Development of a Map-Matching Algorithm for Rural Passenger Information Systems through Mobile Phones and Crowd Sourcing

Nagendra R. Velaga, John D. Nelson, Peter Edwards, David Corsar, Somayajulu Sripada, Nirwan Sharma, Mark Beecroft

Research output: Contribution to journalArticlepeer-review


In this research, a passenger-centric passenger information system is proposed, which uses crowd sourcing and mobile phones so that passengers are not only information consumers but are also providers of information to the system. Passengers can allow the system to track their location through their smart phones when they are traveling on public transportation; this will compensate for the lack of a vehicle tracking system in public transportation, particularly in nonurban areas. Map-matching (MM) algorithms integrate location data (i.e., latitude and longitude) obtained from positioning sensors (in this case, mobile GPS) with a digital geographic information system (GIS) road map.
Any map-matching algorithm first identifies the road link on which a vehicle is traveling and then determines the vehicle’s location on that road segment. In the proposed information system, at a given point of time, a number of vehicle locations (latitude and longitude) are received from passengers traveling on a bus. In order to provide a precise vehicle location at a given point of time, a novel map-matching algorithm using fuzzy logic, which integrates multiple vehicle locations (obtained from passenger’s smart phones) with a GIS road map, has been developed. The developed map-matching algorithm was tested using real-world data collected on four different bus routes in Aberdeenshire,
Scotland, and also GPS data collected in and around Nottingham, United Kingdom. It was identified that the developed MM algorithm is efficient and capable of supporting the proposed passenger information system
Original languageEnglish
Pages (from-to)732-742
Number of pages11
JournalJournal of Computing in Civil Engineering
Issue number6
Early online date14 Aug 2012
Publication statusPublished - Nov 2013

Bibliographical note

This research is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, award reference EP/G066051/1. Positioning data from Nottingham was obtained from Real-time Intelligent Map-matching Algorithms for Advanced Transport Telematics Systems project (funding reference number: EP/F018894/1)


  • Map-matching
  • GPS
  • Passenger information
  • Crowd sourcing
  • Fuzzy logic


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