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A discrete-continuous model of freight mode and shipment size choice

A discrete-continuous model of freight mode and shipment size choice. Megersa Abate (presenter), The Swedish National Road and Transport Research Institute (VTI); Inge Vierth, VTI ; Gerard de Jong, Significance, Uni. o f Leeds, CTS, Stockholm .

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A discrete-continuous model of freight mode and shipment size choice

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  1. A discrete-continuous model of freight mode and shipment size choice Megersa Abate (presenter), The Swedish National Road and Transport Research Institute (VTI); Inge Vierth, VTI ; Gerard de Jong, Significance, Uni. of Leeds, CTS, Stockholm

  2. Introduction – The Swedish National Freight Model • The main feature of the Swedish freight transport model (SAMGODS) is incorporation of a logistic model component in the traditional freight demand modeling framework • The SAMGODS model consists of • Product specific demand PC-matrices (producers-consumers) • Logistics model (LOGMOD) • Network model

  3. StructureofSAMGODS model: ADA • ADA modelbased on de Jong and Ben-Akiva (2007)

  4. Introduction: Deterministic cost minimization • The current logistic model is based on a deterministic cost minimization model where firms are assumed to minimize annual total logistic cost [G(.)] • argmin • The cost trade-off involves order costs; transport, consolidation and distribution costs; cost of deterioration and damage during transit; capital holding cost; inventory cost; stock-out costs

  5. Limitation of the current logistic model • The current logistic model lacks two mains elements: • other determinants of shipment size and transport chain choice ( decisions are solely based on cost) • stochastic element ( it is deterministic)

  6. Objective of the current project • This project is a first step towards estimating a full random/stochastic utility logistic model • We formulate econometric models to analyze the determinants of firms’ transport chain and shipment size choices • Parameter estimates from this model will later be used to set-up a stochastic logistic model • Estimation of elasticity for policy analysis

  7. Stochastic logistic model • A full random utility logistic model was planned but has not yet been estimated on disaggregate data ( de Jong and Ben-Akiva, 2007) • The model is specified as: • Ul = -Gl – l • whereUlis the utility derived from logistics and transport chain choice, Glis logistics cost, and l is a random variable

  8. Modeling framework • The main econometric work involves modeling the interdependence between shipment size and transport chain choices • This interdependence implies the use of a joint ( e.g. discrete-continuous) econometric model to account for the simultaneity problem

  9. Econometric model • Discrete-Continues econometric set-up • Ul= 1X + G + 1(1) • SS2 = 2X +2 (2) • Where Ul is a utility form a mode choice and SS is shipment size, X and G are vectors of explanatory variables that determine SS the choice of transport chain,

  10. Modeling approaches in the literature • 1. An independent discrete mode choice model (which is the most common formulation) • Ul= 1X + 1 (1) • A joint model with discrete mode and discrete shipment size choice (e.g. Chiang et al. 1981; de Jong, 2007; Windisch et al. 2009) • Ul= 1X + G + 1 (1’) • 3. A joint model with discrete mode and continuous shipment size choice ( Abate and de Jong, 2013; Johnson and de Jong, 2010; Dubin and McFadden 1984; Abdelwahab and Sargious,1992;Holguín-Veras ,2002) • Ul= 1X + G + 1(1) • SS2 = 2X + 2 (2)

  11. Determinants of shipment size/transport chain choice

  12. Data • Main data source : • - National CommodityFlowSurvey 2004/05 (CFS) based on the US CFS • - Network data – mainly transport time and costvariables from the logistics moduleof SAMGODS

  13. Descriptive Statistics

  14. Major commodities - outgoing shipments Swedish CFS 2004/05 • There are 28 commodity groups in the CFS based on the SAMGODS classification, and 6 commodities make up 80% of the shipment

  15. Transportation Costs and Commodity value – Metal Products

  16. Transport Chain Type Definitions

  17. Shipment size categories

  18. Results • Estimation results for a Nested Logitmodel for discrete mode and discrete shipment size choice (2004/5 CFS)

  19. Results • Nest Structure of mode and chain

  20. Results • NL for discrete mode and discrete shipment size choice from 2004/5 CFS (Windisch et al. 2009)

  21. Results: Estimation results for mixed multinomial logit model including estimated shipment size at instrumental variable(Johnson and de Jong, 2009)

  22. A joint model with discrete mode and continuous shipment size choice: Metal Products • A joint model with discrete mode and continuous shipment size choice (Dubin and McFadden 1984 ) • SS2 = 2X + 2(1) • Ul= 1X + G + 1(2)

  23. Results: Shipment Size model preliminary results

  24. Results: MNL model for metal products CFS 04/05

  25. Results: Marginal Effectsofcost – Truck

  26. Results: Marginal Effectsofcost – Truck-Rail-Truck

  27. Results: Marginal Effectsofcost – Truck-Ferry-Truck

  28. Results: Marginal Effectsofcost – Truck-Vessel-Truck

  29. Results: Conditional shipment quantity model using the Dubin-McFadden Method

  30. Results: Elasticity Comparison ( Johnson and de Jong, 2009)

  31. Conclusions • Transport Cost , Transport Time and Firm characteristics such as access to rail and quay at origin are important determinants of transport chain and shipment size choices. • Low elasticity for road (truck) transport cost • It is important to handle the simultaneous nature of the decisions on mode/transport chain and shipment size choices • Due to large data, estimation can be difficult to utilize the most theoretically sound model

  32. Thank you for your attention ! • Contact: megersa.abate@vti.se • https://sites.google.com/site/megersabate/

  33. References • Abate, M. and de Jong, G. (2013) The optimal shipment size and truck size choice- the allocation of trucks across hauls"manuscript • Abdelwahab, W. M. and M. A. Sargious (1992) Modelling the Demand for Freight Transport, Journal of Transport Economics and Policy 26(1), 49-70. • Chiang, Y., P.O. Roberts and M.E. Ben-Akiva (1981) Development of a policy sensitive model for forecasting freight demand, Final report. Center for Transportation Studies Report 81-1, MIT, Cambridge, Massachusetts. • Dubin, J.A. & McFadden, D.L., 1984. An Econometric Analysis of Residential Electric Appliance Holdings and Consumption. Econometrica, 52 (2), pp.345--362. • Holguín-Veras, J. (2002) Revealed Preference Analysis of the Commercial Vehicle Choice Process, Journal of Transportation Engineering, American Society of Civil Engineers 128(4), 336-346. • Jong, G.C. de and M.E. Ben-Akiva (2007) A micro-simulation model of shipment size and transport chain choice, Special issue on freight transport of Transportation Research B, 41, 950-965. • McFadden, D.L., C. Winston, and A. Boersch-Supan (1985) Joint estimation of freight transportation decisions under non-random sampling, in E.F. Daughety (Ed.) Analytical Studies in Transport Economics, Cambridge University Press, Cambridge. • Windisch, E. (2009) A disaggregate freight transport model of transport chain and shipment size choice on the Swedish Commodity Flow Survey 2004/05, MSc Thesis, Delft University of Technology. • .

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