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 1.Product specific demand PC-matrices (producers-consumers) 2.Logistics model (LOGMOD) 3.Network model

3. Structure of SAMGODS model: ADA ADA model based on de Jong and Ben-Akiva (2007)

4. Introduction: Deterministic cost minimization

5. Limitation of the current logistic model The current logistic model lacks two mains elements: 1.other determinants of shipment size and transport chain choice ( decisions are solely based on cost) 2.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: U l = -G l –  l where U l is the utility derived from logistics and transport chain choice, G l is 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 U l =  1 X +  G +  1 (1) SS 2 =  2 X +  2 (2) Where U l 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) U l =  1 X +  1 (1) 2.A joint model with discrete mode and discrete shipment size choice (e.g. Chiang et al. 1981; de Jong, 2007; Windisch et al. 2009) U l =  1 X +  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) U l =  1 X +  G +  1 (1) SS 2 =  2 X +  2 (2)

11. Determinants of shipment size/transport chain choice Variables (in X and G)Effect on SSEffect on mode/chain choice Transport CostNegative Transport TimeNegative Value DensityNegative? Access to Rail/Quay?? Firm Characteristics?? Network Characteristics??

12. Data Main data source : - National Commodity Flow Survey 2004/05 (CFS) based on the US CFS - Network data – mainly transport time and cost variables from the logistics module of SAMGODS

13. Descriptive Statistics VariableMean/% Rail Access2% Quay Access0.4% Shipment Weight (KG)26010.6 Shipment Value (SEK)37121.9 Value Density (SEK/KG)1231.4 Transport Costs ( 10 5 SEK)1129.6 Transport Time (hours)13.5 No. of Obervation 2,897,175

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 CommodityFreq. Share (%) Avg. ValueAvg. weight Avg. value density (value/weight) (SEK)(KG)(SEK/KG) Live Animals1281364.4229081.903542.2910.24 Foodstuff and animal fodder30495610.5320788.931181.893162.02 Metal products392351.3539147.356472.7332.20 Leather and textile1787446.1714364.23490.892511.12 Timber148186251.158863.7734123.720.26 Machineries2317488.0027381.46280.677920.00 Total236468181.62 Total shipments in CFS2897010

15. Transportation Costs and Commodity value – Metal Products VariableAverage Values From CFS ( values per shipment) Weight (kg)6556.49 Value (SEK)31942.84 Tonne-Kilometer7071.12 Value/Tonne (SEK/KG)24.38 From Network Data based on all available choices Distance/shipment (KM)591.41 Transport Cost (SEK)3.92e+07 Transport Tim (hours)10.24

16. Transport Chain Type Definitions Chains % Share Truck 96 Truck-Truck-Truck 0.01 Truck-Vessel-Truck 1.66 Truck-Ferry- Truck 0.50 Truck-Rail-Vessel-Truck 0.20 Truck-Rail-Truck 0.22 Truck-Air-Truck0.53

17. Shipment size categories CategoryFrom (kg)To (kg)Freq.Percent 1050703,93924.36 251200153,2225.3 3201800160,4205.55 48013000157,8915.46 530017500136,8844.74 6750112500127,5834.42 71250120000161,6885.6 82000130000210,9197.3 93000135000207,6227.19 103500140000344,69511.93 114000145000340,49811.78 1245001100000153,8575.32 1310000120000010,8350.37 142000014000007,2380.25 154000018000006,4170.22 16800001-5,6410.2 Total2,889,349100

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

19. Results Nest Structure of mode and chain ModeChains Truck Truck-Truck-Truck Water Truck-Vessel-Truck Truck-Ferry- Truck Truck-Vessel Rail Truck-Rail-Vessel-Truck Truck-Rail-Truck AirTruck-Air-Truck

20. Results NL for discrete mode and discrete shipment size choice from 2004/5 CFS (Windisch et al. 2009) VariableRelevant alternatives NL Coefficient Proxy to Rail/QuayRail/Vessel7.02*** Value density in SEK/kg All modes: all smallest shipment sizes 1.11*** Transport cost in SEK/shipmentAll-0.0012*** Number of observations: 2.225.150 Pseudo rho-squared w.r.t. zero: 0.73 Pseudo rho-squared w.r.t. constants: 0.32

21. Results: Estimation results for mixed multinomial logit model including estimated shipment size at instrumental variable (Johnson and de Jong, 2009) VariableRelevant alternatives Coefficientt-ratioDistribution (standard deviation) t-ratio Road constantRoad3.169126.6 Rail constantRail-1.107-21.1 Water constantWater-1.385-22.6 Company is in biggest size class (sector-dependent) Rail.2798.1 Commodity type is metal productsRail-.471-9.3 Commodity type is chemical productsRail-.0338-.6 Absolute difference between estimated and average observed shipment size V l All-.240-63.0 Transport cost in SEK/shipmentRoad, rail, water, air -.0000240-35.2-.0000142 -54.5 Transport time in hours (*10)Road-.00745-32.2.0000918.5 Transport time in hours (*10)Rail-.00317 -17.1.000132.5 Transport time in hours (*10)Air-.328-20.4.16719.2 Number of observations: 744860 Final log likelihood value: -124835.5142 Pseudo rho-squared w.r.t. zero:.8791 Pseudo rho-squared w.r.t. constants:.0529

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 ) SS 2 =  2 X +  2 (1) U l =  1 X +  G +  1 (2)

23. Results: Shipment Size model preliminary results Dependent Variable VARIABLESLog-shipment size (kg) Log. Value Density-1.925*** (0.0389) Access to Rail at Origin2.117*** (0.485) International Shipment1.921*** (0.155) Total Shipments-0.000695*** (1.55e-05) Summer0.302*** (0.0485) Log. Distance0.385*** (0.0224) Container mindre än 20 fot-2.100 (2.816) Pallastat (pallagt,palletiserat) gods-0.980** (0.407) Okänd-0.374 (1.812) Observations33,121 R-squared0.230

24. Results: MNL model for metal products CFS 04/05 Truck-Rail- Truck Truck-Ferry- Truck Truck- Vessel-Truck Log. Cost0.74***0.46***3.5*** (0.037)(0.036)(0.52) Log. Time0.26***1.71***6.31*** (0.049)(0.116)(1.46) Constant-12.04***-13.88***-84.92*** (0.445)(0.53)(14.37) Observations33183 Pseudo R-squared0.4249

25. Results: Marginal Effects of cost – Truck

26. Results: Marginal Effects of cost – Truck-Rail-Truck

27. Results: Marginal Effects of cost – Truck-Ferry-Truck

28. Results: Marginal Effects of cost – Truck-Vessel-Truck

29. Results: Conditional shipment quantity model using the Dubin-McFadden Method TruckRailFerryVessel Log. Value Density-0.937***-0.0379-0.108-1.266 Log. Total Shipments-0.187***0.0270**0.03560.224 Access to Rail0.139* International0.536-0.411***-0.1160.217 SummerIncluded Cargo TypeIncluded Firm Size-3.678***-0.264*0.0993-0.189 Select_Truck1.685***0.141-2.940 Select_Rail-28.38***-7.914***-3.641* Select_Ferry19.40**2.114***6.904*** Select_Vessel16.62-2.288***7.398*** Constant8.117***12.40***2.910*13.54*** Observations31,4121,526130115

30. Results: Elasticity Comparison ( Johnson and de Jong, 2009) Independent mode choice Discrete shipment size and mode Continuous shipment size and discrete mode Road cost-0.002-0.030-0.003 Rail cost-0.438-0.126-0.393 Water cost-0.920-0.073-0.639 Air cost-0.311-0.001-0.198 Road time-0.040--0.025 Rail time-0.447--0.302 Air time-1.391-0.871-1.454

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.semegersa.abate@vti.se https://sites.google.com/site/megersabate/

33. References 1.Abate, M. and de Jong, G. (2013) The optimal shipment size and truck size choice- the allocation of trucks across hauls" manuscript 2.Abdelwahab, W. M. and M. A. Sargious (1992) Modelling the Demand for Freight Transport, Journal of Transport Economics and Policy 26(1), 49-70. 3.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. 4.Dubin, J.A. & McFadden, D.L., 1984. An Econometric Analysis of Residential Electric Appliance Holdings and Consumption. Econometrica, 52 (2), pp.345--362. 5.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. 6.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. 7.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. 8.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..