1. Innovative data collection methods for road freight
transport statistics
EUROSTAT, Oct 11, 2012

2. Current situation
Quality concerns regarding road transport data
 Limited relevance of data for Austria’s traffic & transport planners
 due to sample errors on county or smaller traffic cell levels
 Insufficient precision of reported tonne-km
 as a result of automated imputation of the distance travelled between origin and destination of a
journey using distance matrices, ignoring potential detours in between.
 Possible over-estimation of empty trips
 assuming that distances between places of unloading and subsequent places of loading are empty
trips
 Assumed under reporting of both empty & loaded trips
 respondents assumed to minimize efforts, and report a vehicle to be out-of-order during the sample
week.
 Limited accuracy of cargo types
 Respondents have limited information regarding cargo moved („mixed cargo“)
 Assignment of goods to NST/R classification by respondents leads to incoherent results
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3. Consortium
Gebrüder Weiss GmbH
Petschl Transporte Österreich GmbH & Co KG
Process knowledge and experience of transport companies
Paradigma Unternehmensberatung GmbH
Austrian Institute of Technology – Department Mobility
Technology, data management und electronic data exchange
Wirtschaftsuniversität Wien – Inst. f. Transportwirtschaft und Logistik
Methods and legal environment of road freight statistics in Europe
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4. Goals and Results of the Project
Project Goals Project deliverables
 Further reduction of the  Prototypic and fully functional
respondents efforts through implementation of the connection
automation between data (from companies) to
 Return to a larger sample to meet the XML-Interface
national requirements  Test the applicability of automatic
 Increase of data quality and data collection technologies and
actuality algorithms to obtain precise
measurements.
 Reduction of required ressources for
preparation and processing of the  Legal, economic and methodical
data evaluation of the results with
respect to the road freight transport
statistic
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5. Research Objectives
 Prove the feasibility of using data available from transport companies
 building a working prototype, using IT data from transportation companies as a source
 develop a sufficiently generic standard interface (fleet information, consignment & location data)
 Assess the organizational impact on the respondents
 obtain empirical information on the benefits as well as potential issues
 Use information about goods from transport booking data, to infer
cargo types (NST/R)
 Train a Bayes algorithm, part of the KNIME data mining software to classify goods using free text
 Use GPS location to measure distances travelled and to infer
load/unload events.
 Obtain route information from GPS readings – infer events and compare with order information
 Obtain experience with the technical and economic challenges to
implement the standard interface
 industry software as well as individually developed software

6. Road Transport Statistics in Austria
 EU Regulation 70/2012 provides a general legal and methodological
framework for the different national surveys (territoriality principle)
 Stratified quarterly sample comprises 6,500 vehicles per quarter out
of a total of about 72,000 registered.
 Meets quality criteria described in the regulation
 Original sample size of 26000 vehicle weeks per year significantly reduced (since 2006)
 Local units, operating vehicles and drawn for the sample use paper
based questionnaires or an electronic questionnaire (11% in 2009)
 Efforts to complete the questionnaire have been reduced.
 27.3 minutes on average to complete a questionnaire
 150 work weeks per annum for the Austrian economy …
 Main task for completing the questionnaires consists of collecting and
preparing the information within their companies.
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7. Target Solution Architecture
 Data interfaces are in the public  Completion of missing data and
domain and provided to the corrections are performed by the
Software- and System developers respondent
 Different information entities are  Based on the transfer-format the
consolidated according to the data specific required structure for the
model specifications and business respective country questionaire is
logic generated

8. Information elements collected
4 XML-based interface specifications are provided
FleetMasterData FleetStatusData ConsignmentData PositionData
Data on lorries Information on order related GPS readings,
and trailers specific lorries at data containing country/ZIP
(capacity, axles, o certain times information on codes, activity
dometer, age, lice (driven distance, goods, packaging, (loading, border
nse, etc.) fuel usage, etc.) origin and crossing)
destination …
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9. Data Collection Service
Protoytped process deployed to move data from IT systems to eQuest
eQuest
Process activities
Web-
application
 Questionnaires as XML files are generated by
API
the eQuest system run by Statistics Austria.
ERP/
TSM  These Questionnaires contain the selection
criterias for the data export.
Database
XML-
Access
Data
export
Selection
criteria Question-  The data export extracts the information out
naire
of the ERP/TMS-systems and saves them as
XML-files (4 predefined formats).
Exported
XML data
 These files are uploaded to the „SGVS-
Konsole“ web-application.
 The respondent can now revise the data.
Web application  The web-application generates a „completed
„SGVS Console“
Questionnaire questionnaire“ and uploads this to the eQuest
with data
system.
 In the eQuest system the report is finished.
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10. Web-Application
Page  10

11. Validation Rules (Excerpt)
 Every lorry or articulated vehicle mentioned in the NSI’s
questionnaire must have an entry in the company’s fleet
management system.
 Odometer readings at the beginning and the end of the reporting
period must be available, where the latter has to be greater or equal
than the former. If multiple odometer readings are available over
time, the sequence of readings must be non-decreasing.
 Every shipment must have been allocated to one or more sections of
a journey.
 If events and activities such as load, unload are reported or inferred
from the position data (see below), corresponding sections of
journey’s have to be reported as well.
 Reported sections and journeys of a given vehicle must not overlap

12. Automatic classification of goods
Official  In compliance with national regulations all transported goods are
statistics classified by the NST/R
 Hauliers and forwarding agents often use free text in their operative
Respondents data. (i.e. 10 ldm bathtubs, granite, etc.)
 Assignment to NST/R classification is conducted manually.
 Assignment of goods to NST/R classification through respondents
Experiences leads to incoherent results (DE)
 Hauliers provide free texts, classification is done by NSO (NL)
 Development of a model for automatic classification according to
InnoRFDat-X NST/R categories for all free texts.
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13. Automatic classification - experiences
Based on an algorithm trained on classification texts and applied to a sample of
1000 cargo descriptions from transport orders
Correct categorization Insufficiently discriminating Imprecisions
 Expect improvements  Pervasive use of product  Find a balance between
when trained using codes in one case „rote learning“ and the
respondents texts  Transport provider has capability to correctly
insufficient information classify new descriptions
(„45 parcels“)
Autoteile , Coils Stahl und Blech 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Bleche 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Bleche max . 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Bleche Überbreite 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Coils 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
kompl . Profile 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Leitschienen 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
nahtlose Stahlrohre 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Profile 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Profile 12,2 m 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Profile lt . Beilage 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Rohre 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Sonderfahrt , Profile 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Stabstahl 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Stahl 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Stahl ( S320GD+Z275MB) , 2 Coil 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Stahl Rohre 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Stahl Vg . 1/7 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
Stahlbleche 5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
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14. Route & Event Detection
Protoytped process deployed to generate trips using GPS data
•Timestamp Objective
•GPS-Coordinates
Speed gradient  Position data file containing
the geographic details of
Detect Stops Geographic change every tour where
Loading Spatial distance  a tour starts with the
point
order info: •Stop-Position
loading of an empty lorry
(ZIP-Code) •Stop-Duration
 ends with the unloading
Mandatory rest period of the last cargo
Classify Stops
Distance to  Two-Step Heuristics applied
point of loading
 First to detect all stops
Distance to
motorway services, etc. in the data file
 Second to eliminate non-
loading/unloading stops
Resting stop Loading stop
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16. Event detection performance
 Heuristics gave
100% Recall but
only 28% Precision
 Heuristics + Order
details (ZIP-Codes)
gave 100% Recall
and 85% Precision

17. Lessons learned
 The collection and use of operational available at transport
companies to produce road traffic statistics is feasible
 Standardized interfaces can be cost-effectively implemented
 Research implications
 Use patterns in position data to discriminate between rest and load/unload
stops (independent of consignment information)
 Respondent specific training sets is expected to increase the precision of
goods classification
 Generation of data from ERP/TMS-system would allow for continuous reporting
of transport activities
 Recommended changes to the legal framework
 Enable NSI‘s to utilize available IT assets for the collection of raw data
 Adapt statistical sampling to changes in the market place (freight forwarders)
 Uniform collection and production methodologies across EU member states

18. TMS Software penetration in Austria
Methodology
32
12  Contacts to 55 larger companies,
o.w. 29 responded and provided
40 information
14  Significant proportion has
outsourced transport: Strabag, Rail
Cargo, Lenzing AG, Magna Steyr,
14 Borealis
15
 Respondents represented 7,3 % of all
trucks registered in Austria (SGVS
2007, Q4)
Sauer Bespoke SW
Hypersoft, -sped Keine TMS  5.230 / 72.000 = 7,3 %
Helpten COSware
C-Logistic Transporeon (?)
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19. The way forward
 Solution is not restricted to the Austria territory.
 The choice of application architecture and scope has been guided by the
vision of a European rather than only a national application.
 A Europe wide adoption of the approach is expected to increase
quality of the data collected by the member states.
 applicability of transport statistics all over Europe will be enhanced
 comparability of transport statistics across countries will be improved.
 Piloted process has shown the potential to substantially reduce the
administrative burden on reporting companies
 Expectation to further raise the efficiency of statistical production
processes leading to cost reductions and time savings.

20. Summary
 Model deployed is capable to automatically classify cargo
 Sufficient precision achieved after training with respondent specific
datasets
 Less effort required from the respondents
 Quality improvements as a consequence of consistent classification
 Implementation aspects
 Improve „training phase“ using both descriptions and pre-
classifications of a sample of respondents
 Encourage consigners to provide more descriptive data of their goods
 Provide functions to manually override misclassifications
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