By extracting data from Enterprise Resource Planning (ERP) and Transport Management (TM) systems, particularly larger companies can easily generate data for official reporting obligation and directly transfer it to the National Statistical Institution (NSI).

1. Innovative data collection methods for road freight
transport statistics

2. 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.
Page  2

3. 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 trips
 respondents assumed to minimize efforts, and report a vehicle to be out-of-order during the sample
week.
 Limited accuracy of cargo types i
 Respondents have limited information regarding cargo moved („mixed cargo“)
 Assignment of goods to NST/R classification by respondents leads to incoherent results
Page  3

4. 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
Page  4

5. Goals and Results of the Project
Project Goals
 Further reduction of the
respondents efforts through
automation
 Return to a larger sample to meet
national requirements
 Increase of data quality and
actuality
 Reduction of required ressources for
preparation and processing of the
data
Page  5
Project deliverables
 Prototypic and fully functional
implementation of the connection
between data (from companies) to
the XML-Interface
 Test the applicability of automatic
data collection technologies and
algorithms to obtain precise
measurements.
 Legal, economic and methodical
evaluation of the results with
respect to the road freight transport
statistic

6. Research Objectives
 Prove the technical feasibility
 building such a working prototype
 develop a sufficiently generic standard interface
 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

7. International coordination
Identify similar European initiatives
 Identification of similar projects in Europe
 Learn from experience and best practice
 Implications of easier data exchange for survey design and sampling
EUROSTAT
 International harmonization of electronic data exchange
 Potential requirements for national RF surveys (data collection)
 Standards and architectures
Software providers (ERP, TMS)
 Statistical microdata exporter as standard software component
 Economies of scale targeting the EU marketplace
 Talks with SAP, Navision, TransIT, Sauer, …
Page  7

8. International responses
EUROSTAT: Joint approach of data collection
 Metadata – enrichment as early as possible
 Joint, comparable Method (for consolidation)
 Collection of fuel use (CO²-emission)
CBS (NL) is a guide for InnoRFDat-X
 Sustains good contacts with SW-industry for realisation of XML-based
reports
 Uses algorithms for ease of input (goods classification, route
validation)
Experiences of the Ministère du Développement Durable (FR)
 Hesitant Respondents
 Heavily fragmented company landscape with litte IT use
 No strategy for integration of SW-industry
Page  8

9. International responses
Trafik Analys (Sweden)
 At the moment with traditional questionnaires only with manual
recording
 Interested in InnoRFDat-X approach
Kraftfahrt-Bundesamt Flensburg (Germany)
 Survey: Increasing use of standardised software but lack of citical
mass of one provider
 Search for alternative data sources for groups of goods, origin &
destination
Danske Statistik (Denmark)
 Legal requirement to replace paper as used medium by the end of
2012 -> Web-questionnaire with manual input
 Attempts on using TMS/ERP-data
Page  9

10. Target Solution Architecture
 Data interfaces are in the public
domain and provided to the
Software- and System developers
 Completion of missing data and
corrections are performed by the
respondent
 Different information entities are
consolidated according to the data
model specifications and business
logic
 Based on the transfer-format the
specific required structure for the
respective country questionaire is
generated

11. Stakeholders & expected benefits
Respondents
 Carriers
 Freight Forwarders
 Companies with own fleet
Users
 Public sector decision
makers
 Interest groups
 General public
Increase of data quality
Producers
 National Statistics
Institutes
 Ministries of Transportation
Page  11
Reduce cost
Lower production cost
 No need to collect data manually
 Reduction of paper-based work
 No follow-up calls from NSI‘s
 Increased accuracy
 Increased coverage
 Less correction, completion efforts
 Reduce, eliminate paper based work

12. Information elements collected
4 XML-based interface specifications are provided
FleetMasterData
FleetStatusData
ConsignmentData
PositionData
Data on lorries
and trailers
(capacity, axles,
odometer, age,
license, etc.)
Information on
specific lorries at
certain times
(driven distance,
fuel usage, etc.)
order related
data containing
information on
goods, packaging,
origin and
destination …
GPS readings,
country/ZIP
codes, activity
(loading, border
crossing)
Page  12

13. The information model
 0 or more journeys performed during the observation
period per vehicle can be reported.
 0 to 3 trailers per journey can likewise be reported as
well as 0 or more different shipments per journey.
 0 or more combined
transports per journey
can be reported.
class PIM Ov erv iew
query
response
 0 or more transit countries
(international journeys) per
journey can be reported
(not shown in the diagram).
query::j ourney::
combinedTransport
query::j ourney::shipment::commodity::
dangerous
1
0..*
1..*
query::motorVehicleNotification
query::j ourney
0..*
0..*
1..*
1
 0 or more shipments per
journey may be reported;
each shipment can be associated with 0 or more
containers.
query::motorVehicle
 A shipment can comprise 1 or more commodities; each
commodity can be classified to be a dangerous good if
applicable.
0..1
0..3
query::j ourney::
trailer
query::j ourney::
shipment
0..2
query::j ourney::
shipment::container
query::j ourney::
1..* shipment::commodity

14. Data Collection Service
Protoytped process deployed to move data from IT systems to eQuest
eQuest
Webapplication
API
ERP/
TSM
Insert your own text here
 Questionnaires as XML files are generated by
the eQuest system run by Statistics Austria.
 These Questionnaires contain the selection
criterias for the data export.
Database
Access
Data
export
Selection
criteria
XMLQuestionnaire
Exported
XML data
 The data export extracts the information out
of the ERP/TMS-systems and saves them as
XML-files (4 predefined formats).
 These files are uploaded to the „SGVSKonsole“ web-application.
 The respondent can now revise the data.
Web application
„SGVS Console“
Questionnaire
with data
 The web-application generates a „completed
questionnaire“ and uploads this to the eQuest
system.
 In the eQuest system the report is finished.
Page  14

15. Web-Application
Page  15

16. Web-application
Page  16

17. 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

18. Automatic classification of goods
Official
statistics
 In compliance with national regulations all transported goods are
classified by the NST/R
Respondents
 Hauliers and forwarding agents often use free text in their operative
data. (i.e. 10 ldm bathtubs, granite, etc.)
 Assignment to NST/R classification is conducted manually.
Experiences
 Assignment of goods to NST/R classification through respondents
leads to incoherent results (DE)
 Hauliers provide free texts, classification is done by NSO (NL)
InnoRFDat-X
 Development of a model for automatic classification according to
NST/R categories for all free texts.
Page  18

19. 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
 Expect improvements
when trained using
respondents texts
Autoteile , Coils Stahl und Blech
Bleche
Bleche max .
Bleche Überbreite
Coils
kompl . Profile
Leitschienen
nahtlose Stahlrohre
Profile
Profile 12,2 m
Profile lt . Beilage
Rohre
Sonderfahrt , Profile
Stabstahl
Stahl
Stahl ( S320GD+Z275MB) , 2 Coil
Stahl Rohre
Stahl Vg . 1/7
Stahlbleche
Page  19
Insufficiently discriminating
Imprecisions
 Pervasive use of product
codes in one case;
 Find a balance between
„rote learning“ and the
capability to correctly
classify new descriptions
 Transport provider has
insufficient information
(„45 parcels“)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)
5 - Eisen, Stahl und NE-Metalle (einschl. Halbzeug)

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 preclassifications of a sample of respondents
 Encourage consigners to provide more descriptive data of their goods
 Provide functions to manually override misclassifications
Page  20

21. Route & Event Detection
Protoytped process deployed to generate trips using GPS data
Objective
•Timestamp
•GPS-Coordinates
Speed gradient
Geographic change
Detect Stops
Loading
point
order info:
(ZIP-Code)
Spatial distance
•Stop-Position
•Stop-Duration
Mandatory rest period
Distance to
point of loading
Classify Stops
Distance to
motorway services, etc.
Resting stop
Page  21
Loading stop
 Position data file containing
the geographic details of
every tour where
 a tour starts with the
loading of an empty lorry
 ends with the unloading
of the last cargo
 Two-Step Heuristics applied
 First to detect all stops
in the data file
 Second to eliminate nonloading/unloading stops

22. Route & Event Detection: Prototype
Implementation architecture
Implementation
 GPS data input (via ad hoc
XML file)
 Consignment data input using
interface definition
 Linkage between GPS Data
and consignment data via
postcodes (geoname.org)
 Result fed into the
Position.Data interface
definition
Page  22

23. Event detection performance
 Heuristics gave
100% Recall but
only 28% Precision
 Heuristics + Order
details (ZIP-Codes)
gave 100% Recall
and 85% Precision

25. Live Test Run Experiences / Feedback
Use of unfamiliar terms which did not always
correspond with specific business practices
Usability problems with handling the web-service
prototype (method to complex)
Interface was tested successfully and is usable
Inconsistencies and errors in the application were
corrected
Journeys could be reconstructed based on position data
and associated with certain orders
Automatic classification of transported goods possible
Page  25

26. TMS Software penetration in Austria
12
Methodology
32
40
14
 Contacts to 55 larger companies,
o.w. 29 responded and provided
information
 Significant proportion has
outsourced transport
14
15
 Respondents represented 7,3 % of all
trucks registered in Austria (SGVS
2007, Q4)
 5.230 / 72.000 = 7,3 %
Sauer
Hypersoft, -sped
Helpten
C-Logistic
Page  26
Bespoke SW
No TMS
COSware
Transporeon (?)

27. 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
 A large set of position data should reveal patterns of loading/unloading
locations to better determine between rest and load/unload stops
 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
 New regulations to mandate the collection and production methodologies
 Development towards a mode-integrative approach
 Obligation/encouragement of NSI‘s to utilize available technologies for the
collection of raw data

28. 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 our methodology is expected to
significantly increase both quality as well as the quantity 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
 It is also expected to further raise the efficiency in the statistical
production process leading to cost reductions and time savings.