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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
20.12.2023
DOI
https://doi.org/10.7307/ptt.v35i6.353
ISSUE
2023 (Vol 35), Issue 6
LICENSE
Copyright (c) 2024 Berna AKSOY, Mustafa GURSOY
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

A Different Nested Logit Model Structure Consistent with Random Utility Theory for Various Freight Types – A Case Study for Istanbul

Authors:Berna AKSOY, Mustafa GURSOY

Abstract

Freight transport significantly contributes to urban traffic but is often overlooked by decision-makers compared to passenger transport. Conventional transportation modelling studies often use aggregate approaches for freight transport, undoubtedly due to the difficulty of data collection. However, the nature of freight transportation is much more complex. For this reason, examining the determinants of freight vehicle preferences with discrete approaches is crucial for the contributions that can be put forward, especially in local studies. To address this apparent gap in the study of local disaggregated approaches to freight transport, we present a discrete modelling-based methodology to investigate the factors that determine freight vehicle preferences for shippers and senders. The estimated nested logit model is constructed with the RU2 approach, the second part of random utility theory, thus avoiding the theoretical inconsistencies that arise when generic coefficients are used. As a result, the model structures provided satisfactory results compared to the literature. It was revealed that the factors affecting freight vehicle choice preferences were influenced by packaging preferences and differed according to freight groups. This local study is the first nested logit study for freight modelling in Istanbul and it is aimed to shed light on future national studies.

 

Keywords:urban freight transport, road freight transport

References

  1. [1] Gokasar I, Şahin O. Evaluation of the travel behaviors and attitudes of the passengers towards the BRT line in Istanbul. Journal of Transportation Systems. 2020;(1):16-27. DOI: 10.5281/zenodo.3696656.
  2. [2] Chankaew N, et al. Freight traffic analytics from national truck GPS data in Thailand. Transportation Research Procedia. 2018;34:123-130. DOI: 10.1016/j.trpro.2018.11.023.
  3. [3] Kiba-Janiak M. Urban freight transport in city strategic planning. Research in Transportation Business & Management. 2017;24:4-16. DOI: 10.1016/j.rtbm.2017.05.003.
  4. [4] Vullapu SS, Jain J, Tarafdar AK. Streamlining freight transport through planning interventions in Vijayawada city. In: Chatterjee U, Bandyopadhyay N, Setiawati MD, Sarkar S. (eds) Urban Commons, Future Smart Cities and Sustainability. Springer Geography. Springer, Cham; 2023. DOI: 10.1007/978-3-031-24767-5_37.
  5. [5] De Jong GC, de Bok M, Thoen S. Seven fat years or seven lean years for freight transport modeling? Developments since 2013. Journal of Transport Economics and Policy. 2021;55(2):124-140.
  6. [6] De Jong GC, et al. Recent developments in national and international freight transport models within Europe. Transportation. 2013;40:347-371. DOI: 10.1007/s11116-012-9422-9.
  7. [7] Abate M, et al. A disaggregate stochastic freight transport model for Sweden. Transportation. 2019;46:671-696. DOI: 10.1007/s11116-018-9856-9.
  8. [8] Jensen AF, et al. A disaggregate freight transport chain choice model for Europe. Transportation Research Part E. 2019;121:43-62. DOI: 10.1016/j.tre.2018.10.004.
  9. [9] Manski CF. The structure of random utility models. Theor Decis 8. 1977;229-254. DOI: 10.1007/BF00133443.
  10. [10] Luce RD, Suppes P. Preference, utility, and subjective probability. In: Luce RD, Bush RR, Galanter E. (eds) Handbook of Mathematical Psychology. Vol. III. New York: Wiley; 1965. p. 252-410.
  11. [11] Tezcan HO, Öğüt KS, Çidimal B. A multinomial logit car use model for a megacity of the developing world: Istanbul. Transportation Planning and Technology. 2011;34(8):759-776. DOI: 10.1080/03081060.2011.613585.
  12. [12] Ben-Akiva M, Lerman SR. Discrete choice analysis: Theory and application to travel demand. Cambridge, MA: MIT press; 1985.
  13. [13] Manski CF. The analysis of qualitative choice. PhD thesis. MIT; June 1973.
  14. [14] Domenchic T, McFadden DL. Urban travel demand - A behavioral analysis. Chapter 4. North-Holland Publishing Co.; 1975.
  15. [15] Koppelman FS, Bhat C. A self-instructing course in mode choice modeling: Multinomial and nested logit models. U.S. Department of Transportation; 2006.
  16. [16] Tavasszy L, de Jong GC. Modeling freight transport. Elsevier; 2014. DOI: 10.1016/C2012-0-06032-2.
  17. [17] Bhat C. Flexible model structures for discrete choice analysis. In: Hensher DA, Button KJ. (eds) Handbook of Transport Modelling. Oxford, UK: Elsevier Science Ltd; 2000. p. 71-89.
  18. [18] Louviere JJ, Hensher DA, Swait JD. Stated choice methods: Analysis and applications. Cambridge, UK: Cambridge University Press; 2000.
  19. [19] Kenneth T. Discrete choice methods with simulation. Cambridge: Cambridge University Press; 2003.
  20. [20] Hensher DA, Rose JM, Greene WH. Applied choice analysis: A primer. Cambridge, UK: Cambridge University Press; 2005.
  21. [21] Ben-Akiva M. Structure of passenger travel demand models. PhD thesis. Department of Civil Engineering, MIT; 1973.
  22. [22] Daly A, Zachary S. Improved multiple choice models. In: Hensher D, Dalvi Q. (eds) Identifying and measuring the determinants of model choice. Teakfield, London; 1979.
  23. [23] Williams HCWL. On the formation of travel demand models and economic evaluation measures of user benefit. Envir. and Planning. 1977;9:285-344.
  24. [24] Ben-Akiva M, Lerman S. Disaggregate travel and mobility choice models and measures of accessibility. In: Hensher D, Stopher P. (eds) Behavioral travel modelling. Croom Helm, London; 1979.
  25. [25] McFadden D. Modelling the choice of residential location. In: Karlquist A, et al. (eds) Spatial interaction theory and residential location. North Holland, Amsterdam; 1978. p. 75-96.
  26. [26] Bradley MA, Daly AJ. Estimation of logit choice models using mixed stated preference and revealed preference information. In: Stopher PR, Lee-Gosselin M. (eds) Understanding travel behavior in an era of change. Amsterdam: Elsevier; 1997.
  27. [27] Jiang F, Johnson P, Calzada C. Freight demand characteristics and mode choice: An analysis of the results of modeling with disaggregate revealed preference data. Journal of Transportation and Statistics. 1999;2(2):149-158.
  28. [28] De Jong GC, Vellay C, Houée M. A joint SP/RP model of freight shipments from the region Nord-Pas de Calais. Proceedings of the AET European Transport Conference, 10-12 Sep. 2001, Cambridge, UK. 2001. 15 p.
  29. [29] Arunotayanun K. Modelling freight supplier behavior and response. Ph.D. thesis. Centre for Transport Studies, Imperial College; 2009.
  30. [30] Martino A, et al. TRIMODE – Integrated transport model for Europe. Proceedings of 7th Transport Research Arena TRA 2018, 16-19 Apr. 2018, Vienna, Austria. 2018.
  31. [31] Nugroho M, de Jong GC, Whiteing AE. Port and inland mode choice from the exporters and forwarders perspectives: Case study – Java, Indonesia. Research in Transportation Business and Management. 2016. DOI: 10.1016/j.rtbm.2016.03.010.
  32. [32] Grene WH. NLOGIT6 reference guide; 2016.
  33. [33] Hensher DA, Greene WH. Specification and estimation of nested logit model: Alternative normalisations. Transportation Research Part B. 2002;36(1):1-17. DOI: 10.1016/S0191-2615(00)00035-7.
  34. [34] Bierlaire M. A theoretical analysis of the cross-nested logit model. Annals of Operations Research. 2006;144:287-300. DOI: 10.1007/s10479-006-0015-x.
  35. [35] Silberhorn N, Boztug Y, Hildebrandt L. Estimation with the nested logit model: Specifications and software particularities. OR Spectrum. 2008;30(4):635-653. DOI: 10.1007/s00291-007-0109-0.
  36. [36] Ortúzar JD, Willumsen LG. Modelling transport. 4th Ed. John Wiley & Sons; 2011. DOI: 10.1002/9781119993308.
  37. [37] Koppelman FS, Wen CH. Alternative nested logit models: Structure, properties and estimation. Transportation Research Part B: Methodological. 1998;32(5):289-298. DOI: 10.1016/S0191-2615(98)00003-4.
  38. [38] Tezcan HO. Discrete choice modelling in transport. Lecture notes (not published); 2019.
  39. [39] Istanbul Metropolitan Municipality & Bimtaş. Istanbul logistic master plan project; 2018.
  40. [40] De Jong GC, et al. Analysis of route and mode transport choice in Eastern South Asia following integration agreements. In: Dappe MH, Kunaka C. (eds) Connecting to thrive: Challenges and opportunities of transport integration in Eastern South Asia. World Bank, Washington, DC; 2021. DOI: 10.1596/978-1-4648-1635-2_ch2.
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