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''This page is about the applications of machine learning (ML) in the context of understanding mobility patterns. For an overview of mobility models more generally, please see the [https://en.wikipedia.org/wiki/Mobility_model Wikipedia page] on this topic.''
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. ML offers great potential to progress the following areas. ▼
 
▲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.
=== '''Vehicle and road detection''' ===
To better understand existing transport systems, machine learning can be used to detect the structure of a transport network as well as its utilisation. In the context of highly congested cities (especially with parked cars), this could, for example, support the discussion for a more equal street space allocation and the adoption of more low carbon mobility, such as walking or riding bicycles<ref>{{Cite journal|last=Creutzig|first=Felix|last2=Javaid|first2=Aneeque|last3=Soomauroo|first3=Zakia|last4=Lohrey|first4=Steffen|last5=Milojevic-Dupont|first5=Nikola|last6=Ramakrishnan|first6=Anjali|last7=Sethi|first7=Mahendra|last8=Liu|first8=Lijing|last9=Niamir|first9=Leila|last10=d’Amour|first10=Christopher Bren|last11=Weddige|first11=Ulf|date=2020-11-01|title=Fair street space allocation: ethical principles and empirical insights|url=https://doi.org/10.1080/01441647.2020.1762795|journal=Transport Reviews|volume=40|issue=6|pages=711–733|doi=10.1080/01441647.2020.1762795|issn=0144-1647}}</ref>. ▼
 
▲To better understand existing transport systems, machine learning'''ML''' '''can be used to detect the structure of a transport network as well as its utilisationutilization'''. In the context of highly congested cities (especially with parked cars), this could, for example, support the discussion for a more equal street space allocation and the adoption of more low carbon mobility, such as walking or riding bicycles<ref>{{Cite journal|last=Creutzig|first=Felix|last2=Javaid|first2=Aneeque|last3=Soomauroo|first3=Zakia|last4=Lohrey|first4=Steffen|last5=Milojevic-Dupont|first5=Nikola|last6=Ramakrishnan|first6=Anjali|last7=Sethi|first7=Mahendra|last8=Liu|first8=Lijing|last9=Niamir|first9=Leila|last10=d’Amour|first10=Christopher Bren|last11=Weddige|first11=Ulf|date=2020-11-01|title=Fair street space allocation: ethical principles and empirical insights|url=https://doi.org/10.1080/01441647.2020.1762795|journal=Transport Reviews|volume=40|issue=6|pages=711–733|doi=10.1080/01441647.2020.1762795|issn=0144-1647}}</ref>.
=== '''Analysis and Classification of Travel Patterns''' ===
 
TheFurthermore, analysis'''ML andapplied classificationto''' of'''analyze and classify travel patterns''' can serveshelp to better understand urban mobility flows. As human mobility in urban areas follow strong regularities<ref>{{Cite journal|last=González|first=Marta C.|last2=Hidalgo|first2=César A.|last3=Barabási|first3=Albert-László|date=2008|title=Understanding individual human mobility patterns|url=https://www.nature.com/articles/nature06958|journal=Nature|language=en|volume=453|issue=7196|pages=779–782|doi=10.1038/nature06958|issn=1476-4687|via=}}</ref>, this understanding constitutes the basis for making short- and long-term predictions of mobility demand.
 
MachineNext Learningto that, '''ML can be used to assess howthe aimpact givenof a''' '''local context, (such as the urban form,) impactson mobility'''. Building up on this, planning strategies can be derived which support a low carbon transport future. ▼
=== '''Impact of urban form on mobility''' ===
▲Machine Learning can be used to assess how a given local context, such as the urban form, impacts mobility. Building up on this, planning strategies can be derived which support a low carbon transport future.
 
== 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> ''':''' An Overview This paperreview provides a great overview of the complete modelling process, starting from finding potential data sets, cleaning and preprocessing data sets, searching for patterns using ML, evaluating models and obtained patterns as well as acting on discovered knowledge. When it comes to finding mobility patterns, the authors also highlight different predictors and their applicability in different contexts.
 
'''“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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