A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models

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How app-based ride-hailing services influence travel behavior: An empirical study from China (2020, methodology): “Consequently, two discrete choice models—a multinomial logit (MNL) model and a binary logit model (Koppelman & Bhat, 2006)—are applied here to determine and quantify the effects of these factors on individuals’ travel mode choices behavior and car-purchasing behaviors, respectively.”
Willingness to Pay for Energy Conservation and Free-Ridership on Subsidization: Evidence from Germany (2009, unknown): “As discussed by Koppelman and Bhat (2006), this more flexible approach captures both heterogeneous preferences and complex correlation patterns by layering the error components on top of the random-parameter logit model.”
An analysis of bicycle route choice preferences in Texas, US (2009, background): “Thus, an advantage of using a disaggregate-level analysis framework is that it better captures the fundamental behavioral relationship between bicyclist route preferences and its determinants (see Koppelman and Bhat, 2006 for an extensive discussion).”