a discrete-continuous model of freight mode and shipment
TRANSCRIPT
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 OD Flows Assignment
Disaggregation A C Aggregation
B
Disaggregate firms Firms Shipments
and shipments (agents)
Logistic
decisions
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 𝐺 .
• 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 Mean/%
Rail Access 2%
Quay Access 0.4%
Shipment Weight (KG) 26010.6
Shipment Value (SEK) 37121.9
Value Density (SEK/KG) 1231.4
Transport Costs (105 SEK) 1129.6
Transport Time (hours) 13.5
No. of Obervation 2,897,175
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
Commodity Freq.
Share
(%)
Avg.
Value Avg. weight
Avg. value
density
(value/weight)
(SEK) (KG) (SEK/KG)
Live Animals 128136 4.42 29081.90 3542.29 10.24
Foodstuff and animal
fodder 304956 10.53 20788.93 1181.89 3162.02
Metal products 39235 1.35 39147.35 6472.73 32.20
Leather and textile 178744 6.17 14364.23 490.89 2511.12
Timber 1481862 51.15 8863.77 34123.72 0.26
Machineries 231748 8.00 27381.46 280.67 7920.00
Total 2364681 81.62
Total shipments in CFS 2897010
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 From (kg) To (kg) Freq. Percent
1 0 50 703,939 24.36
2 51 200 153,222 5.3
3 201 800 160,420 5.55
4 801 3000 157,891 5.46
5 3001 7500 136,884 4.74
6 7501 12500 127,583 4.42
7 12501 20000 161,688 5.6
8 20001 30000 210,919 7.3
9 30001 35000 207,622 7.19
10 35001 40000 344,695 11.93
11 40001 45000 340,498 11.78
12 45001 100000 153,857 5.32
13 100001 200000 10,835 0.37
14 200001 400000 7,238 0.25
15 400001 800000 6,417 0.22
16 800001 - 5,641 0.2
Total 2,889,349 100
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
Water Truck-Vessel-Truck
Truck-Ferry- Truck
Truck-Vessel
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 NL
Coefficient
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 -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
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 Distribution
(standard
deviation)
t-ratio
Road constant Road 3.169 126.6
Rail constant Rail -1.107 -21.1
Water constant Water -1.385 -22.6
Company is in biggest size class
(sector-dependent)
Rail .279 8.1
Commodity type is metal products Rail -.471 -9.3
Commodity type is chemical products Rail -.0338 -.6
Absolute difference between estimated
and average observed shipment size Vl
All -.240 -63.0
Transport cost in SEK/shipment Road, 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 .167 19.2
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
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
Dependent Variable
VARIABLES Log-shipment size (kg)
Log. Value Density -1.925***
(0.0389)
Access to Rail at Origin 2.117***
(0.485)
International Shipment 1.921***
(0.155)
Total Shipments -0.000695***
(1.55e-05)
Summer 0.302***
(0.0485)
Log. Distance 0.385***
(0.0224)
Container mindre än 20 fot -2.100
(2.816)
Pallastat (pallagt,palletiserat) gods -0.980**
(0.407)
Okänd -0.374
(1.812)
Observations 33,121
R-squared 0.230
Results: MNL model for metal products CFS 04/05
Truck-Rail-
Truck
Truck-Ferry-
Truck
Truck-
Vessel-Truck
Log. Cost 0.74*** 0.46*** 3.5***
(0.037) (0.036) (0.52)
Log. Time 0.26*** 1.71*** 6.31***
(0.049) (0.116) (1.46)
Constant -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
Effects
on P
r(M
od
echa
in_S
==
1)
0 2 4 6 8 10 12 14 16 18logcost
Average Marginal Effects of logcost
Results: Marginal Effects of cost – Truck-Rail-Truck
0.2
.4.6
.8
Effects
on P
r(M
od
echa
in_S
==
121
)
0 2 4 6 8 10 12 14 16 18logcost
Average Marginal Effects of logcost
Results: Marginal Effects of cost – Truck-Ferry-Truck
-.2
0.2
.4.6
Effects
on P
r(M
od
echa
in_S
==
131
)
0 2 4 6 8 10 12 14 16 18logcost
Average Marginal Effects of logcost
Results: Marginal Effects of cost – Truck-Vessel-Truck
2.8
33.2
3.4
3.6
Effects
on P
r(M
od
echa
in_S
==
141
)
0 2 4 6 8 10 12 14 16 18logcost
Average Marginal Effects of logcost
Results: Conditional shipment quantity model using the Dubin-McFadden
Method
Truck Rail Ferry Vessel
Log. Value Density -0.937*** -0.0379 -0.108 -1.266
Log. Total Shipments -0.187*** 0.0270** 0.0356 0.224
Access to Rail 0.139*
International 0.536 -0.411*** -0.116 0.217
Summer Included Included Included Included
Cargo Type Included Included Included Included
Firm Size -3.678*** -0.264* 0.0993 -0.189
Select_Truck 1.685*** 0.141 -2.940
Select_Rail -28.38*** -7.914*** -3.641*
Select_Ferry 19.40** 2.114*** 6.904***
Select_Vessel 16.62 -2.288*** 7.398***
Constant 8.117*** 12.40*** 2.910* 13.54***
Observations 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
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trucks across hauls" manuscript
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of Transport Economics and Policy 26(1), 49-70.
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forecasting freight demand, Final report. Center for Transportation Studies Report 81-1, MIT,
Cambridge, Massachusetts.
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