Transit smart card data mining for passenger origin information extraction

Xiao-lei MA; Yin-hai WANG; Feng CHEN; Jian-feng LIU

doi:10.1631/jzus.C12a0049

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Transit smart card data mining for passenger origin information extraction

Computer & Automation | Updated：2022-12-09

- Transit smart card data mining for passenger origin information extraction
- Transit smart card data mining for passenger origin information extraction
- Frontiers of Information Technology & Electronic Engineering Vol. 13, Issue 10, Pages: 750-760(2012)
- Affiliations：
  
  1.Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA
  2.Beijing Transportation Research Center, Beijing 100073, China
- Author bio：
  
  [ "Xiao-lei MA, E-mail: xiaolm@uw.edu" ]
  Yin-hai WANG, E-mail: yinhai@uw.edu
- Funds：
- DOI：10.1631/jzus.C12a0049
  CLC： U121;TP391
- Received：23 February 2012，
  
  Revised：2012-;8-20，
  
  Published：2012-10
- Accepted：
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Xiao-lei MA, Yin-hai WANG, Feng CHEN, et al. Transit smart card data mining for passenger origin information extraction[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(10): 750-760.
DOI：

Xiao-lei MA, Yin-hai WANG, Feng CHEN, et al. Transit smart card data mining for passenger origin information extraction[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(10): 750-760. DOI： 10.1631/jzus.C12a0049.

摘要

Abstract

The automated fare collection (AFC) system

also known as the transit smart card (SC) system

has gained more and more popularity among transit agencies worldwide. Compared with the conventional manual fare collection system

an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival. Although it is possible to collect highly valuable data from transit SC transactions

substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection. This is true especially for the Beijing AFC system

where a passenger's boarding stop (origin) on a flat-rate bus is not recorded on the check-in scan. To extract passengers' origin data from recorded SC transaction information

a Markov chain based Bayesian decision tree algorithm is developed in this study. Using the time invariance property of the Markov chain

the algorithm is further optimized and simplified to have a linear computational complexity. This algorithm is verified with transit vehicles equipped with global positioning system (GPS) data loggers. Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers' origin information from SC transactions with a relatively high accuracy. Such transit origin data are highly valuable for transit system planning and route optimization.

关键词

Keywords

references

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