Difference between revisions of "Understanding passenger modal preferences"

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''This page is about the applications of machine learning (ML) in the context of understanding passenger modal preferences. For an overview of the modal share (or modal split) more generally, please see the [https://en.wikipedia.org/wiki/Modal_share Wikipedia page] on this topic.''
   
 
In order for large fractions of trips to be shifted from unsustainable transportation modes (e.g. private vehicles) to more sustainable modes (e.g. public transportation), it is key to understand why passengers currently use a mode rather than another one, and under which conditions would they be willing to change their habits. ML can help understand data and model behaviors related to trips and their motivations.
 
In order for large fractions of trips to be shifted from unsustainable transportation modes (e.g. private vehicles) to more sustainable modes (e.g. public transportation), it is key to understand why passengers currently use a mode rather than another one, and under which conditions would they be willing to change their habits. ML can help understand data and model behaviors related to trips and their motivations.

Latest revision as of 15:06, 26 August 2021

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This page is about the applications of machine learning (ML) in the context of understanding passenger modal preferences. For an overview of the modal share (or modal split) more generally, please see the Wikipedia page on this topic.

In order for large fractions of trips to be shifted from unsustainable transportation modes (e.g. private vehicles) to more sustainable modes (e.g. public transportation), it is key to understand why passengers currently use a mode rather than another one, and under which conditions would they be willing to change their habits. ML can help understand data and model behaviors related to trips and their motivations.

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