Understanding mobility patterns: Difference between revisions
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Large amounts of geolocated traces are being collected that enable the analysis of mobility patterns. This can be useful for better managing existing as well as planning future transport systems. Machine learning (ML) offers great potential to progress the following areas.
To better understand existing transport systems, '''
Furthermore,
== Background Readings ==
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=== '''Analysis and Classification of Travel Patterns''' ===
'''“Urban Human Mobility Data Mining: An Overview (2016)”'''<ref>{{Cite journal|last=Zhao|first=Kai|last2=Tarkoma|first2=Sasu|last3=Liu|first3=Siyuan|last4=Vo|first4=Huy|date=2016|title=Urban human mobility data mining: An overview|url=http://ieeexplore.ieee.org/document/7840811/|journal=2016 IEEE International Conference on Big Data (Big Data)|location=Washington DC,USA|publisher=IEEE|volume=|pages=1911–1920|doi=10.1109/BigData.2016.7840811|isbn=978-1-4673-9005-7|via=}}</ref>
'''“Truck traffic monitoring with satellite images (2019)”'''<ref>{{Cite journal|last=Kaack|first=Lynn H.|last2=Chen|first2=George H.|last3=Morgan|first3=M. Granger|date=2019-07-17|title=Truck Traffic Monitoring with Satellite Images|url=http://arxiv.org/abs/1907.07660|journal=arXiv:1907.07660 [cs]}}</ref>: An example of how satellite images can be utilised to detect average road traffic.
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