Transit smart card data mining for passenger origin information extraction

作     者：Xiao-lei MA Yin-hai WANG Feng CHEN Jian-feng LIU 

作者机构：Department of Civil and Environmental Engineering University of Washington Seattle WA 98195-2700 USA Beijing Transportation Research Center Beijing 100073 China 

出 版 物：《Journal of Zhejiang University-Science C(Computers and Electronics)》 (浙江大学学报C辑（计算机与电子（英文版）)

年 卷 期：2012年第13卷第10期

页      面：750-760页

核心收录：

学科分类：08[工学] 081203[工学-计算机应用技术] 0835[工学-软件工程] 0823[工学-交通运输工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project supported by the National Natural Science Foundation of China (No. 51138003) the Beijing Transportation Research Center (BTRC),China 

主　　题：Transit smart card Automated fare collection(AFC) Bayesian decision tree Markov chain Origin inference 

摘      要：The automated fare collection(AFC) system,also known as the transit smart card(SC) system,has gained more and more popularity among transit agencies *** 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 *** 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 *** 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 *** extract passengers origin data from recorded SC transaction information,a Markov chain based Bayesian decision tree algorithm is developed in this *** the time invariance property of the Markov chain,the algorithm is further optimized and simplified to have a linear computational *** algorithm is verified with transit vehicles equipped with global positioning system(GPS) data *** verification results demonstrated that the proposed algorithm is effective in extracting transit passengers origin information from SC transactions with a relatively high *** transit origin data are highly valuable for transit system planning and route optimization.