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Abstract

Mobile participatory 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.

Keywords

Train Station Post Processing Module Collect Sensor Data Societal Phenomenon Cellular Data Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Raghu Ganti
    • 1
  • Iqbal Mohomed
    • 1
  • Ramya Raghavendra
    • 1
  • Anand Ranganathan
    • 1
  1. 1.IBM T. J. Watson Research CenterHawthorneUSA

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