Modelling the impact of epidemics on the operational efficiency of container transportation
Document Type : Original Article
Author
Assistant Professor, Faculty of Economics and Management, Khorramshahr University of Marine Science and Technology, Khorramshahr, Iran
10.22034/road.2023.363683.2094
Abstract
During the last few years, the transportation industry faced several crises with virus pandemic. The measures which are taken by the countries to tackle the pandemic has negatively affected the supply chain. It leaves the maritime transport industry with a vacuum of correctly predicting the volume of operations. The current research is an attempt to reduce the research gap and help market players to make more correct decisions in the future crisis. The purpose of this research is to model the time series of container transportation to predict the short-term trend of these indicators and its relationship with Covid-19 as an epidemic virus. The impact of Covid-19 is examined by forecasting container traffic indicators in 70 major international ports. The modeling process has been done with seasonal moving average and exponential smoothing model. To evaluate the performance of the models, information evaluation criteria and measurement error are calculated and compared. The seasonal integrated moving average model was found to be the most suitable and better model for predicting container indices.
Keywords
- Bhakuni, A. S., Sunori, S. K., Jethi, G. S., and Juneja, P. K., (2020), “Fuzzy Clustering of Paper Mill Data”, 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN): IEEE, pp. 678-683.
- Campbell, D., (2020), “UK coronavirus crisis' to last until spring 2021 and could see 7.9 m hospitalised'”, The Guardian. Sun, 15.
- Chan, H. K., Xu, S., and Qi, X., (2019),
“A comparison of time series methods for forecasting container throughput”, International journal of logistics research and applications, 22, No. 3, pp. 294-303.
- Darekar, A. S., Pokharkar, V., and Datarkar, S. B., (2016), “Onion price forecasting in Kolhapur market of Western Maharashtra using ARIMA technique”, International Journal of Information Research and Review, 3, No. 12, pp. 3364-3368.
- Fathei, K., (2018), “Machiavelli and the cultural relativity of leadership: An historical perspective”, Journal of Competitiveness Studies, 26, No. 1/2, pp. 97-106.
- Fernandes, N., (2020), “Economic effects of coronavirus outbreak (COVID-19) on the world economy”.
- Koyuncu, K., Tavacioğlu, L., Gökmen, N., and Arican, U. Ç., (2021), “Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models”, Maritime Policy & Management, 48,
No. 8, pp. 1096-1108. - Mishra, N. K., (2020), “Forecasting of Monthly Mean Rainfall in East Sikkim using ETS and SARIMA model in ‘R’Software,” Indian Institute of Technology, Kharagpur.
- Moon, M. A., (2018), “Demand and supply integration,” in Demand and Supply Integration, De Gruyter.
- Nalcaci, G., Özmen, A., and Weber, G. W., (2019), “Long-term load forecasting: models based on MARS, ANN and LR methods”, Central European Journal of Operations Research, 27, No. 4, pp. 1033-1049.
- Pandya, A. A., Herbert-Burns, R., and Kobayashi, J., (2011), “Maritime commerce and security: the Indian Ocean”, Henry L. Stimson Center.
- Paparoditis, E. and Politis, D. N., (2018), “The asymptotic size and power of the augmented Dickey–Fuller test for a unit root”, Econometric Reviews, 37, No. 9,
pp. 955-973. - Sintiya, E. S., Kusumawardana, A., Furqon, M. A., Najwa, N. F., Puspitaningrum, A. C., and Afrah, A. S., (2020), “SARIMA and Holt-Winters Seasonal Methods for Time Series Forecasting in Tuberculosis Case”, 2020 4th International Conference on Vocational Education and Training (ICOVET), IEEE,
1-5.
- Sotomayor, D. A. C. and Carlos, F. B. S. M., (2021), “Application of the Integrated Autoregressive Method of Moving Averages for the analysis of series of cases of COVID-19 in Peru”.
- Sunori, S. K., Negi, P. B., Maurya, S., Mittal, A., Bisht, N., and Juneja, P., (2021), “Developing Soft Computing based Models for Prediction of Pollutant PM10 of Air”, 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI): IEEE, pp. 149-154.
- Wadhawan, D. and Singh, H., (2019), “Estimating and forecasting volatility using ARIMA model: a study on NSE, India”, Indian Journal of Finance, 13, No. 5,
pp. 37-51. - Wang, Y., Zhang, Y., and Zhou, Q., (2016), “A Stein-like estimator for linear panel data models”, Economics Letters, 141,
pp. 156-161. - Warnock-Smith, D., Graham, A., O'Connell, J. F., and Efthymiou, M., (2021), “Impact of COVID-19 on air transport passenger markets: Examining evidence from the Chinese market”, Journal of air transport management, 94, p. 102085.