Analysis of public transit service performance using transit smart card data in Seoul

2015 ◽  
Vol 19 (5) ◽  
pp. 1530-1537 ◽  
Author(s):  
Jin Ki Eom ◽  
Ji Young Song ◽  
Dae-Seop Moon
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Service Performance ◽  
Transit Service ◽  
Smart Card Data ◽  
Transit Service Performance ◽  
Transit Smart Card

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

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.

Modeling Relation Proximity of Passengers Using Public Transit Smart Card Data

2020 ◽  
pp. 0-0
Author(s):  
Xia Zhao ◽  
Yong Zhang ◽  
Yongli Hu ◽  
Zhen Sean Qian ◽  
Hao Liu ◽  
...  
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Smart Card Data ◽  
Modeling Relation ◽  
Transit Smart Card

scholarly journals Methodology for Extracting Potential Customized Bus Routes Based on Bus Smart Card Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2224 ◽  
Author(s):  
Jing Li ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Qi Ouyang
Keyword(s):  
Smart Card ◽  
Traffic Congestion ◽  
Spatial Clustering ◽  
Extraction Process ◽  
Methodological Framework ◽  
Transit Service ◽  
Dbscan Algorithm ◽  
Smart Card Data ◽  
Division Process

To alleviate traffic congestion and traffic-related environmental pollution caused by the increasing numbers of private cars, public transport (PT) is highly recommended to travelers. However, there is an obvious contradiction between the diversification of travel demands and simplification of PT service. Customized bus (CB), as an innovative supplementary mode of PT service, aims to provide demand-responsive and direct transit service to travelers with similar travel demands. But how to obtain accurate travel demands? It is passive and limited to conducting online surveys, additionally inefficient and costly to investigate all the origin-destinations (ODs) aimlessly. This paper proposes a methodological framework of extracting potential CB routes from bus smart card data to provide references for CB planners to conduct purposeful and effective investigations. The framework consists of three processes: trip reconstruction, OD area division and CB route extraction. In the OD area division process, a novel two-step division model is built to divide bus stops into different areas. In the CB route extraction process, two spatial-temporal clustering procedures and one length constraint are implemented to cluster similar trips together. An improved density-based spatial clustering of application with noise (DBSCAN) algorithm is used to complete these procedures. In addition, a case study in Beijing is conducted to demonstrate the effectiveness of the proposed methodological framework and the resulting analysis provides useful references to CB planners in Beijing.

Analyzing Transit User Behavior with 51 Weeks of Smart Card Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 33-45 ◽  
Author(s):  
Elodie Deschaintres ◽  
Catherine Morency ◽  
Martin Trépanier
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Travel Behavior ◽  
Travel Demand ◽  
Public Transit ◽  
User Behavior ◽  
Individual Level ◽  
Smart Card Data ◽  
Demand Models ◽  
The Individual

A better understanding of mobility behaviors is relevant to many applications in public transportation, from more accurate travel demand models to improved supply adjustment, customized services and integrated pricing. In line with this context, this study mined 51 weeks of smart card (SC) data from Montréal, Canada to analyze interpersonal and intrapersonal variability in the weekly use of public transit. Passengers who used only one type of product (AP − annual pass, MP − monthly pass, or TB − ticket book) over 12 months were selected, amounting to some 200,000 cards. Data was first preprocessed and summarized into card-week vectors to generate a typology of weeks. The most popular weekly patterns were identified for each type of product and further studied at the individual level. Sequences of week clusters were constructed to represent the weekly travel behavior of each user over 51 weeks. They were then segmented by type of product according to an original distance, therefore highlighting the heterogeneity between passengers. Two indicators were also proposed to quantify intrapersonal regularity as the repetition of weekly clusters throughout the weeks. The results revealed MP owners have a more regular and diversified use of public transit. AP users are mainly commuters whereas TB users tend to be more occasional transit users. However, some atypical groups were found for each type of product, for instance users with 4-day work weeks and loyal TB users.

Understanding commuting patterns using transit smart card data

2017 ◽  
Vol 58 ◽  
pp. 135-145 ◽  
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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