Report

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 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 Structure of SAMGODS model: ADA ADA model based on de Jong and Ben-Akiva (2007) Aggregate flows PWC flows Disaggregation OD Flows A C Aggregation B Disaggregate firms and shipments Firms (agents) Shipments Logistic decisions Assignment 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 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) 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 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 where Ul is the utility derived from logistics and transport chain choice, Gl is logistics cost, and l is a random variable 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 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, Modeling approaches in the literature 1. An independent discrete mode choice model (which is the most common formulation) Ul = 1X + 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) 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) Determinants of shipment size/transport chain choice Variables (in X and G) Effect on SS Effect on mode/chain choice Transport Cost Negative Transport Time Negative Value Density Negative ? Access to Rail/Quay ? ? Firm Characteristics ? ? Network Characteristics ? ? 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 Descriptive Statistics Variable Rail Access Quay Access Mean/% 2% 0.4% Shipment Weight (KG) 26010.6 Shipment Value (SEK) 37121.9 Value Density (SEK/KG) Transport Costs (105 SEK) Transport Time (hours) 1231.4 1129.6 13.5 2,897,175 No. of Obervation Major commodities - outgoing shipments Swedish CFS 2004/05 Freq. Share (%) Live Animals Foodstuff and animal fodder Metal products 128136 4.42 Avg. value Avg. density Value Avg. weight (value/weight) (SEK) (KG) (SEK/KG) 29081.90 3542.29 10.24 304956 39235 10.53 1.35 20788.93 39147.35 1181.89 6472.73 3162.02 32.20 Leather and textile 178744 6.17 14364.23 490.89 2511.12 Timber Machineries 1481862 231748 51.15 8.00 8863.77 27381.46 34123.72 280.67 0.26 7920.00 Total 2364681 81.62 Total shipments in CFS 2897010 Commodity There are 28 commodity groups in the CFS based on the SAMGODS classification, and 6 commodities make up 80% of the shipment Transportation Costs and Commodity value – Metal Products Variable Average Values From CFS ( values per shipment) Weight (kg) 6556.49 Value (SEK) 31942.84 Tonne-Kilometer 7071.12 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 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-Truck 0.53 Shipment size categories Category 1 2 3 4 5 6 7 8 9 10 11 From (kg) 0 51 201 801 3001 7501 12501 20001 30001 35001 40001 To (kg) 50 200 800 3000 7500 12500 20000 30000 35000 40000 45000 Freq. 703,939 153,222 160,420 157,891 136,884 127,583 161,688 210,919 207,622 344,695 340,498 Percent 24.36 5.3 5.55 5.46 4.74 4.42 5.6 7.3 7.19 11.93 11.78 12 45001 100000 153,857 5.32 13 100001 200000 10,835 0.37 14 200001 400000 7,238 0.25 15 16 400001 800001 800000 - 6,417 5,641 0.22 0.2 2,889,349 100 Total Results Estimation results for a Nested Logit model for discrete mode and discrete shipment size choice (2004/5 CFS) Results Nest Structure of mode and chain Mode Chains Truck Truck Truck-Truck-Truck Truck-Vessel-Truck Truck-Ferry- Truck Truck-Vessel Water Rail Truck-Rail-Vessel-Truck Truck-Rail-Truck Air Truck-Air-Truck Results NL for discrete mode and discrete shipment size choice from 2004/5 CFS (Windisch et al. 2009) Variable Relevant alternatives Proxy to Rail/Quay Rail/Vessel 7.02*** Value density in SEK/kg All modes: all smallest shipment sizes 1.11*** Transport cost in SEK/shipment All Number of observations: 2.225.150 Pseudo rho-squared w.r.t. zero: 0.73 Pseudo rho-squared w.r.t. constants: 0.32 NL Coefficient -0.0012*** Results: Estimation results for mixed multinomial logit model including estimated shipment size at instrumental variable (Johnson and de Jong, 2009) Variable Relevant alternatives Coefficient t-ratio Road constant Rail constant Water constant Company is in biggest size class (sector-dependent) Commodity type is metal products Road Rail Water Rail 3.169 -1.107 -1.385 .279 126.6 -21.1 -22.6 8.1 Rail -.471 -9.3 Commodity type is chemical products Rail -.0338 -.6 Absolute difference between estimated All and average observed shipment size Vl -.240 -63.0 Transport cost in SEK/shipment Road, rail, water, air -.0000240 Transport time in hours (*10) Transport time in hours (*10) Road Rail -.00745 -.00317 Transport time in hours (*10) Air -.328 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 Distribution (standard deviation) t-ratio -35.2 -.0000142 -54.5 -32.2 -17.1 .0000918 .000132 .5 .5 -20.4 .167 19.2 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) Results: Shipment Size model preliminary results VARIABLES Log. Value Density Access to Rail at Origin International Shipment Total Shipments Summer Log. Distance Container mindre än 20 fot Pallastat (pallagt,palletiserat) gods Okänd Observations R-squared Dependent Variable Log-shipment size (kg) -1.925*** (0.0389) 2.117*** (0.485) 1.921*** (0.155) -0.000695*** (1.55e-05) 0.302*** (0.0485) 0.385*** (0.0224) -2.100 (2.816) -0.980** (0.407) -0.374 (1.812) 33,121 0.230 Results: MNL model for metal products CFS 04/05 Log. Cost Log. Time Constant Truck-RailTruck Truck-Ferry- TruckTruck Vessel-Truck 0.74*** 0.46*** 3.5*** (0.037) (0.036) (0.52) 0.26*** 1.71*** 6.31*** (0.049) (0.116) (1.46) -12.04*** -13.88*** -84.92*** (0.445) (0.53) (14.37) Observations 33183 Pseudo R-squared 0.4249 Results: Marginal Effects of cost – Truck -.6 -.4 -.2 0 Average Marginal Effects of logcost 0 2 4 6 8 10 logcost 12 14 16 18 Results: Marginal Effects of cost – Truck-Rail-Truck 0 .2 .4 .6 .8 Average Marginal Effects of logcost 0 2 4 6 8 10 logcost 12 14 16 18 Results: Marginal Effects of cost – Truck-Ferry-Truck -.2 0 .2 .4 .6 Average Marginal Effects of logcost 0 2 4 6 8 10 logcost 12 14 16 18 Results: Marginal Effects of cost – Truck-Vessel-Truck 2.8 3 3.2 3.4 3.6 Average Marginal Effects of logcost 0 2 4 6 8 10 logcost 12 14 16 18 Results: Conditional shipment quantity model using the Dubin-McFadden Method Log. Value Density Log. Total Shipments Access to Rail International Summer Cargo Type Firm Size Select_Truck Select_Rail Select_Ferry Select_Vessel Constant Observations Truck Rail Ferry Vessel -0.937*** -0.187*** -0.0379 0.0270** 0.139* -0.411*** Included Included -0.264* 1.685*** -0.108 0.0356 -1.266 0.224 -0.116 Included Included 0.0993 0.141 -7.914*** 0.217 Included Included -0.189 -2.940 -3.641* 6.904*** 0.536 Included Included -3.678*** -28.38*** 19.40** 16.62 8.117*** 2.114*** -2.288*** 12.40*** 7.398*** 2.910* 13.54*** 31,412 1,526 130 115 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 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 Thank you for your attention ! Contact: [email protected] https://sites.google.com/site/megersabate/ References 1. 2. 3. 4. 5. 6. 7. 8. . 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.