Uncertainty in traffic models

The paper begins with a literature review of the surprisingly limited quantitative oriented literature on uncertainties of transport models systems as a whole. Based on this review, is suggested a structure that categorises different sources of uncertainty. The sources of uncertainties are then arranged in order to describe the four most commonly used model components, i.e. 1) trip generation, trip attraction and balancing, 2) trip distribution, 3) mode choice, and 4) assignment. These may occur in various nesting structures and step orders. Once the structure of sources of uncertainty have been outlined, it is fairly easy to estimate the statistical distribution of each source, if this is indeed based on a new study and statistical estimation (which is e.g. often the case for mode choice models). Itis on the other hand much more difficult to assess the uncertainty on figures that are just based on guidelines or pure tradition, such as e.g. standard parameters in the US BPR (Bureau of Public Road) curve for speed-flow relationships. Many assignment models use e.g. standards software with standard values that are not estimated for the specific case.ûIf - however - uncertainties have been assessed by estimation, judgements or pure guesses, then the overall uncertainty of a model system may be assessed by Monte Carlo simulation techniques. Finally, how to model the interaction between model components is discussed. A fairly small transport model for the town of Nastved as a "test laboratory" for evaluation of model uncertainty.It is a traditional 4-step model of trip production (regression model), trip distribution (gravity model), mode choice (logit model) and assignmentmodels (stochastic user equilibrium for road, schedule-based assignment for bus). The model has 97 zones and about 3,000 links in the road network.Passenger transport is split into commuters, business and leisure trips, and vans and trucks are added for the road assignment. The model system was implemented in ArcGIS by Model Builder, which made it easy to add different assumptions of uncertainty and simulate this. Some conclusions are made on how some sources of uncertainty may propagate in a linear way, while other may influence the final result in a non-linear way. It was also revealed, that the uncertainty of some components in some model steps may havemuch higher influence on the outcome than other. For the covering abstract see ITRD E135582.

• Record URL:
  http://abstracts.aetransport.org/conference/index/id/12
• Availability:
  • Find a library where document is available. Order URL: http://etcproceedings.org/conference
• Corporate Authors:

  Association for European Transport (AET)

  1 Vernon Mews, Vernon Street, West Kensington
  London W14 0RL,    
• Authors:
  • NIELSEN, O A
  • KNUDSEN, M A
• Conference:
  • European Transport Conference (ETC)
  • Location: Strasbourg , France
  • Date: 2006-9-0 to 2006-9-0
• Publication Date: 2006

Language

• English

Subject/Index Terms

• TRT Terms: Conferences; Mathematical models; Planning; Traffic assignment; Transportation; Transportation modes; Travel; Trip generation
• Uncontrolled Terms: Selection
• ITRD Terms: 8525: Conference; 663: Generated traffic; 698: Journey; 6473: Mathematical model; 143: Planning; 9072: Selection; 679: Traffic assignment; 1155: Transport; 1145: Transport mode
• Subject Areas: Planning and Forecasting; Transportation (General); I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01089867
• Record Type: Publication
• Source Agency: Transport Research Laboratory
• ISBN: 1905701012
• Files: ITRD
• Created Date: Mar 17 2008 10:07AM