Road traffic modelling
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Transcript of Road traffic modelling
Cargo Movements on Austria„s Road Network (iMOVE)
EUROSTAT, Oct 11, 2012
Page 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
and production forecasts
A series of connected sub-models
with a detailed network at its
core to map actual and
forecasted O/D flows
Actual O/D data essentially
recompiled periodically (current
version 2009)
Network characteristics
Overall 243.000 km (o. w. 109.000 road, 84.000 rail)
Austria 32.300 km (o. w. 26.000 road, 6.300 rail)
2412 counties/municipalities in Austria; 216 NUTS3 / NUTS0 regions in Europe
Page 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
Main objectives of the iMOVE project
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
Improve the production process and the quality of
the O/D flow matrices
Page 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)
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)
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
Planned production process
Calibration using the Network Model
Analyze and correct deviations
Traffic survey‘s
European
Road Freight
Austrian Road
Freight
Statistics
Combine &
disaggregate
Cross Alpine
Freight
Toll Data
Records
Traffic Count
Data
Traffic counts
Map flows onto
network graph
Page 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.