Modelling trip generation/ trip accessibility using logit models

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 theappropriateness 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 directlylinks 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