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

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.

[1]  Emmanuel John M. Carranza,et al.  Multivariate regression analysis of lithogeochemical data to model subsurface mineralization: A case study from the Sari Gunay epithermal gold deposit, NW Iran , 2015 .

[2]  Fahad Irfan Siddiqui,et al.  Simple and multiple regression models for relationship between electrical resistivity and various soil properties for soil characterization , 2013, Environmental Earth Sciences.

[3]  William H. K. Lam,et al.  Forecasts and reliability analysis of port cargo throughput in Hong Kong , 2004 .

[4]  Lori Tavasszy,et al.  Combining Models and Commodity Chain Research for Making Long-Term Projections of Port Throughput: an Application to the HamburgLe Havre Range , 2012, European Journal of Transport and Infrastructure Research.

[5]  A. C. Rencher.  Methods of multivariate analysis , 1995 .

[6]  N. H. Timm.  Applied Multivariate Analysis , 2002 .

[7]  David P Middendorf,et al.  DEVELOPMENT AND APPLICATION OF STATEWIDE, MULTIMODAL FREIGHT FORECASTING PROCEDURES FOR FLORIDA , 1982 .

[8]  Supaporn Kiattisin,et al.  A Comparison of Traditional and Neural Networks Forecasting Techniques for Container Throughput at Bangkok Port , 2011 .

[9]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[10]  V. A. Akinbinu.  Prediction of fracture gradient from formation pressures and depth using correlation and stepwise multiple regression techniques , 2010 .

[11]  P B Noyes.  RESPONDING TO CITIZEN REQUESTS FOR MULTIWAY STOPS , 1994 .

[12]  R R Tadi,et al.  TRUCK TRIP GENERATION CHARACTERISTICS OF NONRESIDENTIAL LAND USES , 1994 .

[13]  William H. K. Lam,et al.  Forecasting cargo growth and regional role of the port of Hong Kong , 2003 .

[14]  Gyei-Kark Park,et al.  Empirical Analysis of Influence Factors to Container Throughput in Korea and China Ports , 2011 .

[15]  Amelia C. Regan,et al.  State-of-the art of freight forecast modeling: lessons learned and the road ahead , 2010 .

[16]  Raimon Tolosana-Delgado,et al.  Simplifying compositional multiple regression: Application to grain size controls on sediment geochemistry , 2010, Comput. Geosci..

[17]  Haitham Al-Deek,et al.  Truck Trip Generation Models for Seaports with Container and Trailer Operation , 2000 .

[18]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[19]  Steven C. Wheelwright,et al.  Forecasting: Methods and Applications, 3rd Ed , 1997 .

[20]  Jun-Nan Chen,et al.  Forecasting container throughputs at ports using genetic programming , 2010, Expert Syst. Appl..

[21]  Abdelmonem A. Afifi,et al.  Practical Multivariate Analysis , 2011 .

[22]  Ram M. Pendyala,et al.  Forecasting Statewide Freight Toolkit , 2008 .

[23]  William F. Christensen,et al.  Methods of Multivariate Analysis: Rencher/Methods , 2012 .

[24]  Anthony Kenneth Charles Beresford,et al.  Seaport research: a structured literature review on methodological issues since the 1980s , 2011 .

[25]  George E. P. Box,et al.  Time Series Analysis: Box/Time Series Analysis , 2008 .

[26]  Haitham Al-Deek,et al.  Which Method Is Better for Developing Freight Planning Models at Seaports—Neural Networks or Multiple Regression? , 2001 .

[27]  Alexander Prinz,et al.  Forecasting container transshipment in Germany , 2009 .

[28]  Eddie C.M. Hui,et al.  Forecasting Cargo Throughput for the Port of Hong Kong: Error Correction Model Approach , 2004 .

[29]  Haitham Al-Deek,et al.  Transferability of an Intermodal Freight Transportation Forecasting Model to Major Florida Seaports , 2003 .

[30]  Ardeshir Faghri,et al.  EVALUATION OF ARTIFICIAL NEURAL NETWORK APPLICATIONS IN TRANSPORTATION ENGINEERING , 1992 .