Smart Objects and Technologies for Social Good. Second International Conference, GOODTECHS 2016, Venice, Italy, November 30 – December 1, 2016, Proceedings

Research Article

Crowd Sensing of Weather Conditions and Traffic Congestion Based on Data Mining in Social Networks

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  • @INPROCEEDINGS{10.1007/978-3-319-61949-1_37,
        author={Rita Tse and Lu Zhang and Philip Lei and Giovanni Pau},
        title={Crowd Sensing of Weather Conditions and Traffic Congestion Based on Data Mining in Social Networks},
        proceedings={Smart Objects and Technologies for Social Good. Second International Conference, GOODTECHS 2016, Venice, Italy, November 30 -- December 1, 2016, Proceedings},
        proceedings_a={GOODTECHS},
        year={2017},
        month={7},
        keywords={Smart city Social networks Data mining Weather condition Traffic congestion Mediation analysis},
        doi={10.1007/978-3-319-61949-1_37}
    }
    
  • Rita Tse
    Lu Zhang
    Philip Lei
    Giovanni Pau
    Year: 2017
    Crowd Sensing of Weather Conditions and Traffic Congestion Based on Data Mining in Social Networks
    GOODTECHS
    Springer
    DOI: 10.1007/978-3-319-61949-1_37
Rita Tse1,*, Lu Zhang1,*, Philip Lei,*, Giovanni Pau,*
  • 1: Macao Polytechnic Institute
*Contact email: ritatse@ipm.edu.mo, vivianppmonkey@gmail.com, philiplei@ipm.edu.mo, gpau@cs.ucla.edu

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 raw dataset pre-processing, target dataset processing and statistical data analysis, analytic corpus containing tweets related to different weather conditions, traffic congestion and human outdoor activity is selected to test causal relationships by Granger Causality Test. 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.