Behavioural data mining of transit smart card data: A data fusion approach
technology - passenger information, planning - surveys, ridership - behaviour
Smart card data, Data fusion, Data mining, Behavioural analysis, Naïve Bayes classifier
The aim of this study is to develop a data fusion methodology for estimating behavioural attributes of trips using smart card data to observe continuous long-term changes in the attributes of trips. The method is intended to enhance understanding of travellers’ behaviour during monitoring the smart card data. In order to supplement absent behavioural attributes in the smart card data, this study developed a data fusion methodology of smart card data with the person trip survey data with the naïve Bayes probabilistic model. A model for estimating the trip purpose is derived from the person trip survey data. By using the model, trip purposes are estimated as supplementary behavioural attributes of the trips observed in the smart card data. The validation analysis showed that the proposed method successfully estimated the trip purposes in 86.2% of the validation data. The empirical data mining analysis showed that the proposed methodology can be applied to find and interpret the behavioural features observed in the smart card data which had been difficult to obtain from each independent dataset.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Kusakabe, T. & Asakura, Y. (2014). Behavioural data mining of transit smart card data: A data fusion approach. Transportation Research Part C: Emerging Technologies, Vol. 46, pp. 179–191.