Evaluation of Transfer Efficiency between Bus and Subway based on Data Envelopment Analysis using Smart Card Data

• Published in: KSCE journal of civil engineering Vol. 23; no. 2; pp. 788 - 799
• Main Authors: Lee, Eun Hak, Lee, Hoyoung, Kho, Seung-Young, Kim, Dong-Kyu
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Civil Engineers 01.02.2019; Springer Nature B.V; 대한토목학회
• Subjects: Analysis; Automatic fare collection; Buses; Civil Engineering; Data; Data analysis; Data envelopment analysis; Data processing; Efficiency; Engineering; Evaluation; Factor analysis; Geotechnical Engineering & Applied Earth Sciences; Industrial Pollution Prevention; Operations research; Population number; Regression analysis; Regression models; Smart cards; Social factors; Socioeconomic factors; Subway stations; Transfer stations; Transit; Transportation Engineering; 토목공학; transfer station; smart card data; public transportation; Data Envelopment Analysis (DEA) model; Tobit regression analysis
• ISSN: 1226-7988; 1976-3808
• DOI: 10.1007/s12205-018-0218-0

The government of Seoul has been operating the automatic fare collection system based on the smart card since 2004. The smart card data in Seoul consist of 15 million instances of individual transit information per day, providing 99% of transit users’ trips. This study provides information about the efficiency of transfer stations in Seoul. The purpose of this study was to estimate the relative efficiency of the transfer stations between bus and subway using smart card data and suggest the improvement strategies for achieving the optimal efficiency. The transfer efficiency was estimated by using the Data Envelopment Analysis (DEA) model, and Tobit regression analysis was conducted to identify the factors that influence transfer efficiency. The DEA model showed that the efficiency scores of 32 major stations were estimated to be 0.557 on average. The transfer efficiency scores of these stations were analyzed to be proportional to the number of transfer trips and the transfer rate of the station. In the external factor analysis, we selected two socioeconomic variables, i.e., population and the number of companies. The external factor analysis indicated that the DEA model produced reasonable results for evaluating transfer efficiency.