Enabling low-carbon shared mobility: Difference between revisions
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''This page is about the applications of machine learning (ML) in the context of enabling low-carbon shared mobility. For an overview of shared transport more generally, please see the [https://en.wikipedia.org/wiki/Shared_transport Wikipedia page] on this topic.'' |
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For shared mobility to be a low-carbon option, it needs to effectively enable to reduce the number of kilometers travelled by pooling users. ML can help real-time decision for example for ride-hailing services. |
For shared mobility to be a low-carbon option, it needs to effectively enable to reduce the number of kilometers travelled by pooling users. ML can help real-time decision for example for ride-hailing services. |
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Latest revision as of 14:52, 26 August 2021
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This page is about the applications of machine learning (ML) in the context of enabling low-carbon shared mobility. For an overview of shared transport more generally, please see the Wikipedia page on this topic.
For shared mobility to be a low-carbon option, it needs to effectively enable to reduce the number of kilometers travelled by pooling users. ML can help real-time decision for example for ride-hailing services.