Understanding mobility patterns: Difference between revisions
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Added a short overview + Background Readings. - For questions etc. feel free to contact me: fw349(at)cam.ac.uk
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Large amounts of geolocated traces are being collected that enable
* Vehicle and road detection
* Analysis and classification of travel patterns
* Understanding the interplay between urban form and mobility
=== '''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), vehicle detection can be of high value to support the discussion for a more equal street space allocation<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>, which in turn creates space for more low carbon mobility, such walking or riding a bicycle.
=== '''Analysis and Classification of Travel Patterns''' ===
The analysis and classification of travel patterns serves 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.
=== '''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 ==
=== '''Vehicle and road detection''' ===
'''“Fast deep vehicle detection in aerial images (2017)”'''<ref>{{Cite journal|last=Sommer|first=Lars Wilko|last2=Schuchert|first2=Tobias|last3=Beyerer|first3=Jurgen|date=2017|title=Fast Deep Vehicle Detection in Aerial Images|url=http://ieeexplore.ieee.org/document/7926624/|journal=2017 IEEE Winter Conference on Applications of Computer Vision (WACV)|location=Santa Rosa, CA, USA|publisher=IEEE|volume=|pages=311–319|doi=10.1109/WACV.2017.41|isbn=978-1-5090-4822-9|via=}}</ref>''':''' An example of how high-resolution images can be used to detect vehicles.
=== '''Analysis and Classification of Travel Patterns''' ===
'''“Urban Human Mobility Data Mining (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 paper provides a great overview of the complete modelling process, 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.
“'''Mining smart card data for transit riders' travel patterns (2013)”'''<ref>{{Cite journal|last=Ma|first=Xiaolei|last2=Wu|first2=Yao-Jan|last3=Wang|first3=Yinhai|last4=Chen|first4=Feng|last5=Liu|first5=Jianfeng|date=2013-11-01|title=Mining smart card data for transit riders’ travel patterns|url=http://www.sciencedirect.com/science/article/pii/S0968090X13001630|journal=Transportation Research Part C: Emerging Technologies|language=en|volume=36|pages=1–12|doi=10.1016/j.trc.2013.07.010|issn=0968-090X}}</ref>. An example of how smart card data is used to model travel patterns of transit riders in Beijing.
=== '''Impact of urban form on mobility:''' ===
'''“Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo (2018)”'''<ref>{{Cite journal|last=Ding|first=Chuan|last2=Cao|first2=Xinyu (Jason)|last3=Næss|first3=Petter|date=2018-04-01|title=Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo|url=http://www.sciencedirect.com/science/article/pii/S0965856417310030|journal=Transportation Research Part A: Policy and Practice|language=en|volume=110|pages=107–117|doi=10.1016/j.tra.2018.02.009|issn=0965-8564}}</ref>: An example of how gradient boost decision trees are used to examine the effect of urban form on mobility.
== Community ==
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== References ==
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