Forecasting Road Freight and Passenger Transport Demands in Türkiye Using the Exponential Smoothing

Yıl 2024, , 580 - 586, 15.05.2024

Hümeyra Bolakar Tosun

https://doi.org/10.34248/bsengineering.1447484

Öz

Exponential smoothing is a popular technique used to analyze and forecast trends in various industries such as road transportation and passenger transportation. This method is used to calculate weighted averages using historical data and adjust forecasts based on recent trends. In the Turkish context, forecasting transportation demands using exponential smoothing provides valuable information for transportation planning and resource allocation. The increasing number of vehicles has caused many negative environmental consequences. This study analyzed ten years of data on road freight and passenger transportation on a regional scale in order to make future predictions. In the study, solution suggestions are presented based on the findings and the policies that should be implemented to solve the problem are evaluated.

Anahtar Kelimeler

Transportation, Transport demands, Exponential smoothing method, Forecasting

Kaynakça

• Alshehrya AS, Belloumia M. 2016. Study of the environmental Kuznets curve for transport carbon dioxide emissions in Saudi Arabia. Renew Sustain Energy Rev, 75: 1339-1347. https://doi.org/10.1016/j.rser.2016.11.122.
• Amekudzi A. 2011. Placing carbon reduction in the context of sustainable development priorities: a global perspective. Carbon Manag, 2(4): 413-423.
• Amin A, Altinoz B, Dogan E. 2020. Analyzing the determinants of carbon emissions from transportation in European countries: the role of renewable energy and urbanization. Clean Technol Environ Pol, 22(8): 1725-1734. https://doi.org/10.1007/ s10098-020-01910-2.
• Bergmeir C, Hyndman RJ, Benítez JM. 2016. Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation. Int J Forecast, 32: 303-312.
• Bowerman Bruce L, Richard TO. 1979. Time series and forecasting: An applied approach. Duxbury Press, New York, US, pp: 36.
• Djakaria I, Saleh S. 2021. Covid-19 forecast using Holt-Winters exponential smoothing. J Physics, 2021: 012033.
• Edenhofer O. 2015. Climate change 2014: mitigation of climate change (Vol. 3). Cambridge University Press, Cambridge, UK, pp: 147.
• Figueroa MJ, Ribeiro SK. 2013. Energy for road passenger transport and sustainable development: assessing policies and goals interactions. Curr Opin Environ Sustain, 5(2): 152-162.
• He F, Chang KC, Li M, Li X, Li F. 2020. Bootstrap ARDL test on the relationship among trade, FDI, and CO2 emissions: based on the experience of BRICS countries. Sustainability, 12(3): 1060.
• Kahn Ribeiro S, Kobayashi S, Beuthe M, Gasca J, Greene D, Lee DS, Zhou PJ. 2007. Transport and its infrastructure. Climate change 2007: mitigation. contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK, pp: 65.
• Konarasinghe K. 2021. Forecasting COVID-19 outbreak in the Philippines and Indonesia. J New Front Healthcare Biol Sci, 2: 1-19.
• Meyer, B. D., & Sullivan, J. X. (2012). Identifying the disadvantaged: Official poverty, consumption poverty, and the new supplemental poverty measure. Journal of Economic Perspectives, 26(3), 111-136.
• Ozkan T, Yanginlar G, Kalayci S. 2019. Testing the transportation-induced environmental Kuznets curve hypothesis: evidence from eight developed and developing countries. Int J Energy Econ Pol, 9(1): 174-183.
• Sharif A, Raza SA, Ozturk I, Afshan S. 2019. The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: A global study with the application of heterogeneous panel estimations. Renew Energy, 133: 685-691. https://doi.org/ 10.1016/j.renene.2018.10.052.
• URL1: http://www.kgm.gov.tr (accessed date: March 5, 2023).
• Yağımlı M, Ergin H. 2017. Türkiye’de iş kazalarının üstel düzetme metodu ile tahmin edilmesi. Marmara Fen Bil Derg, 4: 118-123.