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

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General Modelling Framework (see Ben-Akiva and de

Jong; in Ben-Akiva, Meersman and van de Voorde (2013))

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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

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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).

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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

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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)

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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

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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

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Four situations for a logistics model

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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

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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-

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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

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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

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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

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