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The correction of endogeneity is a problem in strategic transport modelling; the question remains on how to make appropriate forecasts in this case. We propose a variation of the classical Control Function(CF) method, called Control Function Updated(CFU), which considers updating the endogeneity correction using information from the future equilibria. The proposed method is assessed using Monte Carlo simulation for a strategic transport model affected by three endogeneity sources, examining the equilibrium results for various future scenarios. We compare the CFUmethod by doing nothing and with the classical CFapproach. The forecasts are evaluated in terms of recovering the true (simulated) travel times and two indices of fit. Results show that the endogenous (do nothing) model produces large biases in simulated travel times and poor goodness-of-fit measures that steeply worsen with time in future scenarios. The corrected models perform much better and, in particular, the new CFUapproach shows statistically significant improvements over the classical approach in all scenarios tested.

ABSTRACTClassifying individuals into distinctive groups in discrete choice modelling work has become a common procedure since latent class models were introduced in the field. However, latent classes have certain shortcomings regarding the interpretation of the identified classes. On the other hand, hybrid choice models incorporating latent variables and psychometric data, are a powerful tool to treat and identify some underlying attitudes affecting behaviour; however, the treatment of the latent variables into the utility function has not been analysed in sufficient depth. Latent variables accounting for attitudes resemble socio-economic characteristics and, therefore, both systematic-taste-variations and categorizations may also be considered.We examine different ways to categorize individuals based on latent characteristics, and explain why this may be convenient. In particular, we propose a direct categorization of individuals based on underlying latent variables and conduct theoretical analyses contrasting this method with existing approaches. Based on this analysis, we conclude that some of the methods used in the past exhibit certain shortcomings that can be overcome by relying on a direct categorization. Then, we show some of the analytical advantages of the approach with the aid of two illustrative examples. Furthermore, the proposed approach allows bridging the gap between latent classes and latent variable models.