Machine identification method of subway service quality based on smart card data

Abstract

Abstract A new automatic evaluation method of subway service quality based on metro smart card data is proposed suitable for three different levels: station pair, railway line and subway network, which has merits of overcoming the previous lagging and subjective evaluation in the system of ‘questionnaire survey plus evaluation method’. First, passengers' travel time distribution for different operating periods in station OD pairs are introduced initially for service evaluation purposes and are classified into different groups in order to infer the station's operating characteristics at the different periods. Second, the classification is verified by K-means cluster analysis and K-S tests. Third, the service quality weight indicator is proposed to identify the service quality of the entire metro network from the dual perspectives of passengers and companies. Finally, the feasibility and rationality of the proposed method are verified by Shenzhen metro smart card data as an example. The new automated evaluation method of subway service quality is suitable for online and offline application. Highlights • A new machine identification method based on travel time distribution is proposed for estimating the service quality. • The proposed model employs the K-means clustering method to distinguish passenger's travel time distribution into different modes. • Characteristics of the different modes of passenger travel time distribution reflect different service levels of the urban subway system. • The method provides automatically identification of service levels for urban subway operations.

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