Freight transport has historically inherited the passage transport framework and model assumptions. But, are all of them valid? This paper discusses one of the least discussed assumptions: the mutual exclusiveness of alternatives in the freight con-text. To do so, a Multiple Discrete Extreme Value Model (MDCEV) has been used to describe the behavior of grain consolidators with data from a stated preference survey that allowed multiple alternatives to be chosen simultaneously. The choice is de-scribed by the Travel Time, Lead Time, Price paid in the port and Freight Price. The MDCEV gave insights regarding the satiation behavior of the grain consolidators. This way, the MDCEV can become a valuable tool for modeling tactical and strategic choic-es.

Authors

  • Rodrigo Javier Tapia Universidade Federal do Rio Grande do Sul, Rio Grande do Sul – Brasil
  • Ana Margarita Larranaga Universidade Federal do Rio Grande do Sul, Rio Grande do Sul – Brasil
  • Helena Beatriz Cybis Universidade Federal do Rio Grande do Sul, Rio Grande do Sul – Brasil
  • Gerard de Jong University of Leeds, West Yorkshire – Reino Unido

DOI:

https://doi.org/10.14295/transportes.v28i4.2398

Keywords:

MDCEV. Freight Transport. Choice modelling.

Abstract

Freight transport has historically inherited the passage transport framework and model assumptions. But, are all of them valid? This paper discusses one of the least discussed assumptions: the mutual exclusiveness of alternatives in the freight context. To do so, a Multiple Discrete Extreme Value Model (MDCEV) has been used to describe the behavior of grain consolidators with data from a stated preference survey that allowed multiple alternatives to be chosen simultaneously. The choice is described by the Travel Time, Lead Time, Price paid in the port and Freight Price. The MDCEV gave insights regarding the satiation behavior of the grain consolidators. This way, the MDCEV can become a valuable tool for modeling tactical and strategic choices.

Downloads

Download data is not yet available.

References

Abdelwahab, W. M., e Sargious, M. (1992) Modelling the Demand for Freight Transport: A New Approach. Journal of Transport Economics and Policy, 26(1), 49–70. doi: 10.2307/20052965

Ahn, J., Jeong, G., e Kim, Y. (2008) A forecast of household ownership and use of alternative fuel vehicles: A multiple discrete-continuous choice approach. Energy Economics, 30(5), 2091–2104. DOI: 10.1016/j.eneco.2007.10.003

Arunotayanun, K., e Polak, J. W. (2009) Accounting for Supply Chain Structures in Modelling Freight Mode Choice Behaviour. European Transport Conference, 44(0), 1–19.

Astroza, S., Bhat, P. C., Bhat, C. R., Pendyala, R. M., e Garikapati, V. M. (2018) Understanding activity engagement across weekdays and weekend days: A multivariate multiple discrete-continuous modeling approach. Journal of Choice Modelling, 28(May), 56–70. DOI: 10.1016/j.jocm.2018.05.004

Bhat, C. R. (2005) A multiple discrete-continuous extreme value model: Formulation and application to discretionary time-use decisions. Transportation Research Part B: Methodological, 39(8), 679–707. doi:10.1016/j.trb.2004.08.003

Bhat, C. R. (2008) The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions. Transportation Research Part B: Methodological, 42(3), 274–303. doi:10.1016/j.trb.2007.06.002

Bhat, C. R. (2018) A new flexible multiple discrete–continuous extreme value (MDCEV) choice model. Transportation Research Part B: Methodological, 110, 261–279. doi:10.1016/j.trb.2018.02.011

Bhat, C. R., e Sen, S. (2006) Household vehicle type holdings and usage: An application of the multiple discrete-continuous extreme value (MDCEV) model. Transportation Research Part B: Methodological, 40(1), 35–53. doi:10.1016/j.trb.2005.01.003

Bhat, C. R., Srinivasan, S., e Sen, S. (2006) A joint model for the perfect and imperfect substitute goods case: Application to activity time-use decisions. Transportation Research Part B: Methodological, 40(10), 827–850. doi:10.1016/j.trb.2005.08.004

Binh, N. T. (2017) A Multi-Stage Impact Assessment Method for Freight Transport Management Measures. Technische Universität Darmstadt.

Bonnet, C., e Bouamra-Mechemache, Z. (2016) Organic Label, Bargaining Power, and Profit-sharing in the French Fluid Milk Market. American Journal of Agricultural Economics, 98(1), 113–133. DOI: 0.1093/ajae/aav047

Brooks, M. R., e Trifts, V. (2008) Short sea shipping in North America: Understanding the requirements of Atlantic Canadian shippers. Maritime Policy and Management, 35(2), 145–158. DOI: 10.1080/03088830801956805

Calastri, C., Hess, S., Choudhury, C., Daly, A., e Gabrielli, L. (2017) Mode choice with latent availability and consideration: Theory and a case study. Transportation Research Part B: Methodological, 123, 374–385. doi:10.1016/j.trb.2017.06.016

Chow, J. Y. J., Yang, C. H., e Regan, A. C. (2010) State-of-the art of freight forecast modeling: Lessons learned and the road ahead. Transportation, 37(6), 1011–1030. doi:10.1007/s11116-010-9281-1

Copperman, R. B., e Bhat, C. R. (2007) An analysis of the determinants of children’s weekend physical activity participation. Transportation, 34(1), 67–87. doi:10.1007/s11116-006-0005-5

Daly, A., Hess, S., e Train, K. (2012) Assuring finite moments for willingness to pay in random coefficient models. Transportation, 39(1), 19–31. doi:10.1007/s11116-011-9331-3

Danielis, R., e Marcucci, E. (2007) Attribute cut-offs in freight service selection. Transportation Research Part E: Logistics and Transportation Review, 43(5), 506–515. doi:10.1016/j.tre.2005.10.002

de Bok, M., e Tavasszy, L. (2018) An empirical agent-based simulation system for urban goods transport (MASS-GT). Procedia Computer Science, 130, 126–133. doi:10.1016/j.procs.2018.04.021

de Jong, G., e Ben-Akiva, M. (2007) A micro-simulation model of shipment size and transport chain choice. Transportation Research Part B: Methodological, 41(9), 950–965. doi:10.1016/j.trb.2007.05.002

de Jong, G., Vierth, I., Tavasszy, L., e Ben-Akiva, M. (2013) Recent developments in national and international freight transport models within Europe. Transportation, 40(2), 347–371. doi:10.1007/s11116-012-9422-9

Ellison, R. B., Teye, C., e Hensher, D. A. (2017) Commodity-based heavy vehicle model for Greater Sydney. 5th International Choice Modelling Conference. Cape Town, South Africa.Disponível em: http://www.icmconference.org.uk/index.php/icmc/ICMC2017/paper/view/1226. Acessado em: 05/06/2019

Enam, A., Konduri, K. C., Eluru, N., e Ravulaparthy, S. (2018) Relationship between well-being and daily time use of elderly: evidence from the disabilities and use of time survey. Transportation, 45(6), 1783–1810. doi:10.1007/s11116-017-9821-z

Hendel, I. (1999) Estimating Multiple-Discrete Choice Models: An Application to Computerization Returns Author(s Estimating Multiple-Discrete Choice Models: An Application to Computerization Returns. Review of Economic Studies, 66(66), 423–446. doi:10.1111/1467-937X.00093

Hensher, D., e Figliozzi, M. A. (2007) Behavioural insights into the modelling of freight transportation and distribution systems. Transportation Research Part B: Methodological, 41(9), 921–923. doi:10.1016/j.trb.2007.04.002

Hess, S. e Palma, D. 2019; Apollo: a flexible, powerful and customisable freeware package for choice model estimation and application, Journal of Choice Modelling, Volume 32, September 2019, 100170, doi: 10.1016/j.jocm.2019.100170

Holguín-Veras, J. (2002) Revealed Preference Analysis of Commercial Vehicle Choice Process. Journal of Transportation Engineering, 128(August), 336–346,doi: 10.1061/(ASCE)0733-947X(2002)128:4(336).

Huh, S. Y., Lee, H., Shin, J., Lee, D., e Jang, J. (2018) Inter-fuel substitution path analysis of the korea cement industry. Renewable and Sustainable Energy Reviews, 82(June 2017), 4091–4099. doi:10.1016/j.rser.2017.10.065

Instituto Nacional de Tecnología Agropecuaria. (2009) Análisis de la cadena de soja en Argentina. Proyecto Específico 2742: Economía de las Cadenas Agroalimentarias y Agroindustriales, 119.Disponível em: https://inta.gob.ar/sites/default/files/script-tmp-cadena_soja.pdf. Acesso em: 04/02/2019

Jäggi, B., Weis, C., e Axhausen, K. W. (2013) Stated response and multiple discrete-continuous choice models: Analyses of residuals. Journal of Choice Modelling, 6, 44–59. doi:10.1016/j.jocm.2013.04.005

Jian, S., Rashidi, T. H., e Dixit, V. (2017) An analysis of carsharing vehicle choice and utilization patterns using multiple discrete-continuous extreme value (MDCEV) models. Transportation Research Part A: Policy and Practice, 103, 362–376. doi:10.1016/j.tra.2017.06.012

Johnson, D., e de Jong, G. (2011) Heterogeneous response to transport cost and time and model specification in freight mode and shipment size choice. International Choice Modelling Conference. disponível em: http://www.icmconference.org.uk/index.php/icmc/ICMC2011/paper/view/284. Acesso em 21/05/2019.

Khan, M., e Machemehl, R. (2017a) Commercial vehicles time of day choice behavior in urban areas. Transportation Research Part A: Policy and Practice, 102, 68–83. doi:10.1016/j.tra.2016.08.024

Khan, M., e Machemehl, R. (2017b) Analyzing tour chaining patterns of urban commercial vehicles. Transportation Research Part A: Policy and Practice, 102, 84–97. doi:10.1016/j.tra.2016.08.014

Larranaga, A. M., Arellana, J., e Senna, L. A. (2017) Encouraging intermodality: A stated preference analysis of freight mode choice in Rio Grande do Sul. Transportation Research Part A: Policy and Practice, 102, 202–211. doi:10.1016/j.tra.2016.10.028

Lu, H., Hess, S., Daly, A., e Rohr, C. (2017) Measuring the impact of alcohol multi-buy promotions on consumers’ purchase behaviour. Journal of Choice Modelling, 24(2014), 75–95. doi:10.1016/j.jocm.2016.05.001

McFadden, D. (1973) Conditional logit analysis of qualitative choice behavior. Frontiers in Econometrics. doi:10.1108/eb028592

Mcfaden, D., Winston, C., e Boersch-supan, A. (1986) Joint estimation of freight transportation decisions under non random sampling. Daugherty, A. (Ed.), Analytical Studies in Transport Economics (p. 137–157), Cambridge University Press.

Nogueira, Í. M., e Bertoncini, B. V. (2018) Proposta de método para modelar a geração de viagens intermunicipais de transporte de cargas a partir de dados secundários. 32 Congresso de Pesquisa e Ensino em Transporte da ANPET (p. 2467–2470). Gramado. Disponível em: http://146.164.5.73:30080/tempsite/anais/documentos/2018/Modelos%20e%20Tecnicas%20de%20Planejamento%20de%20Transportes/Modelagem%20Aplicada%20ao%20Transporte%20de%20Carga/5_168_RT.pdf. Acesso em: 07/03/2019

Nurul Habib, K. M., e Miller, E. J. (2008) Modelling daily activity program generation considering within-day and day-to-day dynamics in activity-travel behaviour. Transportation, 35(4), 467–484. doi:10.1007/s11116-008-9166-8

Paleti, R., Copperman, R. B., e Bhat, C. R. (2011) An empirical analysis of children’s after school out-of-home activity-location engagement patterns and time allocation. Transportation, 38(2), 273–303. doi:10.1007/s11116-010-9300-2

Pourabdollahi, Z., Karimi, B., e Mohammadian, A. (2013) Joint Model of Freight Mode and Shipment Size Choice. Transportation Research Record: Journal of the Transportation Research Board, 2378(312), 84–91. doi:10.3141/2378-09

Rashidi, T. H., e Roorda, M. J. (2018) A business establishment fleet ownership and composition model. Transportation, 45(3), 971–987. doi:10.1007/s11116-017-9758-2

Rich, J., Holmblad, P. M., e Hansen, C. O. (2009) A weighted logit freight mode-choice model. Transportation Research Part E: Logistics and Transportation Review, 45(6), 1006–1019. doi:10.1016/j.tre.2009.02.001

Rose, J. M., e Bliemer, Mi. C. J. (2009) Constructing efficient stated choice experimental designs. Transport Reviews, 29(5), 587–617. doi:10.1080/01441640902827623

Shin, J., Hong, J., Jeong, G., e Lee, J. (2012) Impact of electric vehicles on existing car usage: A mixed multiple discrete-continuous extreme value model approach. Transportation Research Part D: Transport and Environment, 17(2), 138–144. doi:10.1016/j.trd.2011.10.004

Sikder, S., e Pinjari, A. R. (2013) The benefits of allowing heteroscedastic stochastic distributions in multiple discrete-continuous choice models. Journal of Choice Modelling, 9(1), 39–56. doi:10.1016/j.jocm.2013.12.003

Spissu, E., Pinjari, A. R., Bhat, C. R., Pendyala, R. M., e Axhausen, K. W. (2009) An analysis of weekly out-of-home discretionary activity participation and time-use behavior. Transportation, 36(5), 483–510. doi:10.1007/s11116-009-9200-5

Tanner, R., e Bolduc, D. (2014) The Multiple Discrete-continuous Extreme Value Model (MDCEV) with Fixed Costs. Procedia - Social and Behavioral Sciences, 111, 390–399. doi:10.1016/j.sbspro.2014.01.072

Tapia, R. J., de Jong, G., Larranaga, A. M., e Cybis Bettella, H. B. (2019) Exploring multiple discreteness in freight transport . A Multiple Discrete Extreme Value Model application for grain consolidators in Argentina. TRB 2019 Annual Meeting (p. 1–16).

Tapia, R. J., dos Santos Senna, L. A., Larranaga, A. M., e Cybis, H. B. B. (2019) Joint mode and port choice for soy production in Buenos Aires province, Argentina. Transportation Research Part E: Logistics and Transportation Review, 121, 100–118. doi:10.1016/j.tre.2018.04.010

Tavasszy, L., e de Jong, G. (2014) Modelling Freight Transport. (L. Tavasszy & G. de Jong, Eds)Modelling Freight Transport. Elsevier Inc.( ISBN: 9780124104006)

Train, K. (2003) Discrete Choice Methods with Simulation. Cambridge University Press, 1–388. doi:10.1017/CBO9780511753930

Vellay, C., e de Jong, G. (2003) A simultaneous SP/RP Analysis of Mode Choice in Freight Transport in the Region Nord-Pas-de-Calais.

Von Haefen, R. H., e Phaneuf, D. J. (2005) Kuhn-Tucker Demand System Approaches To Non-Market Valuation. Applications of Simulation Methods in Enviromental and Resource Economics. Spri.doi: 10.1007/1-4020-3684-1_8

Von Haefen, R. H., Phaneuf, D. J., e Parsons, G. R. (2004) Estimation and Welfare Analysis With Large Demand Systems. Journal of Business and Economic Statistics, 22(2), 194–205. doi:10.1198/073500104000000082

Windisch, E., De Jong, G., Van Nes, R., e Hoogendoom, S. P. (2010) A Disagregate freight transport model of transport chain and shipment size choice. European Transport Conference, (Abdelwahab 1998).Disponível em: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.850.9821&rep=rep1&type=pdf. Acesso em 08/01/2019

Yu, H., Zeng, A. Z., e Zhao, L. (2009) Single or dual sourcing: decision-making in the presence of supply chain disruption risks. Omega, 37(4), 788–800. doi:10.1016/j.omega.2008.05.006

Published

2020-11-16

How to Cite

Tapia, R. J., Larranaga, A. M., Cybis, H. B., & de Jong, G. (2020). Freight transport has historically inherited the passage transport framework and model assumptions. But, are all of them valid? This paper discusses one of the least discussed assumptions: the mutual exclusiveness of alternatives in the freight con-text. To do so, a Multiple Discrete Extreme Value Model (MDCEV) has been used to describe the behavior of grain consolidators with data from a stated preference survey that allowed multiple alternatives to be chosen simultaneously. The choice is de-scribed by the Travel Time, Lead Time, Price paid in the port and Freight Price. The MDCEV gave insights regarding the satiation behavior of the grain consolidators. This way, the MDCEV can become a valuable tool for modeling tactical and strategic choic-es. TRANSPORTES, 28(4), 64–75. https://doi.org/10.14295/transportes.v28i4.2398

Issue

Section

Artigos Vencedores do Prêmio ANPET Produção Científica