Data underlying the publication: A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data

This research proposes a way to estimate heterogeneous mode choice preferences in the context of intermodal transport directly from aggregate data. To do so, we develop a Weighted Logit Mixture (WLM) methodology , which is compared to a benchmark consisting of a Multinomial Logit (MNL) model. In the WLM, we estimate the Lognormal distribution of the intermodal cost coefficient within the shippers' population. Then, we compare the performance of our WLM against the benchmark in terms of mode share predictions, elasticities, and correlations. Finally, we also investigate the influence of adding the Value Of Time (VOT) in the WLM on the model's performance.