Modelling Freight Generation and Distribution for Nationwide Interstate Freight Movement

  • Mounisai Siddartha Middela
  • Sasanka Bhushan Pulipati
  • C. S. R. K. Prasad
Original Article
  • 14 Downloads

Abstract

Freight modelling is very important to understand how freight moves from one place to another place. Due to data and other resource constraints like time and cost of conducting surveys, freight models are not available for various countries. Overcoming these constraints, freight generation and distribution models are developed using secondary sources of data. The primary motivation of the study is to understand the different factors on which freight generation depends upon. This is accomplished by building ordinary least squares regression models. Net State Domestic Product, Area, Agricultural Area, Secondary sector workers, Petroleum and Electricity Consumption of the Traffic Analysis Zones are the factors that affect freight generation. The secondary motivation of the study is to estimate the friction factors for the gravity model by calibration using three-dimensional furness procedure, being used for the first time in freight distribution. The important contributions of this paper are an improved understanding of freight movement patterns at the regional level and the estimation of friction factors that can be used for predicting future freight movements when origin–destination data are not available.

Keywords

Ordinary least squares regression Friction factors Tri-proportional approach Gravity model Three-dimensional furness procedure 

Notes

Acknowledgements

Most of the data utilized in this study were obtained from the website of the Planning Commission of India and the authors would like to acknowledge it. Also, the authors acknowledge the opportunity to present the research work that forms the basis of this article at the 12th Conference on Transportation Planning and Implementation Methodologies for Developing Countries (TPMDC) (India) held during 19–21 December 2016. They thank the reviewers for the comments on the manuscript which helped them in improving the paper.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

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References

  1. 1.
    RITES Ltd. (2007) Total transport system study. New DelhiGoogle Scholar
  2. 2.
    National Transport Development Policy Committee (2014) India transport report: moving India to 2032: volume 1: executive summary. Routledge, New DelhiGoogle Scholar
  3. 3.
    Ben-Akiva M, de Jong G (2013) The aggregate-disaggregate-aggregate (ADA) freight model system. In: Freight transport modelling. Emerald Group Publishing Limited, Cambridge, pp 69–90CrossRefGoogle Scholar
  4. 4.
    United States Department of Transportation, Research, Innovative Technology Administration, National Cooperative Freight Research Program, Cambridge Systematics, GeoStats LLP (2010) Freight-demand modeling to support public-sector decision making, vol 8. National Academies Press, Washington, D.CGoogle Scholar
  5. 5.
    Bovenkerk M (2005) SMILE+: the new and improved Dutch national freight model system. In: European transport conference, Strasbourg, 3–5 October 2005Google Scholar
  6. 6.
    Comi A, Donnelly R, Russo F (2014) Urban freight models. In: Modelling freight transport. Elsevier, pp 163–200Google Scholar
  7. 7.
    Holguín-Veras J, Jaller M, Sanchez-Diaz I, Wojtowicz J, Campbell S, Levinson H, Lawson C, Powers EL, Tavasszy L (2012) Freight trip generation and land use, No. NCHRP Project 08-80Google Scholar
  8. 8.
    Horowitz AJ (2006) NCHRP Synthesis 358: Statewide travel forecasting models. Transportation Research BoardGoogle Scholar
  9. 9.
    de Jong G, Vierth I, Tavasszy L, Ben-Akiva M (2013) Recent developments in national and international freight transport models within Europe. Transportation 40:347–371.  https://doi.org/10.1007/s11116-012-9422-9 CrossRefGoogle Scholar
  10. 10.
    Marzano V, Papola A (2004) Modelling freight demand at national level: theoretical developments and application to italian demand. European Transport Conference Strasbourg, pp 1–18Google Scholar
  11. 11.
    Sivakumar A, Bhat C (2002) Fractional split-distribution model for statewide commodity-flow analysis. Transp Res Rec J Transp Res Board 1790:80–88.  https://doi.org/10.3141/1790-10 CrossRefGoogle Scholar
  12. 12.
    Yang Y (2015) Development of the regional freight transportation demand prediction models based on the regression analysis methods. Neurocomputing 158:42–47CrossRefGoogle Scholar
  13. 13.
    Brogan JJ, Brich SC, Demetsky MJ, Board VCT (2001) Application of a statewide intermodal freight planning methodology, No. VTRC-02-R5. Virginia Transportation Research CouncilGoogle Scholar
  14. 14.
    Hancock KL (2008) Freight demand modeling: tools for public-sector decision making. Transportation Research Board, Washington, D.C.Google Scholar
  15. 15.
    De Jong G, Gunn H, Walker W, Widell J (2002) Study on ideas on a new national freight model system for Sweden. RAND, Santa MonicaGoogle Scholar
  16. 16.
    Kulpa T (2014) Freight truck trip generation modelling at regional level. Procedia Soc Behav Sci 111:197–202.  https://doi.org/10.1016/j.sbspro.2014.01.052 CrossRefGoogle Scholar
  17. 17.
    Ashtakala B, Murthy ASN (1988) Optimized gravity models for commodity transportation. J Transp Eng 114:393–408.  https://doi.org/10.1061/(ASCE)0733-947X(1988)114:4(393) CrossRefGoogle Scholar
  18. 18.
    Evans SP, Kirby HR (1974) A three-dimensional furness procedure for calibrating gravity models. Transp Res 8:105–122.  https://doi.org/10.1016/0041-1647(74)90037-9 CrossRefGoogle Scholar
  19. 19.
    Mao S, Demetsky MJ (2002) Calibration of the gravity model for truck freight flow distribution, No. UVACTS-5-14-14. Pennsylvania Transportation Institute, Pennsylvania State UniversityGoogle Scholar
  20. 20.
    JD Ortúzar, LG Willumsen (2011) Modelling transport.  https://doi.org/10.1002/9781119993308
  21. 21.
    Rwakarehe EE, Zhong M, Christie J (2014) Development of a freight demand model for the province of Alberta using public sources of data. Procedia Soc Behav Sci 138:695–705.  https://doi.org/10.1016/j.sbspro.2014.07.263 CrossRefGoogle Scholar
  22. 22.
    Shinghal N, Fowkes T (2002) Freight mode choice and adaptive stated preferences. Transp Res Part E Logist Transp Rev 38:367–378.  https://doi.org/10.1016/S1366-5545(02)00012-1 CrossRefGoogle Scholar
  23. 23.
    Tavasszy LA, Ruijgrok K, Davydenko I (2012) Incorporating logistics in freight transport demand models: state-of-the-art and research opportunities. Transp Rev 32(2):203–219CrossRefGoogle Scholar
  24. 24.
    Tavasszy L, De Jong G (2013) Modelling freight transport, 1st edn. Elsevier, LondonGoogle Scholar
  25. 25.
    Pendyala RM (2006) Modeling freight travel demand using a structural equations modeling framework. University of South FloridaGoogle Scholar
  26. 26.
    Tavasszy L, Davydenko I, Ruijgrok K (2009) The extended generalized cost concept and its application in freight transport and general equilibrium modeling. In: Integration of spatial computable general equilibrium and transport modelling bilateral joint seminar under agreement between NWO and JSPSGoogle Scholar
  27. 27.
    Zhao Y, Kockelman KM (2004) The random-utility-based multiregional input–output model: solution existence and uniqueness. Transp Res Part B Methodol 38:789–807.  https://doi.org/10.1016/j.trb.2003.10.005 CrossRefGoogle Scholar
  28. 28.
    Bilegan IC, Crainic TG, Gendreau M (2008) Forecasting freight demand at intermodal terminals using neural networks—an integrated framework. Eur J Oper Res 13:1–6Google Scholar
  29. 29.
    Williams I, Jin Y, Deane G (2005) Regional pilot for economic/logistic methods the EUNET2.0 freight and logistics model—final report, p 44Google Scholar
  30. 30.
    TetraplanAS (2009) Traffic flow: scenario, traffic forecast and analysis of traffic on the TEN-T, taking into consideration the external dimension of the union. Final reportGoogle Scholar
  31. 31.
    Korzhenevych A (2012) TRANS-TOOLS–an integrated support tool for European transport policy. Information zur Raumentwicklung 7/8:349–366Google Scholar
  32. 32.
    Ambrosini C, Gonzalez-Feliu J, Toilier F (2013) A design methodology for scenario-analysis in urban freight modelling. Eur Transp 54(7):1–21Google Scholar
  33. 33.
    Bröcker J, Korzhenevych A (2013) Forward looking dynamics in spatial CGE modelling. Econ Model 31:389–400.  https://doi.org/10.1016/j.econmod.2012.11.031 CrossRefGoogle Scholar
  34. 34.
    TML (2007) Final report TREMOVE. 1: European Commission, BelgiumGoogle Scholar
  35. 35.
    Bröcker J, Schneekloth N (2005) European transport policy and cohesion—an assessment by CGE analysisGoogle Scholar
  36. 36.
    Divya Priya C, Ramadurai G, Devi G (2015) Freight trip generation models for Chennai, India. Transportation Research Board 94th Annual Meeting Washington, D.C, pp 1–13Google Scholar
  37. 37.
    Patil GR, Sahu PK (2016) Estimation of freight demand at Mumbai Port using regression and time series models. KSCE J Civ Eng 20:2022–2032.  https://doi.org/10.1007/s12205-015-0386-0 CrossRefGoogle Scholar
  38. 38.
    Sahu PK, Patil GR (2015) Handling short run disequilibrium in freight demand forecasting at major Indian ports using error correction approach. Eur Transp Trasp Eur 1–19.  https://doi.org/10.1007/s12205-015-0386-0
  39. 39.
    Patil GR, Sahu PK (2017) Simultaneous dynamic demand estimation models for major seaports in India. Transp Lett 9:141–151.  https://doi.org/10.1080/19427867.2016.1203582 CrossRefGoogle Scholar
  40. 40.
    Chandrasekhar BP (1984) Development of highway freight transportation demand model for a region with application to Hyderabad area. Kakatiya University, WarangalGoogle Scholar
  41. 41.
    RITES Ltd. (2007) Preliminary engineering-cum-traffic study. New DelhiGoogle Scholar
  42. 42.
    Ministry of Statistics and Programme Implementation (2015) Overall balance of payments, open government data platform India. https://data.gov.in/catalog/overall-balance-payments. Accessed 14 Jul 2016
  43. 43.
    Ministry of Petroleum and Natural Gas. http://petroleum.nic.in/. Accessed 15 Jul 2016
  44. 44.
    Power and Energy Division of the Planning Commission of India. http://planningcommission.nic.in/sectors/index.php?sectors=energy. Accessed 14 Jul 2016
  45. 45.
    Casey HJ (1955) Applications to traffic engineering of the law of retail gravitation. Traffic Q IX:23–35Google Scholar
  46. 46.
    Bacharach M (1970) Biproportional matrices and input–output change, vol 16. CUP Archive, CambridgeMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringNational Institute of TechnologyWarangalIndia
  2. 2.Department of Civil EngineeringIndian Institute of Technology, MadrasChennaiIndia

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