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Gerard de Jong Andrew Daly Marits Pieters Stephen Miller Ronald Plasmeijer Frank Hofman 《Transportation》2007,34(4):375-395
This paper provides a review of transport model applications that not only provide a central traffic forecast (or forecasts
for a few scenarios), but also quantify the uncertainty in the traffic forecasts in the form of a confidence interval or related
measures. Both uncertainty that results from using uncertain inputs (e.g. on income) and uncertainty in the model itself are
treated. The paper goes on to describe the methods used and the results obtained for a case study in quantifying uncertainty
in traffic forecasts in The Netherlands.
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