Behavioural data mining of transit smart card data: A data fusion approach

The aim of this study is to develop a fast data fusion method for recognizing metro-to-bus transfer trips based on combined data from smart cards and a GPS system. The method is intended to establish station- and time-specific elapsed time thresholds for overcoming the limitations of one-size-fits-all criterion which is not sufficiently convincing for different transfer pairs and personal characteristics. Firstly, a data fusion method with bus smart card data and GPS data is proposed to supplement absent bus boarding information in the smart card data. Then, a model for identifying metro-to-bus interchange trips is derived based on two rules about maximal allowable transfer distance and elapsed transfer time threshold. Finally, in tests that used half-monthly field smart card data and GPS data from Shenzhen, China, the results recognized by the proposed method were more consistent with the actual surveyed group transfer time with a P value of 0.17 determined by Mann–Whitney U test. The comparison analysis showed that the proposed method can be widely applied to successfully identify and interpret metro-to-bus interchange behavior beyond a static transfer time threshold of 30 min.

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You are how you travel: A multi-task learning framework for Geodemographic inference using transit smart card data

Computers Environment and Urban Systems ◽

10.1016/j.compenvurbsys.2020.101517 ◽

2020 ◽

Vol 83 ◽

pp. 101517 ◽

Cited By ~ 1

Author(s):

Yang Zhang ◽

Nilufer Sari Aslam ◽

Juntao Lai ◽

Tao Cheng

Keyword(s):

Smart Card ◽

Learning Framework ◽

Task Learning ◽

Smart Card Data ◽

Transit Smart Card

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Understanding commuting patterns using transit smart card data

Journal of Transport Geography ◽

10.1016/j.jtrangeo.2016.12.001 ◽

2017 ◽

Vol 58 ◽

pp. 135-145 ◽

Cited By ~ 145

Author(s):

Xiaolei Ma ◽

Congcong Liu ◽

Huimin Wen ◽

Yunpeng Wang ◽

Yao-Jan Wu

Keyword(s):

Smart Card ◽

Smart Card Data ◽

Commuting Patterns ◽

Transit Smart Card

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Analysis of Passenger Transfer Patterns Based on Transit Smart Card Data in Seoul

2013 Joint Rail Conference ◽

10.1115/jrc2013-2496 ◽

2013 ◽

Author(s):

Ji-Young Song ◽

Jin-Ki Eom

Keyword(s):

Smart Card ◽

Regional Level ◽

Long Distance ◽

Transit Service ◽

Smart Card Data ◽

Average Travel Time ◽

Time Distance ◽

Transit Connectivity ◽

The Relationship ◽

Transit Smart Card

This study analyzes the transfer patterns of passengers in Seoul based on transit smart card data that was observed in 2010. The smart card recorded maximum four times of transfer and reported that approximately 90% of trips were less than one transfer and the remains were more than 2 transfers. We focus on trips with more than 3 transfers to figure out the relationship between transit service and regional connectivity. The results show that the average travel time, distance, fare are 45 minutes, 18.3km, and 1,119(KW) respectively. We develop a map for investigating transfer patterns at a regional level (dong and gu). By doing this, three types of transfers are observed as: 1) trips of which origin and destination is either same or near, 2) trips with middle distance (shorter then 6km), and 3) long distance (from 6km to 12km) trip with low transit connectivity.

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