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Social Network Based Crowd Sensing for Intelligent Transportation and Climate Applications

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Abstract

In recent years, the growing prevalence of social networks makes it possible to utilize human users as sensors to inspect city environment and human activities. Consequently, valuable insights can be gained by applying data mining techniques to the data generated through social networks. In this work, a practical approach to combine data mining techniques with statistical analysis is proposed to implement crowd sensing in a smart city. A case study to analyze the relationship between weather conditions and traffic congestion in Beijing based on tweets posted on Sina Weibo platform is presented to demonstrate the proposed approach. Following the steps of data pre-processing and topic determination, we applied Granger Causality Test to study the causal relationships between weather conditions, traffic congestion and human outdoor activity. The mediation analysis is also implemented to verify human outdoor activity as a mediator variable significantly carrying the influence of good weather to traffic congestion. The result demonstrates that outdoor activity serves as a mediator transmitting the effect of good weather on traffic congestion. In addition, the causes of negative emotion are also studied.

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Authors and Affiliations

  1. Computing Program, Macao Polytechnic Institute, Rua de L. Gonzaga Gomes, Macao

    Rita Tse, Lu Fan Zhang & Philip Lei

  2. Computer Science Department, University of California, Los Angeles, CA, USA

    Giovanni Pau

  3. Lip6 – University Pierre et Marie Curie, Paris, France

    Giovanni Pau

Authors
  1. Rita Tse
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  2. Lu Fan Zhang
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  3. Philip Lei
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  4. Giovanni Pau
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Correspondence to Giovanni Pau.

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Tse, R., Zhang, L.F., Lei, P. et al. Social Network Based Crowd Sensing for Intelligent Transportation and Climate Applications. Mobile Netw Appl 23, 177–183 (2018). https://doi.org/10.1007/s11036-017-0832-y

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  • DOI: https://doi.org/10.1007/s11036-017-0832-y

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