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J o u r n a l S e a r c h

M a r i t i m e P o l i c y a n d M a n a g e m e n t

Country: United Kingdom

Subject Area: Social Sciences | Engineering | Environmental Science

Subject Category:

Publisher: . Publication type: Journals. ISSN: 03088839, 14645254

Coverage: 1977-1978, 1980-2014

H Index: 31

Scope:

Thirty years ago maritime management decisions were taken on the basis of experience and hunch. Today, the experience is augmented [...]Show full scope

Charts Data

SJR indicator vs. Cites per Doc (2y)

The SJR indicator measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is. Cites per Doc. (2y) measures the scientific impact of an average article published in the journal, it is computed using the same formula that journal impact factor ™ (ThomsonReuters).

Citation vs. Self-Citation

in Journal Title Search

Exact phrase

CategoryQuartile (Q1 means highest values and Q4 lowest values)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Management, Monitoring, Policy and Law

Geography, Planning and Development

Ocean Engineering

Transportation

Search query

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J o u r n a l R a n k i n g s

Subject Category: Ocean Engineering.Year: 2013.

Download data in MS Excel format (30 Kb)

1 - 50 of 139 << First | < Previous | Next > | Last >>

Subject Area: All Subject Category: Ocean Engineering Country: All Year: 2013 Order By: SJR Display journals with at least: 0 Citable Docs. (3 years) Refresh

Title Type SJRH

index

Total Docs. (2013)

Total Docs.

(3years)

Total Refs.

Total Cites

(3years)

Citable Docs.

(3years)

Cites / Doc.

(2years)

Ref. / Doc.

Country

1International Journal of Impact Engineering

j 66 125 324 3.653 953 315 2,74 29,22

2 Computational Mechanics j 60 186 317 7.638 769 299 2,37 41,06

3 Coastal Engineering j 62 113 302 3.875 773 281 2,53 34,29

4Probabilistic Engineering Mechanics

j 44 55 174 1.605 442 169 2,55 29,18

5International Journal of Engine Research

j 22 46 119 1.470 248 116 2,24 31,96

6Journal of Atmospheric and Oceanic Technology

j 81 206 433 6.677 914 423 1,95 32,41

7 Marine Structures j 33 45 91 1.212 218 89 2,13 26,93

8 Applied Ocean Research j 34 82 152 2.308 286 152 1,87 28,15

9 Ocean Engineering j 46 332 558 9.205 1.214 541 1,95 27,73

10 Nonlinear Dynamics j 59 510 1.118 15.792 3.098 1.081 2,80 30,96

11Limnology and Oceanography: Methods

j 35 52 187 2.013 419 186 1,70 38,71

12 Naval Research Logistics j 41 48 164 1.311 166 154 0,74 27,31

13 Coastal Engineering Journal j 21 16 62 540 108 60 1,91 33,75

14Maritime Policy and Management

j 31 62 121 1.927 235 115 1,87 31,08

15International Journal of Structural Stability and Dynamics

j 14 88 186 2.165 220 180 1,34 24,60

16Structure and Infrastructure Engineering

j 17 148 211 5.379 322 202 1,49 36,34

17Geological Society Special Publication

k 68 250 718 19.928 1.126 703 1,36 79,71

18Ships and Offshore Structures

j 9 78 96 1.409 87 90 1,13 18,06

19

Proceedings of the Institution of Civil Engineers: MaritimeEngineering

j 12 20 62 445 50 48 1,19 22,25

20IEEE Journal of Oceanic Engineering

j 63 108 212 1.800 461 204 2,27 16,67

21Journal of Waterway, Port, Coastal and Ocean Engineering

j 36 53 112 1.476 144 105 1,46 27,85

22 Journal of Ship Research j 28 18 65 480 76 65 1,24 26,67

23 Oceanologia j 26 47 117 2.110 128 117 1,02 44,89

24Mechanics Based Design of Structures and Machines

j 20 27 91 635 82 88 0,93 23,52

Ranking Parameters

2,290

2,176

1,798

1,713

1,618

1,615

1,518

1,497

1,382

1,277

1,132

1,131

1,129

1,009

0,982

0,926

0,856

0,740

0,703

0,687

0,671

0,662

0,552

0,540

Página 1 de 2Journal Rankings on Ocean Engineering

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J o u r n a l R a n k i n g s

Subject Category: Transportation.Year: 2013.

Download data in MS Excel format (26 Kb)


Subject Area: All Subject Category: Transportation Country: All Year: 2013 Order By: SJR Display journals with at least: 0 Citable Docs. (3 years) Refresh

Title Type SJRH

index

Total Docs. (2013)

Total Docs.

(3years)

Total Refs.

Total Cites

(3years)

Citable Docs.

(3years)

Cites / Doc.

(2years)

Ref. / Doc.

Country

1Transportation Research, Series B: Methodological

j 70 135 301 4.613 1.356 294 4,36 34,17

2 Transportation Science j 64 39 103 1.369 334 103 2,93 35,10

3Transportation Research, Part A: Policy and Practice

j 65 134 276 6.238 930 264 3,07 46,55

4Transportation Research, Part E: Logistics and Transportation Review

j 52 114 263 3.534 927 255 3,12 31,00

5Transportmetrica A: Transport Science

j 13 46 62 1.682 123 59 1,86 36,57

6 Tourism Management j 80 127 438 8.474 1.665 406 3,45 66,72

7 Journal of Travel Research j 59 59 151 3.974 444 149 2,64 67,36

8Transportation Research Part C: Emerging Technologies

j 55 227 285 7.294 1.054 256 3,98 32,13

9Journal of Transport Geography

j 43 162 400 7.439 1.114 378 2,54 45,92

10 Transport Reviews j 34 39 112 2.373 276 107 2,19 60,85

11 Transport Policy j 40 138 273 5.439 605 260 2,14 39,41

12 Transportation j 42 84 177 3.188 346 165 1,89 37,95

13International Journal of Sustainable Transportation

j 8 17 65 572 93 62 1,35 33,65

14International Journal of Physical Distribution and Logistics Management

j 34 40 130 2.480 383 127 2,66 62,00

15Transportation Research, Part D: Transport and Environment

j 44 111 248 1.316 510 244 1,96 11,86

16Research in Transportation Economics

j 13 106 155 3.498 203 138 1,32 33,00

17 Economics of Transportation j 3 14 7 403 14 6 2,33 28,79

18

IWCTS 2012 - 5th ACM SIGSPATIAL International Workshop on ComputationalTransportation Science

p 1 0 10 0 2 8 0,25 0,00

19International Journal of Tourism Research

j 15 93 148 5.512 270 144 1,37 59,27

20International Journal of Logistics Management

j 8 21 60 1.199 117 59 1,32 57,10

21Transportation Research Part F: Traffic Psychology andBehaviour

j 43 101 166 4.229 354 165 1,80 41,87

22Maritime Policy and

j 31 62 121 1.927 235 115 1,87 31,08

Ranking Parameters

3,306

3,139

2,433

2,155

2,150

1,961

1,958

1,943

1,715

1,678

1,666

1,645

1,556

1,305

1,255

1,130

1,120

1,093

1,093

1,088

1,016

1,009

Página 1 de 2Journal Rankings on Transportation


Scimago Lab, Copyright 2007-2014. Data Source: Scopus®


Management

23Journal of Transportation Engineering

j 42 139 407 3.388 490 398 1,04 24,37

24European Journal of Transport and InfrastructureResearch

j 10 18 72 764 99 69 1,22 42,44

25Maritime Economics and Logistics

j 27 22 68 842 102 66 1,46 38,27

26Journal of Air Transport Management

j 34 84 251 2.855 290 234 1,12 33,99

27 Public Transport j 8 18 37 479 38 33 1,17 26,61

28IET Intelligent Transport Systems

j 15 55 128 1.431 245 124 1,67 26,02

29International Journal of Shipping and Transport Logistics

j 7 37 70 1.507 86 70 1,21 40,73

30Journal of Transportation Safety and Security

j 5 21 64 567 51 63 0,56 27,00

31Sustainable Cities and Society

j 7 42 65 1.413 125 63 1,98 33,64

32Journal of Transport Economics and Policy

j 33 29 69 800 62 69 0,84 27,59

332010 IEEE Vehicular Networking Conference, VNC 2010

p 6 0 49 0 63 48 0,00 0,00

34International Journal of Crashworthiness

j 21 61 166 1.635 198 165 1,13 26,80

35Research in Transportation Business and Management

j 3 54 65 2.398 41 57 0,72 44,41

36International Journal of Transport Economics

j 14 18 44 814 33 43 0,66 45,22

37Transportation Planning and Technology

j 21 42 143 1.279 100 139 0,57 30,45

38European Transport -Trasporti Europei

j 4 22 62 897 47 58 0,69 40,77

39European Transport Research Review

j 5 45 67 1.294 44 60 0,65 28,76

40Zhongguo GongluXuebao/China Journal of Highway and Transport

j 16 148 380 2.673 211 380 0,56 18,06

41World Review of Intermodal Transportation Research

j 5 13 23 536 13 23 0,17 41,23

42 Transportation Letters j 7 21 59 775 34 59 0,51 36,90

43 IATSS Research j 4 18 40 481 38 40 0,81 26,72

44 Transportation Journal j 23 26 75 1.005 34 59 0,30 38,65

45Journal of Public Transportation

j 2 29 58 770 28 58 0,48 26,55

46

Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/ Journal of Transportation SystemsEngineering and Information Technology

j 12 159 574 1.670 218 574 0,34 10,50

47 Mobilization j 15 0 67 0 53 66 0,40 0,00

48Journal of Transport and Land Use

j 3 20 21 741 13 20 0,65 37,05

49Journal of Transportation Security

j 5 27 62 775 39 58 0,59 28,70

50IEEE Vehicular Networking Conference, VNC

p 4 42 77 621 80 74 1,08 14,79

0,970

0,912

0,896

0,857

0,768

0,760

0,712

0,680

0,677

0,624

0,613

0,593

0,560

0,541

0,502

0,500

0,480

0,472

0,460

0,426

0,425

0,402

0,354

0,329

0,327

0,326

0,288

0,285

Página 2 de 2Journal Rankings on Transportation


2013 JCR Social Science Edition

Journal Summary List Journal Title Changes

Journals from: subject categories TRANSPORTATION

Sorted by: Impact Factor Journals 1 - 20 (of 29) [ 1 | 2 ] Page 1 of 2

Ranking is based on your journal and sort selections.

Mark Rank

Abbreviated Journal Title

(linked to journal information)

ISSN

JCR Data Eigenfactor® Metrics

Total Cites

ImpactFactor

5-YearImpactFactor

ImmediacyIndex Articles

CitedHalf-life

Eigenfactor®

Score

ArticleInfluence®

Score

1 TRANSPORT RES B-METH 0191-2615 5300 3.894 4.439 0.571 126 9.2 0.01137 1.695

2 ACCIDENT ANAL PREV 0001-4575 9082 2.571 3.096 0.371 509 7.0 0.01729 0.840

3 TRANSPORT RES A-POL 0965-8564 3711 2.525 2.855 0.326 129 8.3 0.00801 1.119

4 TRANSPORT SCI 0041-1655 3038 2.294 2.913 0.541 37 >10.0 0.00494 1.706

5 J TRANSP GEOGR 0966-6923 2060 2.214 2.768 0.432 148 4.4 0.00567 0.795

6 TRANSPORT RES E-LOG 1366-5545 2255 2.193 2.943 0.283 99 5.6 0.00718 1.055

7 TRANSPORTMETRICA 1812-8602 241 2.068 2.022 0 3.7 0.00122 0.813

8 TRANSPORT POLICY 0967-070X 1555 1.718 2.084 0.179 134 6.4 0.00419 0.750

9 TRANSPORT RES F-TRAF 1369-8478 1482 1.635 2.349 0.280 100 7.0 0.00329 0.781

10 TRANSPORT RES D-TR E 1361-9209 1666 1.626 2.040 0.206 107 6.6 0.00439 0.746

11 TRANSPORTATION 0049-4488 1629 1.617 2.061 0.696 56 8.3 0.00392 0.952

12 TRANSPORT REV 0144-1647 1016 1.551 2.310 0.892 37 6.7 0.00285 0.927

13 INT J SUSTAIN TRANSP 1556-8318 159 1.447 1.505 0.333 21 3.6 0.00079 0.510

13 MARIT POLICY MANAG 0308-8839 884 1.447 0.116 43 9.0 0.00132

15 INT J SHIP TRANS LOG 1756-6517 129 1.340 1.337 0.143 35 2.9 0.00042 0.272

16 J SAFETY RES 0022-4375 2030 1.303 1.940 0.175 63 8.3 0.00401 0.761

17 TRAFFIC INJ PREV 1538-9588 1106 1.286 1.556 0.296 125 5.1 0.00395 0.560

18 MOBILITIES-UK 1745-0101 323 1.169 1.348 0.200 35 5.0 0.00145 0.641

19 MARIT ECON LOGIST 1479-2931 414 1.045 0.100 20 6.9 0.00103

20 EUR J TRANSP INFRAST 1567-7133 210 1.023 1.132 0.056 18 5.1 0.00073 0.410

Journals 1 - 20 (of 29) [ 1 | 2 ] Page 1 of 2

Página 1 de 2JCR-Web 4.5 Journal Summary List

04/08/2014http://admin-apps.webofknowledge.com/JCR/JCR?RQ=LIST_SUMMARY_JOURN...

1

Alba Martínez López

De: MPM <[email protected]>Enviado el: xoves, 13 de novembro de 2014 3:30Para: 'Alba Martínez López'Asunto: RE: Maritime Policy & Management - Decision on Manuscript ID

TMPM-2012-0103.R3Datos adjuntos: Acceptance letter - Dr Martínez-López.pdf

Dear Prof.Dr. Alba Martínez López Please find attached copy of letter as requested ‐ we have sent in electronic format, please confirm that this will be acceptable, else I can arrange a printed copy to be sent to: The Integrated Group for Engineering Research University A Coruña Edificio de Talleres (Campus de Esteiro) C/ Mendizábal s/n 15403 Ferrol Spain Kind regards Sally Dr Sally Martin Administrative Editor Maritime Policy & Management Email: [email protected] ‐‐‐‐‐Original Message‐‐‐‐‐ From: Alba Martínez López [mailto:[email protected]] Sent: 04 November 2014 01:09 To: [email protected] Subject: RV: Maritime Policy & Management ‐ Decision on Manuscript ID TMPM‐2012‐0103.R3 Dear Dr Martin, When do you estimate that this paper will be published? We suppose that in 2015....in that case, Would it be possible you send to us by post a formal acceptance letter of the paper signed by the editor in chief? I attach an example from another journal. The acceptation mail is not valid for our university system, it demands an acceptance letter like the attached one for the recognition of the paper before its publication.This recognition is important for the financiation of our research group in 2015. Sorry for the inconveniences but that letter is essential for us. Thank you for your comprehension. Sincerely, Prof.Dr. Alba Martínez López (PhD. Naval Engineer and Naval Architect) The Integrated Group for Engineering Research University of A Coruña Edificio de Talleres (Campus de Esteiro) C/ Mendizábal s/n 15403 Ferrol (Spain) Telf. +34981337400 ext. 3848/3866 http://www.gii.udc.es ‐‐‐‐‐Mensaje original‐‐‐‐‐ De: onbehalfof+heather.mclaughlin+canterbury.ac.uk@manuscriptcentral.com [mailto:onbehalfof+heather.mclaughlin+canterbury.ac.uk@manuscriptcentral.com] En nombre de [email protected]

2

Enviado el: sábado, 25 de outubro de 2014 10:33 Para: [email protected] Asunto: Maritime Policy & Management ‐ Decision on Manuscript ID TMPM‐2012‐0103.R3 25‐Oct‐2014 Dear Dr Martínez‐López: Ref: A Multi‐criteria Decision Method for the analysis of the Motorways of the Sea: the application to the case of France and Spain on the Atlantic Coast Our referees and editors have now considered your paper and have recommended publication in Maritime Policy & Management. We are therefore pleased to accept your paper in its current form. Once your paper is allocated to a forthcoming issue, your manuscript files will be forwarded to the publisher for copy editing and typesetting. You will then receive proofs for checking, and instructions for transfer of copyright. The publisher requests that proofs are checked and returned within 48 hours of receipt. Thank you for your contribution to Maritime Policy & Management and we look forward to receiving further submissions from you. Sincerely, Dr McLaughlin Editor, Maritime Policy & Management [email protected] There are now over 1050 Taylor & Francis titles available on our free table of contents alerting service! To register for this free service visit: www.tandfonline.com/alerting.

MARITIME POLICY & MANAGEMENT The flagship journal of international shipping and port research

Editors-in-Chief

Heather McLaughlin Canterbury Christ Church University

Kevin Li Chung-Ang University Email: [email protected]

13 November 2014

Dear Prof. Alba Martínez López,

Re: Acceptance of manuscript for Maritime Policy & Management

Routledge, Taylor & Francis are pleased to accept your paper entitled, ‘A Multi-criteria Decision Method for

the analysis of the Motorways of the Sea: the application to the case of France and Spain on the Atlantic

Coast’ for publication in the Journal Maritime Policy & Management. Your manuscript is scheduled to be

published in 2015.

Should you have further inquiries regarding the publication of this manuscript, please feel free to contact Dr

Sally Martin, the Administrative Editor at [email protected].

Yours sincerely,

Kevin Li

Editor-in-Chief

Maritime Policy & Management

1

Alba Martínez López

De: onbehalfof+heather.mclaughlin+canterbury.ac.uk@manuscriptcentral.com en nombre de [email protected]

Enviado el: sábado, 25 de outubro de 2014 10:33Para: [email protected]: Maritime Policy & Management - Decision on Manuscript ID TMPM-2012-0103.R3

25‐Oct‐2014 Dear Dr Martínez‐López: Ref: A Multi‐criteria Decision Method for the analysis of the Motorways of the Sea: the application to the case of France and Spain on the Atlantic Coast Our referees and editors have now considered your paper and have recommended publication in Maritime Policy & Management. We are therefore pleased to accept your paper in its current form. Once your paper is allocated to a forthcoming issue, your manuscript files will be forwarded to the publisher for copy editing and typesetting. You will then receive proofs for checking, and instructions for transfer of copyright. The publisher requests that proofs are checked and returned within 48 hours of receipt. Thank you for your contribution to Maritime Policy & Management and we look forward to receiving further submissions from you. Sincerely, Dr McLaughlin Editor, Maritime Policy & Management [email protected] There are now over 1050 Taylor & Francis titles available on our free table of contents alerting service! To register for this free service visit: www.tandfonline.com/alerting.


My Manuscripts Author Resources

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4 Manuscripts with Decisions

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Maritime Policy & Management - Decision on Manuscript ID TMPM-2012-0103.R3 (25-Oct-2014)

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Maritime Policy & Management - Manuscript ID TMPM-2012-0103.R2 (17-May-2014)

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Manuscripts with Decisions

Manuscript ID Manuscript Title Date Submitted

Date Decisioned Status Actions

TMPM-2012-0103.R3

A Multi-criteria Decision Method for the analysis of the Motorways of the Sea: the application to the case of France and Spain on the Atlantic Coast [View Submission]

01-Oct-2014 25-Oct-2014

ADM: Martin, Sally

Accept (25-Oct-2014)Awaiting Production Checklist

view decision letter

TMPM-2012-0103.R2

A Multi-criteria Decision Method for the analysis of the Motorways of the Sea: the application to the case of France and Spain on the Atlantic Coast [View Submission]

17-May-2014 30-Sep-2014

ADM: Martin, Sally

Minor Revision (30-Sep-2014)a revision has been submitted


a revision has been submitted (TMPM-2012-0103.R3)

TMPM-2012-0103.R1 20-Dec-2013 05-May-2014

• To submit a new manuscript, click on the "Submit a Manuscript" link below.• Clicking on the various manuscript status links under "My Manuscripts" will display a list of all the

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Página 1 de 3ScholarOne Manuscripts#refX#refX

25/10/2014http://mc.manuscriptcentral.com/tmpm

A Multi-criteria Decision Method for the analysis of the Sea Motorways: the application to the case of France and Spain on the Atlantic Coast [View Submission]

ADM: Martin, Sally

Conditional Accept (05-May-2014)a revision has been submitted



TMPM-2012-0103

A Multi-criteria Decision Method for the analysis of the Sea Motorways: the application to the case of France and Spain on the Atlantic Coast [View Submission]

30-Jul-2012 26-Nov-2013

ADM: Pang, AliceADM: Martin, Sally

Minor Revision (26-Nov-2013)a revision has been submitted



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For Peer Review O

nly

A Multi-criteria Decision Method for the analysis of the

Motorways of the Sea: the application to the case of France

and Spain on the Atlantic Coast

Journal: Maritime Policy & Management

Manuscript ID: TMPM-2012-0103.R3

Manuscript Type: Original Article

Keywords: Freight Markets, Liner shipping, Logistics, Ports, Regulation, Risk, Policy,

Sustainability

URL: http://mc.manuscriptcentral.com/tmpm


For Peer Review O

nly

Author's Response to Decision Letter for (TMPM-2012-0103.R2)

A Multi-criteria Decision Method for the analysis of the Motorways of the Sea: the application

to the case of France and Spain on the Atlantic Coast

COMMENTS IN BRIEF Dear editor, congratulations for the journal and thanks to give us the opportunity to participate in it. First of all, we would like to thank all the reviewers for their comments and suggestions; we are plenty aware of the time dedicated to the revision and we would like to thank their attention. In what follows we provide a brief description of the main changes that have been made and how every reviewer’s suggestions have been addressed. Comments to the Author • Pdf page 2: Line 34. Instead of “Despite the political support of the European Union for the identification of the most interesting Motorways of the Sea” use “Despite the European Union political support to identify the most interesting Motorways of the Sea”. Done • Pdf page 2: Line 37. Instead of “for the identification” use “to identify”. Done • Pdf page 2: In the abstract, the word “interesting” does not provide scientific content. Have Authors thought about other words like feasible, adequate? Give it a thought. Thank you for this appropriated observation. We have substituted interesting by feasible in the second line and by relevant in the third line from the end of the abstract. • Pdf page 4: Lines 11 to 13: “Despite this political support, the last decades have witnessed relevant public investments in land-based transport infrastructures but this financial support was not equally applied to seaway infrastructures whereby, a distorted transport marketplace was generated in favour of land transport [6]”. This paragraph needs to be revised. At an EU level this paragraph is questionable and can raise a lot of discussion because it is not correct. EU funding for public investments in infrastructure has favoured low carbon modes, subject to specific public calls texts and evaluation criteria, and whose number of financed projects depends upon a number of parameters, and two immediate ones are the number of proposals submitted and their quality; this depends from the private sector participation and from the involvement of national public authorities. At a national level it may be accepted, but even here are rules to follow. And yes, a couple of years ago road transport received most of the funding, but rail missed it. The focus of land infrastructure funding falls now on rail because of its numerous bottlenecks, and because rail is part of the low carbon modes. Therefore, this can not be strictly criticised as it appears in the paper even though I can understand what you are trying to say. In the light of this, the distorted transport marketplace, does exist because of two factors: 1) Road and rail have taken advantage of public funding because they are more visible 2) The maritime industry has not been able to take advantage of existing funding, given its inherent business structure, a situation that is now slowly changing. This paragraph was based on the conclusions of the Dr. Baird and Dr. Gesé and Bair which were published by Maritime Policy and Management: Baird A. 2007. The economics of Motorways of the sea. Maritime Policy and Management. 34:4 pp 287-310 Gesé X. and Baird A. 2013. Motorways of the sea policy in Europe. Maritime Policy and Management. 40:1 pp10-26 Consequently, we agree with the reviewer: the current paragraph can arise a wide discussion about the reasons of the distortion of the transport marketplace in EU. Although this is very interesting for our work, obviously it is not the main aim of this paper. Thus, we have decided to simplify the paragraph: ‘Despite this political support, the last decades have witnessed a distorted transport marketplace which has resulted in a favorable situation for the land transport’

Page 1 of 33



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nly

• Pdf page 4: Lines 22: an “a” is missing before “frequency”. Done • Pdf page 4: Lines 25: add “alone” after “transport”. Done • Pdf page 4: Lines 29 to 35: the paragraph starting with “This aspect was…..” does not link to the previous one, needs clarification, and lacks scientific support. As it is the first group of studies that Authors refer to is out of context, and whatever studies are considered they should be identified in the paper. In order to improve the continuity of the text, we have changed the expression of the paragraph: ‘The selection of the ports, and therefore the maritime routes chosen in the intermodal transport, was mainly tackled from….’ The scientific references are provided in the next paragraph. Regarding the first group of studies, these are related mainly to tramp traffic, however the Motorways of the sea projects, presented by the shipping companies to take advantage of the public funds in the framework of the Bilateral Agreement between Spain and France for the development of the Motorways of the Sea, are very significant to this regard. We have introduced a sentence in the body text that tries to clarify this comment. • Pdf page 6 Line 52. Write abbreviation “(RI)” after “Relevance Indexes” this facilitates the reading of the paper equations. Done • Pdf page 7 Line 11. Write abbreviation “(DIR)” after “Differential Indexes of Relevance” this facilitates the reading of the paper equations. Done • Pdf page 7 Lines 23, 29. Please consider revising the abbreviation “IPT” since it does no match with its meaning “Port Index”. Check the overall paper We have substituted IPT by PI (Port Index) along the text. We have also introduced after “Port Index´´ the abbreviation (PI) as it was indicated in the previous comments of the reviewer. • Pdf pages 11 and 13. Abbreviations should be avoided in the head of sections. Done

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nlyA Multi-criteria Decision Method for the analysis of the Motorways of

the Sea: the application to the case of France and Spain on the Atlantic

Coast

Abstract

Despite European Union political support to identify the political support of the European Union for

the identification of the most interesting feasible Motorways of the Sea, the final decision can only be

made by Private Shipping Companies, which are operators of a unique stretch of the intermodal chain.

This paper provides a multi-criteria decision method for the identification ofto identify the most suitable

Motorways of the SeaSea Motorways taking into account the competitiveness of whole intermodal

routes versus the alternative of road transport from the loader´s perspective. The analysis is carried out

assuming a `many to many´ transport model. Firstly indexes of time and cost were defined and

evaluated for every available route in the model in accordance with a multi-criteria decision matrix.

Secondly, through a Monte Carlo simulation a sensitivity analysis was carried out in order to evaluate

the influence on the results of the forecast assumed to construct the matrix. The results obtained are

not only quantitative, but also qualitative. The development of intermodal routes via Motorways of the

Sea is especially relevant interesting for the peripheral European Union countries. Due to the relevance

of the freight flow between France and Spain and the congestion of their connections through the

Pyrenees, the method proposed was applied to the analysis of this particular case

Keywords: Motorways of the Sea, Selection of Maritime Routes, Monte Carlo

Simulation, Sensitivity Analysis, Multi-criteria decision method

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nlyIntroduction

The level of congestion reached by some of the main land transport corridors within the

European Union (EU) suggests the need to divert traffic from roads to alternative modes of

transport with the aim of reducing the costs derived from the high traffic concentration borne

by the former. This reduction would affect both social costs (related mainly to accident levels

and environmental degradation) and private costs (longer travelling time).

To achieve this goal, the development of Short Sea Shipping (SSS) has been contemplated in

European Transport Policy as a fundamental stretch of the intermodal chains. Although the

definitions given for SSS are numerous, according to the communication from the European

Commission of 29 June 1999 [1] the term refers to “the movement of cargo and passengers by

sea between ports situated in geographical Europe or between those ports situated in non-

European countries having a coastline on the enclosed seas bordering Europe”.

The main advantages associated with SSS compared to road transport are: its lower

number of accidents, its low infrastructure costs, its capacity to reach ultra-peripheral regions

and its lower impact on the environment (authors such as Vanherle et al. [2] have made

important clarifications regarding this subject). However, notwithstanding all these

advantages, road transport continues to be predominant for channelling the flow of intra-

communitarian freight. The main reasons why transport decision-makers are reluctant to

adopt intermodal transport are related to the perception of SSS as an out-dated, slower, rigid

and complex alternative from an administrative point of view, in addition to being less reliable.

Potential users thus, tend to consider SSS to be a less satisfactory option than road transport

when offering a door-to-door service [3, 4].

In an attempt to invert this perception, the White Paper on Transport (2001) introduced

the concept of the Sea Motorway as a tool to develop SSS. Motorways of the Sea are not only

maritime corridors but also a network of ports and intermodal services which are able to offer

a door-to-door transport through their correct integration in the intermodal chains for a

particular zone of the EU [5]. In 2003, the European Commission revised the Trans- the

European Network of Transport (TEN-T) including as an objective for 2010 the implementation

of Motorways of the Sea in 4 corridors (TEN-T Project 21): i) the Baltic Sea Motorway; ii) the

Western Europe Sea Motorway ; iii) the South-East Europe Sea Motorway; and iv) the South-

West Europe Sea Motorway, connecting Spain, Portugal, France, Italy and Malta, and linking

up with the South-East Europe Sea Motorway, including links to the Black Sea. Despite this

political support, the last decades have witnessed relevant public investments in land-based

transport infrastructures but this financial support was not equally applied to seaway

infrastructures whereby, a distorted transport marketplace was generated in which has

resulted in a favourable situation for the of land transport [6]. Consequently, the responsibility

for the establishment of the successful Motorways of the Sea and their correct integration

within the intermodal chains was left by the most member states to the private initiative [7].

One exception however, is the case of the Motorways of the Sea between Spain and France in

the Atlantic. On this occasion both countries agreed to boost the most suitable and viable

Motorways of the Sea projects [8], which met the requirements demanded, subsidizing up to

30% of the operative cost during the first three operation years.

Aside from the choice of ports of call, the setting-up of Motorways of the Sea requires the

establishment of a frequency of service for these connections, which will make it possible to

offer a door-to-door service in similar conditions of cost and quality to those offered by road

transport alone [9]. In other words, the success of the Motorways of the Sea depends on their

capacity to integrate their services in an intermodal chain of transport without additional costs

due to bureaucracy or inefficient port services [5, 10]. The same principle is applicable to SSS

services, since Motorways of the Sea are specific short sea shipping services. All this makes

the selection of the port critical for the success of the Sea Motorway [5]. The selection of the

ports, and therefore the maritime routes chosen in the intermodal transportis aspect was

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nlymainly tackled from two different approaches: from the point of view of the shipping

companies and from the modal choice perspective. In the first group the works devoted to

port selection within the SSS framework are very scarce and tend to be limited to the water

borne context (the Motorways of the sea projects, which were presented by the shipping

companies to take advantage of the public funds in the framework of the Bilateral Agreement

between France and Spain for the development of the Motorways of the Sea, are very

significant to this regard [8]), whereas in the second approach, the analyses are focused on

establishing the minimum thresholds of distances between ports or between end routes to

ensure the competitiveness of the intermodal transport versus other alternatives.

Nevertheless, these ranges of distances try to offer general recommendations from the

study of particular cases approached as ‘one origin to one destination’ models. Hence, in

accordance with the conclusions of previous researches the Motorways of the Sea through

Atlantic ports prove to be interesting for maritime inter-port distances ranging between 834

and 1400 km (see projects from the IV Framework programme of the European Commission

[11], or those financed by the Spanish Ministry of Development [12]). Likewise, the EU [1] has

also recommended respecting the minimum threshold of 1385 km of land distance to establish

an intermodal chain of transport (these references were established for the European case)

which is very close to the recommendation of Jiang et al. [13] who proposed 1400 km. The

WEST MOS project, in 2008 [14] also concluded a minimum land distance of 1000 km for the

use of intermodal transport in Spain. Other projects even proposed thresholds for the relative

weight of the road haulage into the intermodal chain to keep the interest in this transport

mode. Thereby in 2005, the INTEGRATION project [15] suggested land stretches should be as

much as 60% of the intermodal route distance, while Ametller [16] affirmed that for the case

of intermodal transport from Spain and a total distance of 1600 km the maximum road

haulage should be 50%. For other contexts, see for example Brooks and Trifts [17].

This paper propose to adopt the perspective of the transport service as a whole

considering that the decision maker of the transport mode is the loader, in this case, the actual

owner of the goods. As a consequence, the transport need demanded by the decision maker

will be a door-to-door service, this forces to evaluate the transport modes according to their

capacity to offer this transport service. On the other hand, the intermodal transport through

Motorways of the Sea must be analysed as a ‘many to many’ transport model. This is a

transport network with many possible origins and destinations, nodes, which share a common

trunk haul: the Sea Motorway [18], which is identified by a pair of ports (hubs). According to

previous considerations, the aim of this paper is to provide a method able to identify the ports

that form the most suitable maritime route to support an intermodal chain that is competitive

with respect to road transport. To support the comprehension of the proposed method, a case

study: the implementation of a Sea Motorway that connects the Atlantic Coastline of Spain

and France, has been used as an application example in this paper.

The method was split into two parts. The first one aims to rank quantitatively the maritime

routes, for this a multi-criteria decision matrix has been initially defined. In a second step a

Monte Carlo simulation was applied to the matrix with the intention of meeting the risk

assumed with the decisions made through the evaluation of the matrix, and to identify the

most influent variables on the success of the intermodal transport.

The first step: the method for the quantification of the results

The multi-criteria decision method has been applied in combination with the analysis of

scenarios by other authors for the selection of ports [19, 20]. However, most of these studies

were focused on the tramp traffic and approached from the point of view of the shipping

company using data obtained from the stated preference methods. The present paper aims to

evaluate different possibilities of maritime routes (Motorways of the Sea) but integrated into

the intermodal chains. This involves that, the competitiveness evaluation of the whole

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nlyintermodal route (door-to-door) must be carried out from the point of view of the actual

owner of the goods. For this purpose a multi-criteria decision method has been applied where

the competitiveness in terms of time and cost (evaluation criteria) of the intermodal chains

versus the road is analysed. Thereby, the results obtained can be evaluated through a three-

dimensional matrix formed by dimensionless relevance indexes.

A useful and realistic method for the transport decision maker (the actual owner of the

goods), as said before, must necessarily contemplate door-to-door transport needs [10,

21].Nonetheless, from a conceptual point of view the articulation of the transport network

must respond to a ‘many to many’ model, as such models are capable of reflecting the widest

possibilities of transport routes. The ‘many to many’ transport models can be characterized by

having a combination of nodes, or extreme points in the routes, and hubs, or cargo

consolidation centres [18]. In the application of this transport model to the method proposed

in this paper, the nodes correspond to final points of the intermodal routes and the hubs to the

ports which define the Motorways of the Sea (see Figure 1). The number and location of nodes

and of hubs is discrete and deterministic. This means the method considers that the number of

end routes and possible ports must be finite and previously defined by the analyst.

Insert figure 1 about here

For the analysis the following assumptions have been made:

• The study is undertaken by considering one direction. Thus, every port of the

departure coast is independently studied by taking into account all the connections

from that port to all the possible ports of the delivery coast K={1,2,3…k}.

• For this analysis an identical fleet of ro-ro vessels (the same cargo capacity and

speed) has been considered for all cases studied.

• The transport alternatives in the analysis are the road and the intermodal transport

(i=1,2).

• The end routes on both coasts (nodes) have an associated possibility that the load

departs from this end route(��), with respect to all other possible departure end

routes (H={1,2,..h}), or it delivers (��) to a concrete end route (J={1,2,..j}).These

weighting factors should be defined by considering the scenario conditions

evaluated. As a first approach, these can be assumed as unitary population factors

(1) and (2) bearing in mind the location of the main production and consumer

centres [22, 23].

�� =��

∑ �� ∑ �j� = 1 ∀ j ∈J (1)

�� =��

∑ �� ∑ γ

h� = 1 ∀ h ∈ H (2)

• The analysis considers the competitiveness of the transport alternatives in terms of

the cost during an elapsed time period in the past. For this, the study takes into

account the evolution of the traffic pattern between each pair of ports in the range

of years considered [24] N={ 1,2,..n} through their weighting with respect to the

yearly cargo volume moved (in tonnes).

�� =��

∑ �� ∑ �n� = 1 ∀ n ∈ N (3)

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nlyOwing to the fact that volume of trade and the evolution of transport costs are inversely

related, the flows of freight could benefit from a reduction of the latter. Such a reduction

could be in monetary terms or in terms of time [25]. Likewise, the time and cost variables have

been remarked as decisive factors in the selection of the transport mode by numerous

previous studies [26-28].Consequently, the competitiveness of the transport modes was

evaluated according to the time and the costs expended on each route. Assuming the previous

points, indexes to assess each of these criteria, as in (4) and (5) are proposed, where (4) is the

cost index and (5) is the time index. Both indexes are normalized according to the two

transport alternatives which have been taken into consideration (i)

�� = 1 −

"�#��$�∑ "�#��$�%#&'

∑ �� (

�)* = 1 (4)

��+ = 1 −

+��#��$∑ +��#��$%#&'

∑ ��+(

�)* = 1 (5)

Thus, the closer the index value is to 1, the less time/cost will be needed with respect to its

alternative. Therefore the interest of the loader in that transport mode versus the alternative

one will be higher when evaluating only each pair of nodes h and j (like a ‘one to one’ model,

this is, all transportation load departures from the same point h and it deliveries to the same

port j) through two ports (the studied port and the port k).

Once the indexes have been calculated, the assessment of each Sea Motorway alternative

is aggregated. This aggregation is done using Relevance Indexes (RI), defined according to (6)

and (7). In this case, they are interpreted as the probability (or relevance) of the decision-

maker choosing a particular transport option (mode and route), taking into account the

transport attributes (in terms of time and cost) to the all extreme routes defined (‘many to

many’ model approach).

,�� = ∑ ∑ (�� × .��

× ��/)�� ∀ h ∈ H; ∀ j ∈ J (6)

,��+ = ∑ ∑ (�� × .��

+ × ��/)�� ∀ h ∈ H; ∀ j ∈ J (7)

It is worthwhile to know the advantages that the intermodal alternative offers compared to

road transport. For this purpose, the Differential Indexes of Relevance (DIR) in terms of time

and cost have been defined according to (8) and (9).These indexes facilitate the comparison of

the competitiveness associated with the intermodal chains articulated through different

Motorways of the Sea.

0�,�+=,�(�

+ - ,�*�+ (8)

0�,�� =.,�(��

1 - ,�*��1 / × β� (9)

Indeed, the Differential Indexes of Relevance can be assumed as the final Port Indexes (PI),

because they are able to reflect the appeal of each Sea Motorway versus the road alternative

for transport users. The higher the index value, the higher the appeal of the intermodal option

is. Thereby, in terms of time, the Port Index matches the respective Differential Index of

Relevance (�232��+ = 0�,�

+). However, this coincidence does not occur in terms of cost. In

this case the Differential Index of Relevance (0�,�� ) is related to a particular year (n), whereas

the Port Index must take into account the total period of time considered, as shown in (10):

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Italic

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nly

�232�� = ∑ .0�,��

/� ∀ n ∈ N (10)

The assessment model for the case of France-Spain

For this particular case, it is important to bear in mind that the amendment of the TEN-T led

to the signing of a bilateral agreement between Spain and France in 2006 to promote the

development of Motorways of the Sea by linking up their respective Atlantic coastlines (‘The

declaration of intentions about the Motorways of the Sea’,[8]). As a result, an

intergovernmental commission was created with the aim of drawing up a proposal for the

selection of Sea Motorway projects. The requirements are: i) a service frequency of at least 4

departures per week each way during the first two years of operation; ii) a frequency of at

least 7 departures per week each way once these 2 years have elapsed; iii) annual traffic of at

least 350,000 semi-trailers should have been reached at the end of 5 years; and iv) it should

have risen to 850,000 after 10 years. The selected projects would receive additional resources.

Taking into account the date of this agreement and the convenience of the medium term

study a time range of 4 years from 2006 to 2009 (N=1,2…,4) has been assumed for the

analysis:. In this manner, the real advantage obtained by the use of intermodal transport

during these years versus the road alternative can be demonstrated.

Afterwards it is necessary to identify the end points of the routes on both coasts. For this

purpose the most consumption and production centres of load, which could be transported

through SSS in the Atlantic were assumed as the most probable end routes. In the case of

France, these are coinciding with the most populated cities; these should be considered as

possible end points. In 2008 the most populated cities in France were (in thousands of

habitants according to INSEE, 2009, French Government [29]): Paris 11,694, Lyon 6,121, Lille

4,022 and Rennes 3,139. Excepting Lyon, the rest of the cities could be reached from the

Atlantic coast specifically through the ports of St.Nazaire, Le Havre and Calais. For the case of

Spain the situation is different. The most important ports on the Atlantic coast are also the

most important production centres of their hinterlands [30], therefore the land distance on

the Spanish coast can be rejected with respect to the whole intermodal chain. Consequently,

the end routes in Spain can be assumed as the ports and the ‘many to many’ transport model

previously presented (see Figure 1) must be adapted to this situation through a ‘one to many’

model [18] (see Figure 2). Hence, in this particular case:

• The studied ports were those Spanish ports on the Atlantic coast which are

considered as Category A ports (they have international relevance and an annual

volume of traffic of over 1.5 million tons of freight or 200,000 passengers). These

are: Vigo, Ferrol, A Coruña, Gijón, Santander and Bilbao. On the other coast the

French ports (k) considered were: St.Nazaie, Le Havre and Calais; K={1,2,3}.

• For the analysis of the time invested in the maritime stretch a fleet of ro-ro vessels

with cargo capacity of 157 trucks each (1960 lane meters) and with a service speed

of 30 knots (kn) has been assumed. Additionally, a constant loading speed at all

ports of 34 trucks per hour (trucks/h) (information provided by Spanish Stowage

Society) has been estimated for all ports studied.

• The transport alternatives in the analysis were the road and the intermodal

transport (i=1,2).

• The end routes on the Spanish coast were coincident with the ports, therefore for

each Spanish studied port it is always h=1, while the French nodes were: Rennes,

Paris and Lille; J={1,2,3}.

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nlyInsert figure 2 about here

To calculate the duration of the road haulage, the European legislation determines the

minimum rest periods for the drivers [31] and the maximum truck speed allowed: 90 kilometre

per hour (km/h) [32, 33] was taken into account. Likewise, to calculate the cost indexes

corresponding to the maritime stretch (see Table 1), estimations used in the INECEU Project

[12] were replicated by considering the influence of the time in the costs [34]. For road

distances, the annual data given by the Observatory of Road Freight Transport Costs [35] were

considered. Finally, the same port dues were assumed for all facilities (Vigo’s dues were taken

as a pattern).

Insert table1 about here

Figure 3 shows the location of the ports and end routes selected for this particular

application. Considering Table 1 and Figure 3, it can be concluded that, the French port (k) of

Le Havre is the closest to all the possible end routes in France (j) by road (16% of the average

land haulage), whereas the port of St.Nazaire articulates the intermodal chains with highest

land stretches (average of 32% land haulage).


Integrating these results, finally, Port Indexes in terms of the time �232��+ and the cost

�232�� have been obtained which can each be appreciated in the Figures 4 and 5 respectively.

In terms of the time all the indexes are positive for the intermodal transport in all studied ports

from Vigo to Gijón on the Atlantic Spanish coast. The situation changes, however, when the

studied ports are closer to the Pyrenees (Santander, and Bilbao), the same occurs for the

indexes in terms of the cost (see Figure 5). This confirms the conclusions obtained by previous

authors, such as Olivella et al. [12] who affirmed that the competitiveness of the intermodal

routes from Spain to France decreases when the ports are located close to the Pyrenees.

Considering the indexes values obtained in terms of the time and the cost (see Figures 4

and 5), the range of values is larger for cost indexes, as was expected [36, 37], thereby, the

most restrictive attribute of the competitiveness is the time. In fact this becomes critical.

Taking into account both transport attributes the best Motorways of the Sea to articulate

intermodal routes with the highest opportunities of success versus the road from a

quantitative perspective are: Ferrol-St. Nazaire, A Coruña-St. Nazaire and Vigo -St. Nazaire.

As regards costs, Le Havre and St. Nazaire are very close in terms of competitiveness of the

intermodal routes which are articulated through them (see Figure 5). Nevertheless, in terms of

the time (see Figure 4), St.Nazaire followed by Le Havre articulates the most competitive

intermodal routes versus the road for the Spanish Ports with positive indexes (Vigo, Ferrol, A

Coruña and Gijón). This is true despite the fact that St. Nazaire is the furthest French port from

the end routes in France (considering the average land distances). This point is important

because the distance between end point and the port is one of the main attributes considered

by the loaders when selecting delivery or departure ports [10, 38]. Therefore, each possible

door-to-door route must be analysed with more attention through the assessment of the time

indexes (5) ��+ (see Table 2).

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nlyInsert figure 4 about here


Table 2 shows the time indexes (5) for every routes (for all cases h=1), the non-competitive

indexes (��+ < 0,5)have been highlighted. As said before, these indexes evaluate the

interest of the loader in a transport mode versus the alternative one by assuming a ‘one to

one’ transport model. Through the assessment of this Table, numerous cases where the most

competitive intermodal routes in terms of the time are not those with the least distance for

land haulage can be found. For example, on the Vigo-Lille route the closest port to Lille is

Calais, so through this port the land haulage is 7.5% of the chain and the time index is

IT

2133=0.60 whereas the intermodal chain of Vigo-St.Nazaire-Lille with a land haulage of 42%

achieves IT

2131=0.61.

Moreover, numerous cases have been detected where the recommended range of

maritime distances for the transport through SSS is not accomplished, but they are however

shown as competitive routes in terms of the time and viceversa. For instance, as Vigo-Le

Havre-Rennes, this route has a land haulage distance of 18% (less than the 30 % is

recommended by Ametller X. [16]) and the land distance between Vigo-Rennes is 1453 km

thus, reaching all the minimum distances recommended by previous authors: Jiang et al. [13];

EU recommendation [1]. Furthermore, the maritime distance of Vigo-Le Havre (Table 1) also

meets the minimum distance recommended: 833 km by SPC-Spain [39] and INECEU project

[12]. Notwithstanding all these aspects the intermodal route is still not competitive with

respect to the road in terms of time (IT

2122=0.49).


Thus, according to the analysis undertaken, the application of the recommended distances

and minimum thresholds is not enough to ensure the competitiveness in terms of the time (a

critical parameter) for intermodal chains versus the road. Moreover, the proximity of a port to

the consumer or production centre has not been shown as the most suitable attribute for port

selection regarding the intermodal transport. As a result, the selection of an intermodal route

should be considered for every concrete scenario and for the door-to-door needs [40].

The second step: the sensitivity analysis and the risk assessment

Owing to the fact that the inputs used for the construction of the multi-criteria matrix in

the first step of the method are related to a temporal range (∀n ∈ N), their values are the

result of forecasts or estimates. This necessarily implies to accept a risk level on the variables

used (uncertainty variables or inputs) and therefore on results obtained. Consequently, the

decisions made through the quantitative evaluation of the Port Indexes (see expressions 8 and

10 and their values in Figures 4 and 5) can involve some mistakes. Thus, to assess the goodness

of a particular Sea Motorway selection, a risk analysis of the Port Index values was carried out

by taking into account the variation in the variables contemplated. For that, once the multi-

critreria decision matrix has been performed, the most uncertainty variables were firstly

identified; the variation of these led to different scenarios which should be analyzed.

Notwithstanding this, there are different methods for the simulation of the scenarios. In this

case, the Monte Carlo method (already used in this field by other authors [41-43]) was chosen

because it allows the evaluation of all selected variable variations at the same time to calculate

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nlythe Port Indexes according to an initially determined probability distribution for all variables.

This signifies a notable advantage with regard to most of the methods for simulating scenarios

(Herz, Unidimensional, etc.) which uniquely contemplate static scenarios (through an

optimistic or pessimistic approach.

The probability distributions for the value of the Port Indexes and their sensitivity analysis

were thereby obtained from Monte Carlo simulations. On the one hand, this allows

ascertaining the risk associated with the selection of the ports. For that purpose the Port

Indexes were evaluated to check whether these indexes were good estimators for the

corresponding distributions obtained and, consequently, whether the decisions made on the

basis of the values of these indexes would be reliable. On the other hand, the sensitivity

analysis obtained from the simulation allows determining the influence of the different inputs

(controllable and non-controllable variables) assumed for the construction of the decision

matrixes.

In the particular case of Spain and France in the Atlantic coast, all the scenarios evaluated

were generated in a past period of time (2006-2009) this involves that the variables values

were already known (they are not the result of forecasts). However, these were provided by

different information sources through diverse formats (formulations and data), therefore a

reasonable level of risk was also recognized in this case. For this reason triangular probability

distributions being assumed for all variables [44,45] with a variation range of 20% between the

most and least probable values. The amount of tests carried out in each simulation was

1,600,000, with a 100% certainty for all results obtained.


For each simulation, the statistical data related to the real probability distribution of the

Indexes (and the theoretical distribution that best fits) and the influences of each variable on

the Port Indexes were obtained. This last point will also allow the risk to be quantified in

association with the lack of control over any variables, and to explain the results achieved in

the risk analysis.

The measurement of the goodness of the Port Indexes as estimators was carried out by

considering three features:

• Bias: measured as the difference between the mean and the base case (value of the

index obtained through the expressions provided).

• Consistency level: considered as the proximity from the base case to the most

probable value in the distribution.

• Efficiency level: the lowest standard deviation means the highest efficiency level for

the indexes [44, 46]. This was measured through the Coefficient of Variability.

The analysis of the Port Indexes in terms of the time 89:;: for the case of

France-Spain

For the calculation of the Port Indexes in terms of time, the following inputs were assumed

as variables: population factors (αj), the loading/unloading speed in port, the truck speed and

the vessel speed. The values of these inputs were taken as uncertainty variables and so could

change in other scenarios. This fact implies a risk for the value of the Port Indexes and, hence,

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nlyfor the selected route. Accordingly, these inputs were considered as variables in the

simulations for this Index.


Figure 7 shows the probability distributions shapes obtained from the simulations

undertaken for each port. As can be seen in this figure, all the probability distributions shapes

of the Port Indexes are very similar for the intermodal routes articulated through the maritime

routes from the same Spanish port to every French ports (in Figure 7 in horizontal sense, e.g.

Vigo-St.Nazaire, Vigo-Le Havre, Vigo-Calais). Nevertheless, the opposite is not met. In other

words, in terms of the time the choice of the Spanish port from a French one involves more

risk for the intermodal transport than the choice of the French port from a Spanish one. This is

mainly due to the coastal geography of the countries (Spain is a peninsula see Figure 3) and to

the fact that ‘one to many’ transport model is assumed for the case of France-Spain (see Figure

2). The latter implies that the election of the Spanish port from the French side, not only

determines the Sea Motorway, but also the extreme point of the whole route (for the

intermodal chain and also for the unimodal transport). However, independently on the French

port selected, from one Spanish port the end routes were always the same for the unimodal

and intermodal transport.

The distributions shapes obtained (continuous for the routes articulated from Vigo to Gijón

on the Spanish coast) can be explained by taking into account the results obtained from the

sensitivity analysis (see Table 3). According to the two parameters that influence greatly on the

variance of the�232��+: the truck speed (negative influence) and the vessel speed (positive

influence). The relative importance between them determines the shape of the distributions

obtained.


Indeed the influence of the truck speed on the �232��+ is greatly governed in the European

Union by the Regulation 561/2006 [31] which determines the driver’s maximum continuous

driving hours per day along with rest periods. This leads to stepped functions for the time

invested in the road transport which likewise influences on the probability functions shapes

obtained for the Port Index. Consequently, depending on road route geography slight changes

in truck speed could mean a difference of one day’s travel.

Even though the influence of the truck speed is a very important factor in all cases, its

effect is moderate for the case of Vigo and Gijón due to the higher influence of the vessel

speed in these ports. As a consequence, the positive and negative contributions balance

themselves out for the case of Vigo, whereas in the case of Gijón the positive contributions are

even higher than the negative ones (the real distribution is actually very close to a continuous

beta distribution).

Here it is important to pay attention to the fact that the intermodal routes articulated

through the Le Havre port have the shortest road haulages distances. For this reason their

competitiveness versus the road is highly influenced by the truck speed. The opposite occurs

for the intermodal routes through St.Nazaire (those with the largest road haulage distance),

their competitiveness is the least dependent on the truck speed as these routes also take

advantage (positive influence) of an increase of the speed on the road haulage. Finally, as

expected, while the relative importance of the vessel speed increases with maritime distance,

the relative weight of the loading/unloading speed is inversely proportional to the maritime

route [47].

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nlyIt is interesting to note that routes with Indexes which are less dependent on the truck

speed (non-controllable variable) are those with the highest improvement range. In this case

they are the routes articulated through Gijón and Vigo to any French port (see Table 3).

Table 4 shows the values obtained from the simulations carried out using the Monte Carlo

method. Differences between the index values and the means reflect that the bias taken in the

selection of the port is low for all cases. However, the level of dispersion of the data is high,

with elevated standard deviations and coefficients of variability. Nonetheless, the preferential

order of the considered optimal routes remains the same due to the consistency of the index

in all the cases and the distance in absolute values of the Indexes among ports.


Looking at the coefficients of variability at Table 4, the lowest dispersion has been achieved in

the intermodal routes through Gijón-St.Nazaire (0.118), Ferrol-St.Nazaire (0.188), A Coruña-

St.Nazaire (0.215) and Vigo-St.Nazaire (0.278). Therefore, taking into account the risk analysis,

the Indexes in these routes are good estimators (their port indexes are centred, efficient and

consistent). As such, for these routes the decisions made through the quantitative analysis of

values of Port Indexes in terms of the time does not involve risk.

From the risk assumed and the dependence on non-controllable variables obtained from

sensitivity analysis of the Port Indexes in terms of time, the most suitable routes would be

Gijón-St.Nazaire and Vigo-St. Nazaire. Moreover, these routes obtained very close absolute

values for the Port Indexes in the decision matrix �232��+ = 0.11 (see Figure 4).

The analysis of the Port Indexes in terms of the cost 89:;= for the case of

France-Spain

For the risk analysis of this index, the assumptions taken into account were: population size

at the French route endpoints (��), the weighting factor regarding the recorded annual volume

of traffic between France and Spain(��), the land transport cost per km and the freight per

transport unit (considering the fuel cost and the capital cost for the shipping company

independently).

The probability distributions shape is shown in Figure 8. According to this, it can be

concluded that in all cases the theoretical probability distribution (beta) fits the real

distribution. This already forwards a reduction of the risk associated to the decisions made

through this index (�232�� ) in comparison to the previous index (�232��

+). In this case, all the

distributions are continuous (there are no important steps).This is mainly owing to the fact

that the total influence of all the variables for all the routes is positive (see Table 5).


Insert table 5 about here

Table 5 shows the results from the sensitivity analysis. Once again, the most influential

variable on the index value is related to the truck: the cost per km. For each Spanish port, the

positive influence of the road cost increases when the influence of the road stretch in the

intermodal transport decreases. Namely, the intermodal routes with large maritime stretches

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nlytake advantage of the effects of economies of scale. Therefore an increase of the cost per

kilometre by road still favours more this advantage in costs. So the influence of this variable is

higher for the maritime routes to Calais versus those to St.Nazaire when the Port Indexes are

positive, this is true from Vigo, A Coruña and Ferrol. As the cost per kilometre by road is not

controllable the preferable maritime routes are those which articulate intermodal routes

which are less influenced by it. This is so for the maritime routes from Vigo, A Coruña and

Ferrol (see Table 5). Furthermore, as expected, the influence of the loading/unloading costs

increases with decreasing maritime distance, but total freight costs exert little influence on the

index value for all the routes.

On the other hand, it is worth noting that the influence of the population factor on the Port

Index is not only dependent on the average distance from the city to the port, or on the

absolute value of the population factor for one city versus the others, but rather on a

combination of all these factors. Hence once again, the attribute of the distance between port

and end route is under discussion now in terms of the cost.


The statistical results obtained for all the routes (see Table 6) show, as expected (see Figure

8), a high coincidence between the respective value of the Port Indexes in terms of cost and

the means of their simulated distributions. Furthermore, the indexes are efficient and

consistent. These are, consequently, good estimators of the probability distributions obtained

and, the risk associated with the selection of all the routes considering the absolute value of

this index is, therefore, low. Thereby, the analysis of risk for the Port Index in terms of the cost

in the study case is not selective.

Conclusions

The method presented involved two evaluations of the intermodal transport articulated

through different Motorways of the Sea based on the ‘many to many’ transport model: a

quantitative and qualitative analysis. The method may be used to make an initial assessment

of the alternative potential routes in order to develop a Sea Motorway that articulates

competitive intermodal chains versus unimodal transport. In the first step of the method a

multi-criteria decision matrix is built through different indexes. From this first step applied to

the study case presented in the paper, the case of France-Spain, it can be concluded that the

advantage of the intermodal services versus unimodal transport is especially noticeable when

considering the cost of the service. As expected, the time criterion is the most restrictive point

with respect to the competitive advantage of intermodal transport. Taking both perspectives

into account, the intermodal alternative is preferable to its unimodal counterpart for all the

maritime routes from the Spanish ports: Vigo, A Coruña and Ferrol to the French ports (see

Table 7). The port of Gijón represents an inflexion point where the Indexes start to take

negative values towards the Pyrenees until Bilbao. The maritime routes with the highest

potential for success from a quantitative point of view are A Coruña-St.Nazaire, Ferrol-

St.Nazaire and Vigo-Saint Nazaire (see Table 7).

In the first step it is important to note that the competitiveness of the intermodal route in

terms of the time does not increase when the road haulage is lower. Hence, this feature for

the selection of the ports in the intermodal chain is under discussion. Additionally, in this step

there are many occasions where the traditional distance ranges recommended for the

intermodal chain are met but the chains are not competitive versus the road transport.

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nlyConsequently, the generalization of distance ranges is not recommended to ensure the

establishment of competitive intermodal chains versus the road.


As the value of the indexes depends on the assumptions considered to calculate them, in

the second step it has been convenient to analyse the risk assumed when selecting a particular

alternative through the evaluation of the indexes. The Monte Carlo simulation was the chosen

tool for this purpose. The results obtained from the simulations allow for stating that, while

the Port Indexes in terms of the cost are good estimators of the probability distributions

obtained for all cases, the Port Indexes in terms of the time just proved to be good estimators

for the routes: Gijón-St.Nazaire, Ferrol-St.Nazaire, A Coruña-St.Nazaire and Vigo-St.Nazaire.

According to this method, the risk assumed by accepting the results of Port Indexes associated

with intermodal versus unimodal transport on these routes is low (see Table 7).

The Monte Carlo simulation also led to the drawing of conclusions from the sensitivity

analysis. For both indexes, the most influential variable is a non-controllable one: the truck

speed (limited by regulations) and the road cost (limited by the cost of gasoil in the trade),

respectively. Thereby, routes with the least sensitive indexes to these variables have a larger

improvement range through their own capacities (they are more sensitive to controllable

variables). The influence of the truck speed (negative) on the Port Indexes in terms of time is

less for the intermodal routes with longest land stretches (see Table 7). The opposite occurs,

however, regarding the influence of the road cost on the positive Port Indexes in terms of cost.

Considering the time advantage as being preferential and the independence of the route

with respect to the non-controllable variables, the selected routes should be Vigo-St. Nazaire

and Gijón-St.Nazaire (see Table 7). Finally, it should be noted that the influence of the truck

speed on the competitiveness of the intermodal transport, in the European Union, is not

continuous but occurs in steps. Therefore for many routes, slight modifications in truck speed

lead to high differences in the competitiveness of intermodal transport. This point is highly

dependent on the geographical location of the routes as well as on the European Regulation

for road transport. Thereby, the results achieved in this paper confirm that the European

Regulations about maximum permitted speed and the driver’s maximum continuous driving

hours on road can be used as efficient policy instruments to discourage the European road

freight transport [48]

On the other hand, it was concluded that the loading/unloading speed is highly relevant in

the intermodal routes with the shortest maritime routes as previous authors had noted.

Finally, from the results achieved the total freight costs were not so relevant with regard to

competitiveness in terms of cost as expected. This paper has accordingly also tried to order

and quantify the main factors that determine the competitiveness of intermodal versus

unimodal transport in order to act on them.

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Figure 1. Model ‘Many to many’ applied for the study of the transport alternatives. 223x135mm (96 x 96 DPI)

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Figure 2. Model ‘One to many’ applied for the study of the transport alternatives in the case: Spain-France. 152x95mm (96 x 96 DPI)

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Figure 3. Model ‘One to many’ for the studied port of Vigo to France into the application case of Spain-France.

441x252mm (96 x 96 DPI)

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Figure 4. Port Indexes in terms of the time PI<k></T> for the case of France-Spain. 206x167mm (96 x 96 DPI)

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Figure 5. Port Indexes in terms of the cost PI<k></C> for the case of France-Spain 215x167mm (96 x 96 DPI)

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Figure 6. Triangular distribution selected for all variables of the scenarios obtained through the Monte Carlo simulations

137x73mm (96 x 96 DPI)

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Figure 7. Probability distributions of the Port Indexes in terms of time PI<k></T> obtained in the Monte Carlo simulations for the case of France-Spain

150x125mm (96 x 96 DPI)

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Figure 8. Probability distributions of the Port Indexes in terms of cost PI<k></C> obtained in the Monte Carlo simulations for the case of France-Spain

151x122mm (96 x 96 DPI)

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Spanish Ports (Studied Port) French Ports(k) Dk (Km) French Cities (j) dj (Km)

Vigo

Calais 1390 Rennes 1453

St. Nazaire 915 Paris 1577

Le Havre 1232 Lille 1793

Ferrol




A Coruña




Gijón




Santander




Bilbao




Source: Spanish Merchant Navy Association.

Table 1. Maritime Distances D<k> and road distances ‘door to door’ d<j> for the routes studied

in the case of France-Spain

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Table 2. Time indexes I <ihjk></T> for the routes studied in the case France-Spain

St. Nazaire Le Havre Calais

Truck SSS Truck SSS Truck SSS

Studied Ports French cities (j) IT11j1 I

T21j1 I

T11j2 I

T21j2 I

T11j3 I

T21j3

Vigo

Rennes 0.45 0.55 0.51 0.49 0.55 0.45

Paris 0.47 0.53 0.49 0.51 0.52 0.48

Lille 0.39 0.61 0.39 0.61 0.40 0.60

Ferrol

Rennes 0.42 0.58 0.49 0.51 0.53 0.47

Paris 0.44 0.56 0.47 0.53 0.50 0.50

Lille 0.36 0.64 0.37 0.63 0.38 0.62

A Coruña

Rennes 0.42 0.58 0.49 0.51 0.54 0.46

Paris 0.45 0.55 0.47 0.53 0.50 0.50

Lille 0.37 0.63 0.38 0.62 0.38 0.62

Gijón

Rennes 0.42 0.58 0.51 0.49 0.55 0.45

Paris 0.45 0.55 0.49 0.51 0.52 0.48

Lille 0.46 0.54 0.48 0.52 0.48 0.52

Santander

Rennes 0.67 0.33 0.76 0.24 0.79 0.21

Paris 0.45 0.55 0.51 0.49 0.54 0.46

Lille 0.46 0.54 0.50 0.50 0.50 0.50

Bilbao

Rennes 0.70 0.30 0.78 0.22 0.81 0.19

Paris 0.47 0.53 0.52 0.48 0.55 0.45

Lille 0.48 0.52 0.51 0.49 0.52 0.48

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Vigo Ferrol A Coruña

SN LH C SN LH C SN LH C

Truck speed -54.40 -55.40 -54.70 -71.00 -71.40 -71.10 -74.70 -74.80 -74.50

Vessel speed 31.00 35.90 37.80 15.70 21.10 23.00 14.30 19.00 20.50

Loading/Unloading speed 12.80 7.90 6.60 10.70 6.50 5.30 9.30 5.70 4.70

Lille (α3) 1.00 0.70 0.70 0.70 0.50 0.50 0.40 0.30 0.30

Paris (α2) 0.80 0.10 -0.10 1.70 0.40 0.00 1.20 0.30 0.00

Rennes (α1) 0.10 0.00 -0.10 0.20 0.00 0.00 0.20 0.00 0.00

Gijón Santander Bilbao


Truck speed -14.40 -20.70 -19.70 -73.10 -73.90 -73.50 -71.30 -71.20 -70.30

Vessel speed 37.70 58.50 63.30 10.60 19.00 19.90 11.60 19.30 19.60

Loading/Unloading speed 42.80 20.70 16.40 15.00 6.30 4.90 15.50 5.90 4.50

Lille (α3) 0.30 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00

Paris (α2) 4.10 0.10 -0.40 1.00 0.00 -0.50 0.00 -1.10 -3.00

Rennes (α1) 0.70 0.00 -0.20 -0.30 -0.80 -1.20 -1.60 -2.10 -2.60

Where SN is Saint Nazaire; LH, Le Havre; and C, Calais.

Table 3. Contribution of Inputs to the variance (%) of IPTPI<k></T> in the case

France-Spain

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Base Case: value �� 0.105 0.057 0.009 0.160 0.100 0.050 0.150 0.090 0.040

Mean 0.099 0.051 0.003 0.140 0.090 0.030 0.130 0.070 0.020

Median 0.103 0.055 0.007 0.150 0.090 0.040 0.130 0.070 0.020

Standard Desviation 0.028 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030

Coeff.of Variability 0.278 0.580 9.55 0.188 0.339 0.862 0.215 0.416 1.500

/Mean-Base Case/ 0.006 0.006 0.006 0.020 0.010 0.020 0.020 0.020 0.020



Base Case: value �� 0.110 0.020 -0.030 0.020 -0.090 -0.140 -0.010 -0.130 -0.180

Mean 0.110 0.020 -0.030 0.050 -0.060 -0.110 -0.110 -0.220 -0.270

Median 0.110 0.020 -0.030 0.020 -0.080 -0.130 -0.020 -0.130 -0.180


Coeff.of Variability 0.118 0.777 -0.440 0.953 -0.708 -0.375 -1.300 -0.630 -0.509

/Mean-Base Case/ 0 0 0 0.030 0.030 0.030 0.100 0.090 0.090


Table 4. Statistical results from the Monte Carlo simulations (in terms of time) in the

case of France-Spain

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€/Km 62.30 67.20 79.68 48.48 57.27 71.95 52.46 60.64 75.16

Paris (α2) 15.60 15.90 5.45 23.51 22.36 9.94 21.03 20.21 7.86

Rennes (α1) 3.50 0.30 -0.09 4.52 0.52 -0.02 4.54 0.46 -0.06

Lille (α3) 1.40 2.20 4.32 1.90 2.71 5.42 1.83 2.63 5.27

βn 12.60 9.40 4.43 17.86 12.81 7.21 16.15 11.45 5.98

The rest of the freight costs(€) -4.40 -4.70 -5.49 -3.46 -4.03 -5.08 -3.66 -4.23 -5.22

Freight costs due to fuel (€) -0.40 -0.30 -0.53 -0.27 -0.30 -0.38 -0.33 -0.39 -0.46



€/Km 97.32 87.79 90.85 89.58 92.08 80.32 89.59 85.23 67.20

Paris (α2) -0.02 2.88 -0.02 0.34 -0.03 -6.26 -2.30 -3.59 -15.06

Rennes (α1) -0.95 -0.01 -0.92 2.13 -0.72 -3.33 0.74 -1.79 -4.50

Lille (α3) 1.68 0.62 1.55 0.03 0.13 0.73 -0.18 -0.02 0.13

βn 0.02 2.34 0.02 1.22 -0.20 -3.24 -0.34 -2.87 -7.92

The rest of the freight costs(€) 0.00 -5.98 -6.25 -6.21 -6.30 -5.65 -6.28 -5.95 -4.74

Freight costs due to fuel (€) 0.00 -0.38 -0.38 -0.49 -0.54 -0.48 -0.57 -0.55 -0.45


Table 5. Contribution of Inputs to the variance (%) of IPTPI<k></C> for the case of

France-Spain

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Base Case: value �� 0.144 0.147 0.092 0.180 0.180 0.122 0.170 0.170 0.108

Mean 0.144 0.147 0.092 0.180 0.180 0.121 0.170 0.170 0.108

Median 0.144 0.147 0.092 0.180 0.180 0.121 0.170 0.170 0.107


Coeff.of Variability 0.058 0.067 0.098 0.049 0.057 0.076 0.050 0.060 0.083

/Mean-Base Case/ 0 0 0 0 0 0 0 0 0



Base Case: value �� 0.090 0.070 0.004 0.040 -0.010 -0.075 -0.020 -0.070 -0.131

Mean 0.090 0.070 0.004 0.030 -0.010 -0.075 -0.020 -0.070 -0.131

Median 0.090 0.070 0.004 0.030 -0.010 -0.075 -0.020 -0.070 -0.131


Coeff.of Variability 0.094 0.166 2.970 0.182 -0.597 -0.117 -0.358 -0.119 -0.072

/Mean-Base Case/ 0 0 0 0.010 0 0 0 0 0


Table 6. Statistical results from the Monte Carlo simulations (in terms of cost) for the

case of France-Spain

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1º Step:

Quantification of

results

��

� 0.105 0.057 0.009 0.160 0.100 0.050 0.150 0.090 0.040

��

� 0.144 0.147 0.092 0.180 0.180 0.122 0.170 0.170 0.108

2º Step: Sensitivity

analysis

Truck speed -54.40 -55.40 -54.70 -71.00 -71.40 -71.10 -74.70-74.80 -74.50

€/Km 62.30 67.20 79.68 48.48 57.27 71.95 52.46 60.64 75.16

2º Step: The risk

assessment

Coeff.of Variability on

��

� 0.278 0.580 9.55 0.188 0.339 0.862 0.215 0.416 1.500


��

� 0.058 0.067 0.098 0.049 0.057 0.076 0.050 0.060 0.083



1º Step:

Quantification of

results

��

� 0.110 0.020 -0.030 0.020 -0.090 -0.140 -0.010-0.130 -0.180

��

� 0.090 0.070 0.004 0.040 -0.010 -0.075 -0.020-0.070 -0.131

2º Step: Sensitivity

analysis

Truck speed -14.40 -20.70 -19.70 -73.10 -73.90 -73.50 -71.30-71.20 -70.30

€/Km 97.32 87.79 90.85 89.58 92.08 80.32 89.59 85.23 67.20

2º Step: The risk

assessment


��

� 0.118 0.777 -0.440 0.953 -0.708 -0.375 -1.300-0.630 -0.509


��

� 0.094 0.166 2.970 0.182 -0.597 -0.117 -0.358-0.119 -0.072


Table 7. Summary table for the application of the method to the Sea Motorways

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