Edinburgh Napier University

  Trip generation is the first stage of the conventional `four-stage' transport model.
The aim of this stage is to predict total number of trips generated to and from
each zone. The two most common techniques for trip generation are linear
regression (the dependent vaziable is alinear-in-parameter function of a number
of explanatory variables) and category analysis including multiple classification
analysis (based on estimating number of trip generations as a function of
household attributes). Both techniques of trip generation rely on the availability
of a large socio-economic, mainly revealed preference data set. They also have
technical limitations such as the assumption of linearity which might result in
unreasonable predictions of trip generation. Any deficiency or inaccuracy in the
estimation at this stage will be carried over and will have implications on
subsequent stages.
The other stages of the `four-stage' model employ other techniques including
logistic analysis which broadens the scope of the analysis. Logistic regression
analysis has been used to model travel choices such as mode, route and departure
time but not trip generation. There has not been much research to investigate the
appropriateness of using this technique to model generation. The main
reason for this is that logistic regression predicts probabilities rather than the
total number of trips.
In order to be able to model trip generation using logistic regression, the number
of trips frequency) can be treated as a set of mutually exclusive categorical
variables; therefore the built-in upper and lower limits are incorporated.
Therefore, it is not possible to predict a negative number of trips and the
estimates of the model will show the underlying probabilities for the actual
number of This will also provide a behavioural framework that directly
links the number of trips to utility-based consumer and decision-making theory.
Logistic regression can be used to model trip generation as binary, multinomial
or nested logit frameworks. An added advantage of using this approach is the
ability to predict the frequency and number of trips made by each individual.
The aim of this research therefore, is to investigate possible methodologies to
improve performance of trip generation modelling. In order to achieve this aim
firstly, this research investigates the appropriateness of logistic regression to
model trip generation and device a methodology for it. The analysis and
comparisons of the results with results from conventional models are examined.
Exploring the use of stated preference data to calibrate trip generation models is
also studied here. Finally, transport policy measures and enhanced transport
accessibility functions have been investigated in generation models.