FREIGHT FLOW ANALYSIS AT MAJOR PORTS IN INDIA GOPAL R PATIL - - PowerPoint PPT Presentation

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FREIGHT FLOW ANALYSIS AT MAJOR PORTS IN INDIA GOPAL R PATIL Assistant Professor Department of Civil Engineering Indian Institute of Technology Bombay (Contribution from Prasant Sahu, PhD Student) Workshop on Urban Freight Transport: A Global

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FREIGHT FLOW ANALYSIS AT MAJOR PORTS IN INDIA

GOPAL R PATIL Assistant Professor Department of Civil Engineering Indian Institute of Technology Bombay

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 1

(Contribution from Prasant Sahu, PhD Student)

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INTRODUCTION

7516.6kms coastline Port Locations

  • 200 ports
  • 54 ports in east coast
  • 146 ports in west coast
  • India’s seaborne trade

95% by volume & 77% by value of international trade

  • Indian Ports Act, 1908

allows Maritime States to set up their own port systems

  • Major Port trust Act, 1963,

regulates 12 major ports

Thematic Diagram of Major Port Locations (Source: www.mapsofindia.com)

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 2

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Growth Dynamics: India’s Port Sector

  • Overall average annual growth (major & non-major) 9.2%

(2000-2012)

  • Major ports (7.3%) & Non major ports (13.7%)
  • Total Traffic, 2000-01: 383.85 Million tons
  • Total Traffic, 2012-13: 933.66 Million tons
  • Capacity utilization around 90-98% at Major ports
  • Highest annual growth in container traffic (15%)

Growth:143%

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 3

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Annual average cargo share for major ports (2002-2011)

  • Sl. No.

Port Name Port Code Annual Average Cargo Share (%) 1 Kolkata 1001 8 2 Paradip 1002 10 3 Visakhapatnam 1003 12 4 Chennai 1004 10 5 Tuticorin 1005 5 6 Cochin 1006 3 7 New Mangalore 1007 6 8 Mormugao 1008 7 9 Mumbai 1009 10 10 JNPT (Mumbai) 1010 11 11 Ennore 1011 2 12 Kandla 1012 16

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 4

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Traffic Variation at each Major Port

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 5

Chennai Cochin Ennore JNPT Kandla Kolkata Mormugao Mumbai New Mangalore Paradip Tuticorin Visakhapatnam 10 20 30 40 50 60 70 80 90 100

Cargo Volume (million tons) 2011-12 2012-13

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Cargo Volume at Indian Ports (2001-2013)

Year Cargo(million tons) Major port Share (%) Minor port Share (%) Major Ports Minor Ports Total Volume

2001-02 287.58 96.27 383.85 74.92 25.08 2002-03 313.55 105.17 418.72 74.88 25.12 2003-04 344.79 120.84 465.63 74.05 25.95 2004-05 383.75 137.83 521.58 73.57 26.43 2005-06 423.56 145.53 569.09 74.43 25.57 2006-07 463.78 186.12 649.9 71.36 28.64 2007-08 519.31 203.62 722.93 71.83 28.17 2008-09 530.53 213.20 743.73 71.33 28.67 2009-10 561.09 288.86 849.95 66.01 33.99 2010-11 570.03 314.85 884.88 64.42 35.58 2011-12 560.13 353.02 913.15 61.34 38.66 2012-13 545.79 387.87 933.66 58.45 41.54

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 6

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Growth in Indian Seaborne Trade

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12

  • 16
  • 12
  • 8
  • 4

4 8 12 16

Growth (%) Year

Indian Seaborne Cargo Indian GDP World Trade Volume World Seborne Cargo

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 7

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Commodity wise traffic at Major Ports

Commodity group 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 POL 142.094 153.548 168.299 174.384 179.104 185.981 Iron ore 79.217 80.584 91.993 94.091 60.401 28.472 Fertilizers 12.196 14.136 16.662 18.198 20.386 14.738 coal 68.827 60.351 64.739 70.594 78.785 86.660 Container 62.009 73.469 92.283 93.123 127.876 127.525 Others 59.225 81.694 84.808 79.979 101.364 110.118 Total 423.568 463.782 519.314 530.379 560.137 545.790

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 8

75% of containers are handled by JNPT and Chennai Port POL: Kandla (27%), Mumbai (18%), New Mangalore (12%)

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Cargo at Mumbai Port (1900-2012)

1900 1920 1940 1960 1980 2000 2020 10 20 30 40 50 60 70

Tonnage value (million tons) Year

Inbound tonnage Outbound tonnage Total tonnage

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 9

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Commodity Share at Mumbai Port (10 Years)

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014

CRL POLP BCHM FTL RPHP SLPH ISTL EDBL 10 20 30 40 50 60

Commodity share (%) Commodity

Inbound share (%) Outbound share(%)

CRL: Crude oil POLP: Petrol Oil and Lubricant Products ISTL: Iron and Steel BCHM: Bulk Chemicals FTL: Fertilizers RPHP: Rock Phosphate SLPH: Sulfur EDBL: Edible Oil

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Cargo Demand Estimation (Mumbai Port)

  • Univariate Regression Models
  • Multi-variate Regression Models
  • Time series Models

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014

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Univariate Regression

Models M1 and M2:

0.477

20.820( ) Inbound GDP 

0.733

0.348( ) Outbound GDP 

Models M3 and M4:

6

3.219 5.010*10 66.747 0.188 Inbound GDP FGP CRLP

   

6

4.232 1.001*10 22.277 0.382 Outbound GDP FGP CRLP

   

GDP: Gross Domestic Product in ‘000 crore INR (ten billion Indian Rupees) CRLP: Crude Oil Production in million tons FGP: Food Grain Production in million tons

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 12

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Statistical parameters for Univariate Models

Tonnage Model R2 Adj. R2 F-value t-statistics (p-values) β0(i) GDP (β1) FGP (β2) CRLP (β3) Inbound M1 0.836 0.833 254.878 16.11 (0.000) 2.49 (0.016) M3 0.942 0.938 259.862 2.31 (0.025) 11.24 (0.000) 3.55 (0.001)

  • 3.23

(0.002) Outbound M2 0.734 0.729 137.969 11.87 (0.000) 2.19 (0.023) M4 0.815 0.809 70.487 2.06 (0.045) 2.03 (0.048)

  • 2.81

(0.025) 4.45 (0.000)

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 13

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Multivariate Linear Regression Model

Models M5 and M6:

6

2.485 10.013*10 50.362 0.372 Inbound GDP FGP CRLP

   

6

3.862 8.002*10 18.864 0.474 Outbound GDP FGP CRLP

   

Statistical parameters for multivariate models

Tonnage Model R2

  • Adj. R2

F-value t-statistics (p-values) β0i GDP (β1) FGP (β2) CRLP (β3) Inbound M5 0.967 0.967 468.848 2.11 (0.039) 2.79 (0.007) 5.40 (0.001)

  • 2.34

(0.023) Outbound M6 0.868 0.862 105.212 2.22 (0.000) 3.46 (0.001)

  • 2.53

(0.024) 6.94 (0.001)

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 14

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Residual analysis, variance inflation factor (VIF)

5 10 15 20 25 30 35 40

  • 2.0
  • 1.5
  • 1.0
  • 0.5

0.0 0.5 1.0 1.5 2.0

Standardized residuals Predicted inbound tonnage (million tons) 4 8 12 16 20

  • 2.0
  • 1.5
  • 1.0
  • 0.5

0.0 0.5 1.0 1.5 2.0

Standardized residuals Predicted outbound tonnage (million tons)

Residual Plots

Variance Inflation Factor Variable VIF GDP 11.31 FGP 7.47 CRLP 10.1

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 15

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Validation of Regression Model (16% data)

Prediction error (%) of regression models with validation data

Cargo Operation Model Modeling Approach Prediction Error (%) Inbound M1 Univariate regression (nonlinear) 12.84 M3 Univariate multiple regression 15.89 M5 Multivariate regression 8.81 Outbound M2 Univariate regression (nonlinear) 18.90 M4 Univariate regression 17.28 M6 Multivariate regression 9.65

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 16

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Validation of Regression Models contd.

1954196519701972198819891997200520082009 10 20 30 40

Tonnage value (million tons) Year

Actual inbound tonnage Predicted inbound tonnage

1954196519701972198819891997200520082009 5 10 15 20 25

Tonnage value (million tons) Year

Actual outbound tonnage Predicted outbound tonnage

4 12 20 28 36 8 16 24 32 40

R

2=98.3%

Predicted inbound tonnage (million tons)

Actual inbound tonnage (million tons)

6 12 18 24 6 12 18 24 R2=98.8% Predicted outbound tonnage (million tons) Actual outbound tonnage (million tons)

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 17

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Vector autoregressive model

Model Structure:

                                          

  t t t t t t

Y Y c c Y Y

2 1 1 2 1 1 1 22 1 12 1 21 1 11 2 1 2 1

     

t t t t

Y Y c Y

1 1 2 1 12 1 1 1 11 1 1

      

  t t t t

Y Y c Y

2 1 2 1 22 1 1 1 21 2 2

      

 

Y1t= Inbound freight flow at time t Y2t = Outbound freight flow at time t

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 18

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VAR (1) MODEL

Models M7 and M8:

1 2 1 1 1

0429 . 0214 . 1 1377 .

  

 

t t t

Y Y Y

1 2 1 1 2

9316 . 0863 . 1612 .

  

 

t t t

Y Y Y

Inbound Parameter Estimate t- value p-value 0.1377 8.790 0.0318 1.0214 36.39 0.0001 0.0429 4.220 0.0238 Outbound 0.1612 9.820 0.0137 0.0863 2.730 0.0075 0.9316 23.58 0.0001

1

c

1 11

1 12

2

c

1 21

1 22

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 19

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Prediction Error(%) – Time series model

ErrorInbound ErrorOutbound

1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014

2 4 6 8 10 12 14 16 18 20

Figure - 4: Flow Prediction Error Plot Prediction Error (%) Port Code

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 20

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Var(1) Model to Forecast

Models developed using data till 2011-12:

1 2 1 1 1

0526 . 0205 . 1 0921 .

  

 

t t t

Y Y Y

1 2 1 1 2

83126 . 0636 . 0513 .

  

 

t t t

Y Y Y

Flow Projected tonnage value (million tons) 2012-13 2013-14 2014-15 2015-16 Inbound flow 40.579 (40.060) (1.3%) 42.299 44.039 45.809 Outbound flow 16.864 (17.978) (6.2%) 16.598 16.487 16.505

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 21

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Actual and Predicted Tonnage

1890 1905 1920 1935 1950 1965 1980 1995 2010

  • 10

10 20 30 40 50 Inbound observed Outbound observed Inbound/outbound predicted Year Inbound (million tons) 5 10 15 20 25 30 35 40 45 Outbound (million tons)

  • Inbound flow continues steep and upward trend
  • Outbound decreases till 2014-15 and then increases

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Conclusions

  • India’s seaborne trade 95% by volume & 77% by value of

international trade

  • Overall annual growth (2000-2012): 9.2%
  • Capacity Utilization at Major Ports: 90-98%
  • Ports in MMR (JNPT & MbPT) handles 21% of the total

cargo by major ports

  • Major ports share decreased from 75% in 2001 to about 58%

in 2013

  • Exponential Growth in cargo volume in the last decade
  • Crude Oil (CRL) and Petroleum, oil and Lubricant products

(POLP) are the primary commodities at Mumbai port

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 23

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Conclusions

  • GDP

, food grain production, and crude oil production are found to be significant in inbound and outbound cargo demand estimation

  • Univariate regression models are reasonably good, but

multivariate regression models are better

  • Inbound demand models’ prediction is better than outbound

models

4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014

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4/15/2014 Workshop on Urban Freight Transport: A Global Perspective9-10 April 2014 25