Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering

The metro passenger route choice, influenced by both train schedule and time constraints, is important to metro operation and management. Smart card data (Automatic Fare Collection (AFC) data in metro system) including inbound and outbound swiping time are useful for analysis of the characteristics...

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Main Authors: Wei Li, Qin Luo, Qing Cai, Xiongfei Zhang
Format: Article
Language:English
Published: Wiley 2018-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2018/2710608
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author Wei Li
Qin Luo
Qing Cai
Xiongfei Zhang
author_facet Wei Li
Qin Luo
Qing Cai
Xiongfei Zhang
author_sort Wei Li
collection DOAJ
description The metro passenger route choice, influenced by both train schedule and time constraints, is important to metro operation and management. Smart card data (Automatic Fare Collection (AFC) data in metro system) including inbound and outbound swiping time are useful for analysis of the characteristics of passengers’ route choices in metro while they could not reflect the property of train schedule directly. Train schedule is used in this paper to trim smart card data through removing inbound and outbound walking time to/from platforms and waiting time. Thus, passengers’ pure travel time in accord with trains’ arrival and departure can be obtained. Synchronous clustering (SynC) algorithm is then applied to analyze these processed data to calculate passenger route choice probability. Finally, a case study was conducted to illustrate the effectiveness of the proposed algorithm. Results showed the proposed algorithm works well to analyze metro passenger route choice. It was shown that passenger route choice during both peak period and flat period could be clustered automatically, and noise data are isolated. The probability of route choice calculated through SynC algorithm can be used to revise traditional model results.
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institution Kabale University
issn 0197-6729
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publishDate 2018-01-01
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series Journal of Advanced Transportation
spelling doaj-art-d690f366448e4df58da4ecee7e3accf32025-02-03T05:54:23ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/27106082710608Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous ClusteringWei Li0Qin Luo1Qing Cai2Xiongfei Zhang3Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen, ChinaShenzhen Key Laboratory of Urban Rail Transit, Shenzhen University, Nanshan Ave 3688, Shenzhen, ChinaDepartment of Civil, Environment and Construction Engineering, University of Central Florida, Orlando, Florida 32816, USACollege of Urban Traffic and Logistics, Shenzhen Technology University, Lantian Road 3002, Shenzhen, ChinaThe metro passenger route choice, influenced by both train schedule and time constraints, is important to metro operation and management. Smart card data (Automatic Fare Collection (AFC) data in metro system) including inbound and outbound swiping time are useful for analysis of the characteristics of passengers’ route choices in metro while they could not reflect the property of train schedule directly. Train schedule is used in this paper to trim smart card data through removing inbound and outbound walking time to/from platforms and waiting time. Thus, passengers’ pure travel time in accord with trains’ arrival and departure can be obtained. Synchronous clustering (SynC) algorithm is then applied to analyze these processed data to calculate passenger route choice probability. Finally, a case study was conducted to illustrate the effectiveness of the proposed algorithm. Results showed the proposed algorithm works well to analyze metro passenger route choice. It was shown that passenger route choice during both peak period and flat period could be clustered automatically, and noise data are isolated. The probability of route choice calculated through SynC algorithm can be used to revise traditional model results.http://dx.doi.org/10.1155/2018/2710608
spellingShingle Wei Li
Qin Luo
Qing Cai
Xiongfei Zhang
Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering
Journal of Advanced Transportation
title Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering
title_full Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering
title_fullStr Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering
title_full_unstemmed Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering
title_short Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering
title_sort using smart card data trimmed by train schedule to analyze metro passenger route choice with synchronous clustering
url http://dx.doi.org/10.1155/2018/2710608
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