Austria's traffic model is used by national and state authorities to plan infrastructure projects. It forecasts road and rail traffic volumes based on demographic and economic data. The iMOVE project aims to improve the model by combining different cargo movement surveys, incorporating toll data, and calibrating origin-destination flows using traffic counts. This will produce a more accurate and repeatable process for compiling cargo movement data on Austria's road network.

1. Cargo Movements on Austria„s
Road Network (iMOVE)
EUROSTAT, Oct 11, 2012

2. Austria„s traffic model (current & future)
 Used by the national & state
authorities to plan infrastructure
projects
 Passenger & cargo induced road
and rail traffic considering public
as well as private transport
 Forecast of volumes based on
demographic projections, trade Network characteristics
and production forecasts
 A series of connected sub-models  Overall 243.000 km (o. w.
with a detailed network at its 109.000 road, 84.000 rail)
core to map actual and  Austria 32.300 km (o. w. 26.000
road, 6.300 rail)
forecasted O/D flows  2412 counties/municipalities in
Austria; 216 NUTS3 / NUTS0
 Actual O/D data essentially regions in Europe
recompiled periodically (current
version 2009)
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3. Perceived weaknesses of the current approach
The model‘s empirical foundation
 Insufficient accuracy of total transportation volume
 Output of transportation sector 2004 estimates range between
363,5 and 415,8 mio. t/km depending on source
 Underestimation by the European Road Freight Statistics ?
 Inhomogeneous data collections
 Spatial differences (NUTS3, highway sections, counties)
 Methodological differences between countries in collecting
national road traffic data
 Periodical differences (quarterly, yearly (t+1), every four
years) between surveys
 Laborious, expensive production process of base data
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4. Main objectives of the iMOVE project
Improve the production process and the quality of
the O/D flow matrices
 Combine & harmonize different surveys
 Incorporate highway toll data into the O/D flows
 trips / between counties per truck category per day
 Calibrate the O/D flows, using traffic count numbers of
permanent counting stations
 [Verify and consider the hypothesized growth in
multimodal movements]
 Prototype a repeatable data production process

5. Major data sources used to compile the O/D matrix
Austrian Road Freight Transport Statistics (SGVS)
 Sampling based on Austria‘s Vehicle Registry
 Political District Level
 Assumed underestimation (foreign (non EU) trucks/non-response)
European Road Traffic Statistics (D-Tables)
 Only trucks registered nationally are surveyed, combined at EU level
 Movements at NUTS3 level
 Assumed underestimation (foreign (non EU) trucks/non-response)
Cross Alpine Freight Data (CAFT)
 All trucks, irrespective of nationality are surveyed
 Survey performed by Alpine nations every 4 years (O/D, cargo)
 Performed at border crossings and major mountain passes
Toll data records & Traffic counts
 Complete set of records collected at gantries for 2009 (highways)
 Traffic count data using automatic permanently installed systems
(primary & secondary roads)
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6. Planned production process
Traffic survey‘s Traffic counts
Cross Alpine European Toll Data Traffic Count
Freight Road Freight Records Data
Austrian Road
Combine &
Freight
Statistics disaggregate Map flows onto
network graph
Calibration using the Network Model
Analyze and correct deviations

7. Experiences, Status
 Combining different survey„s and traffic count information
 Reconciling different value sets used in surveys to describe the same
properties: Truck sizes, cargo types, goods classifications
 Harmonizing, separating the use of different NUTS3 levels for origin
and destination
 Measuring the number of movements between two “traffic
cells”
 Derivation of journey’s from data collected at toll bridges
 Disaggregating journeys from reported levels to „traffic cells“, using
ecological inference approach.
 Calibrating the route allocation of movements to road links
 Minimizing deviations between calculated and counted traffic per link
observing O/D movement bounds using non-linear optimization.
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