In this research, a real-time positioning method, which utilises crowdsourced positioning data obtained from smartphone GPS is developed. Such vehicle location information obtained from crowdsourcing and smartphones in public transport could replace traditional automatic vehicle location systems. However, the location information from smartphone GPS is more erroneous. The proposed methodology serves as an alternative to existing positioning methods to improve the vehicle positioning accuracy. The developed enhanced particle filter algorithm takes smartphone GPS positioning data [from multiple passengers in a single transit vehicle (e.g. bus)] as input data. This ‘crowdsourced’ data can then be utilised to calculate the vehicles’ positioning information with better accuracy using the developed enhanced particle filter algorithm. The developed algorithm was tested using data collected on 14 different bus routes in urban and suburban areas of Mumbai, India, and it was identified that the algorithm is effective in reducing the average error up to 21.3% from a regular smartphone GPS and 10% from extended Kalman filter algorithm and was able to curtail positioning error within 8.672 m (average over 14 routes).
Smartphone-based crowdsourcing for position estimation of public transport vehicles
IET Intelligent Transport Systems ; 11 , 9 ; 588-595
2017-09-28
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
crowdsourcing , smartphone GPS positioning data , vehicle positioning accuracy , enhanced particle filter algorithm , automatic vehicle location systems , Global Positioning System , traffic information systems , intelligent transportation systems , data collection , particle filtering (numerical methods) , position estimation , public transport , crowdsourced data , bus routes , public transport vehicles , smart phones , real-time positioning method , vehicle location information , extended Kalman filter algorithm , mobile computing , smartphone-based crowdsourcing
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