Understanding mobility patterns: Difference between revisions

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:.

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), vehiclethis detectioncould, canfor be of high value toexample, 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>, which in turn creates space for more low carbon mobility, such walking or riding a bicycle.