Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
The travel demand forecasting capability is affected by the model
performance and is restricted by the available modes. The Sampers models
being used in long distance travel demand forecasting doesn’t deal with
nonlinearity and contains no travel service qualitative variables. The RP models
can’t forecast the travel demand of the hypothetical mode of the high speed rail.
Meanwhile, the value of time which is an important indicator in cost-benefits
evaluation needs to be estimated in more specific way. Functional form
improving method of Box-Cox transformation is proved to be efficient in dealing
with nonlinearity and so does the piecewise function is effective in using
discrete variables. Variable related travel service quality is proved to be a
significant estimate in enriching model specification. The value of time is
re-evaluated by taking into account its distribution. The mean values are
sensitive to the model specified and the extent of self-selection has been
analyzed by comparing the value of different current mode users with different
alternatives Based on the stated choice survey about the high speed rail, the
preference and sensitivity is revealed by binary Logit model estimation. The
minor difference between X2000 and high speed rail and self-selection effect
imply the little attractiveness of high speed rail for both train users and air users.
2011. , 85 p.