Skip to main content
SpringerLink
Log in

Estimation of freight demand at Mumbai Port using regression and time series models

KSCE Journal of Civil Engineering volume 20, pages 2022–2032 (2016)Cite this article

Abstract

Forecasting future freight demand at a seaport is important for its planning and development. India has 13 major ports which handle 75% of the total seaport freight. Among the 13 major ports, Mumbai Port, ranked at number three in the country for the year 2013-14, handles about 11% of the total freight at major seaports in India. The focus of this paper is on developing inbound and outbound demand forecasting models for Mumbai Port. The models are developed using additive regression and time series techniques. In regression analysis economic indicators, Gross Domestic Product (GDP) and Crude Oil Production (CRLP) are found to be significant. The multivariate models performed better than the univariate models. The validation of time-series models resulted in error of less than 5%. Both multivariate regression and time-series models are used to forecast freight demand for the years 2014- 15 through 2017-18. The regression models are producing more optimistic forecasts than the time series models. The elasticity analysis suggested that Mumbai’s inbound freight will be growing almost with India’s GDP growth rate, the outbound freight, however, will experience slower growth than that of inbound.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

References

  • Affi, A., May, S., and Clark, A. V. (2012). Practical multivariate analysis, 5th ed., CRC Press, Taylor and Francis Group.

    Google Scholar 

  • Akinbinu, V. A. (2010). “Prediction of fracture gradient from formation pressures and depth using correlation and stepwise multiple regression techniques.” Journal of Petroleum Science and Engineering, Vol. 72, Issue 1–2, pp. 10–17, DOI: 10.1016/j.petrol.2010.02.003.

    Article  Google Scholar 

  • Al-Deek, M. H. (2001). “Which method is better for developing freight planning models at seaports–neural networks or multiple regressions?” Transportation Research Record, No. 1763, pp. 90–97, DOI: 10.3141/1763-14.

    Article  Google Scholar 

  • Al-Deek, M. H., Johnson, G., Mohamed, A., and EI-Maghraby, A. (2000). “Truck trip generation models for seaports with container and trailer operation.” Transportation Research Record, No. 1719, pp. 1–9, DOI: 10.3141/1719-01.

    Article  Google Scholar 

  • Balach, P. and Tadi, R. R. (1994). “Truck trip generation characteristics of nonresidential land uses.” ITE Journal, Vol. 64, No. 7, pp. 43–47.

    Google Scholar 

  • Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (2008). Time series analysis: forecasting and control, 4th ed., John Wiley & Sons, Inc.

    Book  MATH  Google Scholar 

  • Cambridge Systematics, Inc. (1997). A guidebook for forecasting freight transportation demand, NCHRP report no. 388, Transportation Research Board, National Research Council, Washington D.C., USA.

  • Chen, S. and Chen, J. (2010). “Forecast container throughputs at ports using genetic programming.” Expert Systems with Applications, Vol. 37, Issue 3, pp. 2054–2058, DOI: 10.1016/j.eswa.2009.06.054.

    Article  Google Scholar 

  • Chow, J. Y. J., Yang, C. H., and Regan, A. C. (2010). “State-of-the art of freight forecast modeling: Lesions learned and the road ahead.” Transportation, Vol. 37, No. 6, pp. 1011–1030, DOI: 10.1007/s11116-010-9281-1.

    Article  Google Scholar 

  • Cohen, H., Horowitz, A., and Pendyala, R. (2008). Forecasting statewide freight toolkit, NCHRP report 606, Transportation Research Board, Washington, D.C., USA.

    Google Scholar 

  • Department of Economic Affairs, Ministry of Finance (2010). Position paper on the port sector, DoEA, MoF, Government of India.

  • Faghri, A. and Hua, J. (1992). “Evaluation of artificial neural network application in transportation engineering.” Transportation Research Record, No.1358, pp. 71–80.

    Google Scholar 

  • Federal Highway Administration (1999). Guidebook on statewide travel forecasting, U.S. Department of Transportation.

  • Gosasang, V., Chandraprakaikul, W., and Kiattsin, S., (2011). “A comparison of traditional and neural networks forecasting techniques for container throughput at Bangkok port.” The Asian Journal of Shipping and Logistics, Vol. 27, Issue 3, pp. 463–482, DOI: 10.1016/S2092-5212(11)80022-2.

    Article  Google Scholar 

  • Hamid, G., Tabatabaei, S. H., Asadi, H. H., and Carranza, E. J. M. (2015). “Multivariate regression analysis of lithogeochemical data to model subsurface mineralization: a case study from the Sari Gunay epithermal gold deposit, NW Iran.” Journal of Geochemical Exploration, Vol. 148, pp. 249–258, DOI: 10.1016/j.gexplo.2014.10.009.

    Article  Google Scholar 

  • Hui, E. C. M. H., Seabrooke, W., and C, W. G. K. (2004). “Forecasting cargo throughput for the port of Hong Kong: error correction model approach.” Journal of Urban Planning and Development, Vol. 130, Issue 4, pp. 195–203, DOI: 10.1061/(ASCE)0733-9488(2004)130:4 (195).

    Article  Google Scholar 

  • IMF (2014). World economic outlook: World economic and financial surveys, International Monetary Fund (IMF), Washington D.C., USA.

  • IPNG (2013). Indian petroleum and natural gas statistics 2013-2014, Ministry of Petroleum and Natural Gas, Economics and Statistics Division, Government of India, New Delhi.

  • Klodzinski, J. and Al-Deek, M. H. (2003). “Transferability of an intermodal freight transportation forecasting model to major Florida seaports.” Transportation Research Record, No. 1820, pp. 3645, DOI: 10.3141/1820-05.

    Article  Google Scholar 

  • Lam, W. H. K., Ng, P. L. P., Seabrooke, W., and Hui, E. C. M. (2004). “Forecast and reliability analysis of port cargo throughput in Hong Kong.” Journal of Planning and Development, Vol. 130, Issue 3, pp. 133–134, DOI:10.1061/(ASCE)0733-9488(2004)130:3(133).

    Article  Google Scholar 

  • Langen, P. W. de, Meijeren, J. van, and Tavasszy, L. A. (2012). “Combining models and commodity chain research for making long term projections of port throughput: an application to the Hamburg-Le Havre Range.” European Journal Transport Infrastructure Research (EJTIR), Vol. 12, Issue 3, pp. 310–331.

    Google Scholar 

  • Liu, L. and Park, G. (2011). “Empirical analysis of influence factors to container throughput in Korea and China ports.” The Asian Journal of Shipping and Logistics, Vol. 27, Issue 2, pp. 279–304, DOI: 10.1016/S2092-5212(11)80013-1.

    Article  MathSciNet  Google Scholar 

  • Makridakis, S., Wheelwright, C. S., and Hyndman, J. R. (2005). Forecasting methods and applications, 3rd ed., John Wiley & Sons, Inc.

    Google Scholar 

  • Middendrof, D. P., Jelavich, M., and Ellis, R. H. (1982). “Development and application of statewide multimodal freight forecasting procedures for Florida.” Transportation Research Record, No. 889, pp. 7–14.

    Google Scholar 

  • Mumbai Port Trust (2014). Annual report 2013-14, MPT, Mumbai, India.

  • Planning Commission (2011). Report of working group for port sector for the 12th five year plan: 2012-2017, [online] PC,Government of India. www.planningcommission.gov.in/aboutus/committee/wrkgrp12/transport/report/wg_port.pdf (Accessed on 28-08-2013).

  • Rencher, A. C. and Christensen, W. F. (2012). Methods of multivariate analysis, 3rd ed., John Wiley & Sons, Inc., Hoboken, NJ, USA.

    Book  MATH  Google Scholar 

  • Sculze, P. M. and Prinz A. (2009). “Forecasting container transshipment in Germany.” Applied Economics, Vol. 41, No. 22, pp. 2809–2815, DOI: 10.1080/00036840802260932.

    Article  Google Scholar 

  • Seabrooke, W., Hui, E. C. M., Lam, W. H. K., and Wong, G. K. C. (2003). “Forecasting cargo growth and regional role of the port of Hong Kong.” Cities, Vol. 20, No. 1, pp. 51–64, DOI: 10.1016/S0264-2751(02)00097-5.

    Article  Google Scholar 

  • Siddiqui, F. I. and Syed Osman, S. B. A. B. (2013). “Simple and multiple regression models for relationship between electrical resistivity and various soil properties for soil characterization.” Environmental Earth Sciences, Vol. 70, No.1, pp. 259–267, DOI: 10.1007/s12665-012-2122-0.

    Article  Google Scholar 

  • The Department of State Development, Business and Innovation (2009). Report on Indian infrastructure market opportunities in the seaport sector, [online], DSDBI, State Government of Victoria.http://export.business.vic.gov.au/data/assets/pdf_file/0010/337852/Indian-InfrastructureMarket-Opportunities-in-the-Sea-Ports-Sector.pdfm (Accessed on 28-09-2013)

  • Timm, N. H. (2002). Applied multivariate analysis, Springer, New York.

    MATH  Google Scholar 

  • Tolosana-Delgado, R. and Eynatten, H. V. (2010). “Simplifying compositional multiple regression: Application to grain size controls on sediment geochemistry.” Computers and Geosciences, Vol. 36, No. 5, pp. 577–589, DOI: 10.1016/j.cageo.2009.02.012.

    Article  Google Scholar 

  • Woo, S.-H., Pettit, S. J., Kwak, D.-W., and Beresford, A. K. C. (2011). “Seaport research: A structured literature review on methodological issues since the 1980s.” Transportation Research Part A, Vol. 45 No. 5, pp. 667–685, DOI: 10.1016/j.tra.2011.04.01.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India

    Gopal R. Patil

  2. Dept. of Civil Engineering, Birla Institute of Technology and Science Pilani, Pilani, 333031, India

    Prasanta K. Sahu

Authors
  1. Gopal R. Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prasanta K. Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gopal R. Patil.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patil, G.R., Sahu, P.K. Estimation of freight demand at Mumbai Port using regression and time series models. KSCE J Civ Eng 20, 2022–2032 (2016). https://doi.org/10.1007/s12205-015-0386-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12205-015-0386-0

Keywords

search

Navigation