Simultaneously Accounting for Inter-alternative Correlation and Taste Heterogeneity among Long Distance Travelers Using Mixed Nested Logit (MXNL) Model so as to Improve Toll Road Traffic and Revenue Forecast

This paper investigates the potential of using Mixed Nested Logit (MXNL) models to simultaneously account for inter-alternative correlation, taste heterogeneity and the distribution of willingness to pay for toll roads, using a Stated Preference dataset from toll route choice experiments conducted during a recent toll route study in Nigeria. By Mixed Nested Logit model the authors mean the model which combines the mixed logit with the nested logit estimated simultaneously. Results reveal the presence of both correlation (addressed by the nested logit model) and different taste heterogeneity (addressed by the mixed logit model). The estimation results for the combined mixed nested logit model are presented compared with individual estimation results for nested logit on its own, mixed logit on its own and multinomial logit. This paper is unique in that there does not seem to be much work in using this combined mixed nested logit model approach to understanding long distance travellers’ behaviour in the context of road pricing. This paper opens up this area for investigation and shows the additional explanation that can be potentially achieved to improve the models' forecasting ability.