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Open AccessReview

Smart Card Data Mining of Public Transport Destination: A Literature Review

by 1,2, 1,2,3,*, 1 and 4
1
School of Automobile and Traffic Engineering, Jiangsu University, Jiangsu 212013, China
2
School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 264209, China
3
College of Engineering, Texas A&M University, Kingsville, TX 77843-0100, USA
4
School of Vehicle Engineering, Xi’an Aeronautical University, Xi’an 710077, China
*
Author to whom correspondence should be addressed.
Information 2018, 9(1), 18; https://doi.org/10.3390/info9010018
Received: 30 November 2017 / Revised: 5 January 2018 / Accepted: 10 January 2018 / Published: 13 January 2018
Smart card data is increasingly used to investigate passenger behavior and the demand characteristics of public transport. The destination estimation of public transport is one of the major concerns for the implementation of smart card data. In recent years, numerous studies concerning destination estimation have been carried out—most automatic fare collection (AFC) systems only record boarding information but not passenger alighting information. This study provides a comprehensive review of the practice of using smart card data for destination estimation. The results show that the land use factor is not discussed in more than three quarters of papers and sensitivity analysis is not applied in two thirds of papers. In addition, the results are not validated in half the relevant studies. In the future, more research should be done to improve the current model, such as considering additional factors or making sensitivity analysis of parameters as well as validating the results with multi-source data and new methods. View Full-Text
Keywords: destination estimation; smart card; quality assessment; destination estimation model destination estimation; smart card; quality assessment; destination estimation model
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Li, T.; Sun, D.; Jing, P.; Yang, K. Smart Card Data Mining of Public Transport Destination: A Literature Review. Information 2018, 9, 18.

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