and Waltham, MA: Elsevier.
• 10. Turkish Statistical Institute. “Road traffic accident statistics”. Available at: http://www.tuik.gov.tr.
• 11. Hlusička M., J. Kraus, 2017. “Increasing the usability of near-sea aerodromes”. Nase More 64(2): 45-49.