Difference between revisions of "Supporting public transportation network expansion"

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''This page is about the applications of machine learning (ML) in the context of optimizing public transport. For an overview of public transport more generally, please see the [https://en.wikipedia.org/wiki/Public_transport Wikipedia page] on this topic.''
   
 
In the absence of a well-ramified public transportation network (which can include trains, subways, buses and other modes), the populations in the areas poorly covered have little access to low-carbon transportation options. ML can be used in the planning and design process of new public transit lines.
 
In the absence of a well-ramified public transportation network (which can include trains, subways, buses and other modes), the populations in the areas poorly covered have little access to low-carbon transportation options. ML can be used in the planning and design process of new public transit lines.

Latest revision as of 15:12, 26 August 2021

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This page is about the applications of machine learning (ML) in the context of optimizing public transport. For an overview of public transport more generally, please see the Wikipedia page on this topic.

In the absence of a well-ramified public transportation network (which can include trains, subways, buses and other modes), the populations in the areas poorly covered have little access to low-carbon transportation options. ML can be used in the planning and design process of new public transit lines.

Background Readings[edit | edit source]

Conferences, Journals, and Professional Organizations[edit | edit source]

Libraries and Tools[edit | edit source]

Data[edit | edit source]

Future Directions[edit | edit source]

Relevant Groups and Organizations[edit | edit source]

References[edit | edit source]