Mobile and Ubiquitous Systems: Computing, Networking, and Services. 8th International ICST Conference, MobiQuitous 2011, Copenhagen, Denmark, December 6-9, 2011, Revised Selected Papers

Research Article

Analysis of Data from a Taxi Cab Participatory Sensor Network

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  • @INPROCEEDINGS{10.1007/978-3-642-30973-1_17,
        author={Raghu Ganti and Iqbal Mohomed and Ramya Raghavendra and Anand Ranganathan},
        title={Analysis of Data from a Taxi Cab Participatory Sensor Network},
        proceedings={Mobile and Ubiquitous Systems: Computing, Networking, and Services. 8th International ICST Conference, MobiQuitous 2011, Copenhagen, Denmark, December 6-9, 2011, Revised Selected Papers},
        proceedings_a={MOBIQUITOUS},
        year={2012},
        month={10},
        keywords={},
        doi={10.1007/978-3-642-30973-1_17}
    }
    
  • Raghu Ganti
    Iqbal Mohomed
    Ramya Raghavendra
    Anand Ranganathan
    Year: 2012
    Analysis of Data from a Taxi Cab Participatory Sensor Network
    MOBIQUITOUS
    Springer
    DOI: 10.1007/978-3-642-30973-1_17
Raghu Ganti1, Iqbal Mohomed1, Ramya Raghavendra1, Anand Ranganathan1
  • 1: IBM T. J. Watson Research Center

Abstract

Mobile sensing applications are becoming quite popular, where individuals with mobile sensing devices such as smartphones, music players, and in-car GPS devices collect sensor data and share it with an external entity to compute statistics of mutual interest or map common phenomena. In this paper, we present an analysis of the data from a real-world city-scale mobile participatory sensor network comprised of about two thousand taxi cabs. Our analysis spans data collected from the taxi cab sensor network over the course of a year and we use it to make inferences about life in the city. The large scale data collection (size and time) from these taxi cabs allows us to examine various aspects about life in a city such as busy “party” times in the city, peak taxi usage (space and time), most traveled streets, and travel patterns on holidays. We also provide a summary of lessons learned from our analysis that can aid similar city-scale deployments and their analyses in the future.