Estimating the Potential Modal Split of Any Future Mode Using Revealed Preference Data

Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode specifc parameters refecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specifc constant value. For new modes, the mode-specifc parameters and the constant in the utility function of discrete choice models are not known and are difcult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. Tis paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specifc constants and mode-specifc parameters. Tis establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. Tis results in a utility function without any alternative specifc constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. Te parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between diferent transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the frst time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.