Transportation: Difference between revisions
→Machine Learning Application Areas: -> added list of AAs; added subpages and two-sentence descriptions for Reducing transportation activity
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== Machine Learning Application Areas ==
*'''[[Understanding mobility patterns]]:''' Large amounts of geolocated traces are being collected that enable to analyze mobility patterns, which can be useful for example for planning transportation networks. ML can help find relevant patterns, such as transportation modes.
▲=== Reducing transport activity ===
*'''[[Modeling demand for passenger and freight transportation]]:''' Designing efficient transportation systems requires to know well the transportation demand in order to be well adapted to it. ML can improve standard demand modelling tools such as discrete choice models.
*'''[[Enabling low-carbon shared mobility]]:''' 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.
*'''[[Routing for freight and passenger vehicles]]:''' By taking the best possible route, aircrafts, cars and other vehicles can get to their destination with less energy compared to the choices that are most often made. ML can help navigate through large number of possible choices to find optimal pathways.
*'''[[Freight consolidation]]:''' Bundling shipments together through freight consolidation can dramatically reduce the number of trips and associated GHG emissions. ML can optimize complex relationship between the various dimensions involved in shipping decisions, such as shipment mode and origin-destination pairs.
=== Improving vehicle efficiency ===
* Designing
* Improving driving efficiency
*Optimizing public transportation services
=== Alternative fuels and electrification ===
* Electric vehicles driving cycle optimization
*Electric vehicle charging infrastructure
*[[Accelerated Science|Accelerated science]] for alternative fuels
* Electric vehicle [[demand response]]
=== Modal shift ===
*
* Fostering urban cycling
*Supporting public transportation network expansion
== Background Readings ==
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