Road traffic modelling

357 views

Published on

Published in: Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
357
On SlideShare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Road traffic modelling

  1. 1. Cargo Movements on Austria„sRoad Network (iMOVE)EUROSTAT, Oct 11, 2012
  2. 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)Page  2
  3. 3. Perceived weaknesses of the current approachThe 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 dataPage  3
  4. 4. Main objectives of the iMOVE projectImprove 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. 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)Page  5
  6. 6. Planned production process Traffic survey‘s Traffic counts Cross Alpine European Toll Data Traffic Count Freight Road Freight Records DataAustrian Road Combine & Freight Statistics disaggregate Map flows onto network graph Calibration using the Network Model Analyze and correct deviations
  7. 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.Page  7

×