Modeling the Container Selection for Freight Transportation: Case Study of Iran

Document Type : Research Paper

Authors

1 M.Sc. Student, Department of Transportation Engineering, Isfahan University of Technology, Isfahan, Iran

2 Assistant Professor, Department of Transportation Engineering, Isfahan University of Technology, Isfahan, Iran

Abstract

Significant advantages of intermodal and containerized transport have increased the global interest to this mode of transportation. This growing interest is reflected in the annual volume of container cargo growth. However, the container transport inside Iran does not have a proper place. Comparing the count of containers entering and leaving ports with the statistics obtained from railway and road maintenance organizations showed that more than 77% of the containerized imports have been stripped at ports and dispatched toward their ultimate destinations outside containers. These statistics also showed that more than 81% of the containerized exports have transported to ports by means other than containers. The main purpose of this study was to identify the most important variables affecting the selection of containerized freight transport and non-containerized freight transport options by applying decision tree models on the road freight movement and a set of variables describes the differences between these two options. The final model representing the selection of containerized transport was developed by the use of CHAID, QUEST, C5 and C$R decision tree algorithms. The results showed that the decision tree built via pruned C5 algorithm provides the best accuracy and most sensible list of important parameters. High-value and perishable commodities showed the greatest potential for containerized transport. The most important policy factors that could affect the tendency of cargo owners to use containerized transport are tariffs and the status of destination (whether it is a port). Policies that could encourage cargo owners to use intermodal transport include setting a lower tariff on container handling, reducing the cost of loading and unloading, increasing the port facilities supporting the containerized transport, adjusting customs, and development of dry ports.

Keywords

References

- Abdelwahab, W. and  Sayed, T. (1999) “Freight mode choice models using artificial neural networks”, Civil Engineering Systems, Vol. 16, No. 4, pp. 267-286.
- Degerland, J. (2011) “Containerisation International Year Book”, source: Baird Maritime, April, 2011. Website: www.informacargo.com/ciyb
- Esmaeili, M. (2014) “ Concepts and methods of data mining”, Iran: green publishing E-book", First Edition.
- Fowkes, A.S., Nash, C.A. and Twedle, G. (1991) “Investigating the market for intermodal freight technologies”, Transportation Research Part A, Vol. 25, No. 4, pp. 161-172.   
- Kass, G.V. (1980) “An exploratory technique for investigating large quantities of categorical data”. Applied statistics, Vol. 29, No. 2, p. 119.
- Kotsians, S.B. (2007) “Supervised machine learning: A review of classificaon techniques”, Vol. 31, No. 3, pp. 249-268.
- Mohri, S.S. and Haghshenas, H. (2015) “Analysis of Container Transportation in Iran: The current status and the approaches to increase the utility of rail transportation”, M.s thesis, Isfahan University of Technology.  
- Ortuzar, J.D. and Palma, A. (1988) “Stated preference in refrigerated and frozen cargo exports”, Simplified Transport Demand Modelling, Perspective 2, PTRC, London.
- Quinlan, J.R. (2014) “C4. 5: programs for machine learning”, USA: Elsevier.‏
- Rashidi, T.H. and Mohammadian, A. (2011) “Household travel attributes transferability analysis: application of a hierarchical rule based approach”, Transportation, Vol. 38, NO.4, pp. 697-714.‏
- Ravibabu, M. (2013) “A nested logit model of mode choice for inland movement of export shipments: A case study of containerised export cargo from India”, Research in Transportation Economics, Vol. 38, No. 1, pp. 91-100.
- Sayed, T. and Razavi, A. (2000) “Comparison of neural and conventional approaches to mode choice analysis”, Journal of Computing in Civil Engineering, Vol. 14, No. 1, pp. 23-30.‏
- Shinghal, N. and Fowkes, T. (2002) “Freight mode choice and adaptive stated preferences”, Transportation Research Part E, Vol. 38, No. 5, pp. 367-378.
- Tortum, A., Yayla, N. and Gökdağ, M. (2009) “The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system”, Expert Systems with Applications, Vol. 36, No. 3, pp. 6199-6217.‏
- UNCTAD (2012), “Review of Maritime Transport”, source: United Nations Conference on Trade and Development, 04 Dec 2012, 196 pages, Website: www.unctad.org
- Vieira, L.F.M. (1992) “The value of service in freight transportation”, PhD Thesis. Massachusetts Institute of Technology, Cambridge, MA. USA: MIT Libraries.
- Winston, C.M. (1981) “A multinomial model for prediction of the demand for domestic ocean container service”, Journal of Transport Economics and Policy, Vol. 15, No. 3, pp. 243-252.
- Xie, C., Lu, J., and Parkany, E. (2003) “Work travel mode choice modeling with data mining: decision trees and neural networks”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 1854, pp. 50-61.‏
International Journal of Transportation Engineering
Volume 5, Issue 1
July 2017
Pages 31-44

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