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Freight transport models with logistics
in data-rich and not so rich environments
Gerard de Jong - Significance, ITS Leeds
20 March 2014
Motto
“Data! Data! Data!”, he cried impatiently, “I can’t
make bricks without clay.”
Sherlock Holmes
The Adventure of the Copper Beeches
Sir Arthur Conan Doyle
2
General Modelling Framework (see Ben-Akiva and de
Jong; in Ben-Akiva, Meersman and van de Voorde (2013))
3
Economic activity
• Growth factor• Gravity• Input-output• Spatial equilibrium
Logistics choices
• Inventory• Transport chains
Network assignment
Production-consumption flows
Vehicular flows
Examples of PC transport chains
4
road rail road
P C
road inland waterways road
P C
A logistics model
reads in base matrices of commodity flows from producers
to consumers : PC flows
delivers OD matrices to the network model (assignment)
determines shipment size and transport chain
Arguments to do this at the disaggregate level
Examples: SMILE (Netherlands), EUNET (UK), Maurer
(UK), ADA (Sweden, Norway, Denmark, Flanders), Liedtke
(Germany), Friedrich (Germany), Combes (France), Samimi
et al. (US).
5
Typology of data in freight transport (from Tavasszy
and de Jong, 2014, chapter 10)
International trade statistics
National accounts data
Transport statistics by mode
Shippers surveys
Project-specific interviews (incl. stated preference)
Consignment bills and RFIDs – BIG DATA?
Traffic count data – BIG DATA
Traffic safety inspection data
Network data
Cost functions
Terminal data
6
Big data in transport
From automatic traffic count equipment or GPS
Often rather big (many records) but not very deep (few variables)
Lots of info on LHS variables, not much on RHS (explanatory variables)
7
The Swedish Commodity Flow Survey
Carried out by Statistics Sweden for transport authorities
A sample of Swedish production and wholesale companies was asked
to record their shipments in a 1-3 week period
Outgoing shipments (domestic and international) and incoming
(international)
Records=shipments; CFS 2009: 3.5 mln outgoing shipments
Includes data on production and consumption location (municipality
level), industry, weight, value, commodity type and mode chain (e.g.
truck-train-truck)
CFS 2001 and 2004/2005 have been used in previous analyses
8
The French ECHO survey
Envois-Chargeurs-Opérateurs 2004 (ECHO); IFSTTAR plans a new
ECHO
Carried out by IFSTTAR (previously INRETS) and ISL
Starting point: a sample of almost 3,000 French shippers: last
shipments in up to 3 last months
Reconstituted for almost 10,000 shipments the full transport chain
(PC) by also interviewing 27,000 receivers, transport operators and
LSPs
Data includes attributes of the firms involved, locations of
production, consumption and transhipment (NUTS3 level), annual
flow, weight, volume, commodity type and modes used in the chain
9
Four situations for a logistics model
10
PC model Individual shipment data
Yes Yes Data-rich
Yes No Not so rich
No Yes Not so rich
No No Not so rich
Logistics model in a data-rich environment
Estimate transport chain and shipment size models on a shippers survey
Determine PC flows (SCGE, I/O model) and disaggregate to f2f flows
Implement the estimated functions for shipment size and transport chain for
each f2f flow
□ Apply by calculating and summing probabilities over f2f flows
□ Gives OD flows by mode and commodity for uni-modal assignment
11
Example - Multinomial logit model of discrete shipment size and transport
chain choice (Abate, Vierth and de Jong, 2014)
Model 1 (domestic, all
commodities, Windisch, 2009)
Model 2 (metal products)
Variable Relevant
Alternative
Coefficient
estimates
Relevant
Alternative
Coefficient
estimates
Cost All chains -0.0011*** All chains -0.0001***
Transport time (in
hours) times
value of goods (in
mln SEK)
Truck -1.98e-7***
Proxy to
Rail/Quay
Rail, Ferry,
Vessel
0.729***
Value Density All modes:
smallest 2
shipment sizes
0.122***
Value Density 1 Weight Cat 1-
5
-5.79***
Value Density 2 Weight Cat 6-
9
4.49***
Value Density 3 Weight Cat 1 0.961***
Time of Year
(Summer)
1.02***
Rail Constant -3.08***
Ferry Constant -4.51***
Vessel Constant -4.23***
Truck Fixed
Observations 2.225.150 33868
Final LL value -1.601.661 -77652.811
Rho2 (0) 0.737
Rho2 (C) 0.314 0.384
Logistics model in a not so data-rich
environment: PC model, but no shippers survey
Deterministic transport chain and shipment size model that minimises total
logistics cost
Determine PC flows (SCGE, I/O model) and disaggregate to f2f flows
Implement the minimisation function for shipment size and transport chain
for each f2f flow
□ Apply by calculating the 0/1 solution and summing over f2f flows
□ Gives OD flows by mode and commodity for uni-modal assignment
□ Calibrate to observed aggregate OD transport chain shares
13
Logistics model in a not so data-rich
environment: no PC model, no shippers survey
Do a limited shippers survey (sample) to get individual shipments at PC level
(could focus on international flows/flows through ports)
Estimate transport chain and shipment size model
Apply this function on the sample and expand to observed aggregate OD flows
For future years, grow to/from a country by country-specific growth factors.
□ Gives OD flows by mode and commodity for uni-modal assignment
14
Conclusions
A logistics model explains transport chain and shipment size choice
In a data-rich situation this can be estimated on data at the level of individual
shipments (shippers survey/commodity flow survey)
Without such a survey, there is the possibility of a deterministic model,
calibrated to aggregate OD data:
□ Weaker empirical foundation
□ Danger of flip-flop behaviour
If also the PC model is missing, there is no choice really but to collect (a
limited amount of) shipment data:
□ To get the PC pattern
□ To estimate shipment size and transport chain models
15
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