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In travel demand models, traditional objective attributes (TOAs) are very commonly used as explanatory variables. Nowadays, it is understood that latent variables (LVs) also significantly influence travellers' behaviour. A hybrid choice modelling approach allows LVs in mode choice utility functions to be addressed. Specifically, a hybrid random parameter logit (HRPL) model has been developed to explore these influences. In this study, a traditional RPL (TRPL) model is compared with an HRPL model. For the later model, a two-step approach (also known as sequential approach) is implemented to incorporate LVs in choice models. Step 1 is the estimation of a MIMIC (multiple indicators and multiple causes) model; a type of regression model with a latent dependent variable(s). Step 2 is the estimation of a choice model with random parameters; information from the first step is incorporated in the second step. The paper analyses and compares the results of applying these models to a real urban case study using two datasets: 2008/09 and 2010/11 household travel survey (HTS) of Sydney Statistical Division (SSD), and also evaluates the predicted changes of mode choice probabilities based on hypothetical scenarios. Our results show that the HRPL model is superior to TRPL models that ignore the effect of LVs on traveller choice. The minimal changes in the parameter coefficients between the two datasets for each model suggest that the changes in traveller choice behaviour are gradual. Three hypothetical scenarios are simulated to forecast the changes that would be relevant to transport policy responses.

TEMPORAL AND PARAMETRIC STUDY OF TRAVELLER PREFERENCE HETEROGENEITY USING RANDOM PARAMETER LOGIT MODEL