Reproducing Longitudinal In-Vehicle Traveler Experience and the Impact of a Service Reduction with Public Transit Smart Card Data

2016 ◽  
Vol 2541 (1) ◽  
pp. 81-89 ◽  
Author(s):  
Ka Kee Alfred Chu ◽  
André Lomone
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Smart Card Data ◽  
The Impact ◽  
Transit Smart Card

scholarly journals Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

2021 ◽  
Author(s):  
Christian Martin Mützel ◽  
Joachim Scheiner
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Mobility Patterns ◽  
Transit Planning ◽  
Transit Systems ◽  
Passenger Flow ◽  
Smart Card Data ◽  
Depth Analysis ◽  
Spatio Temporal ◽  
The Impact

AbstractModern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous.

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

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

scholarly journals Rationalizing Urban Transportation using Smart Card Data

2019 ◽  
Vol 8 (11S) ◽  
pp. 507-515
Keyword(s):  
Smart Card ◽  
Public Transport ◽  
Public Transportation ◽  
Urban Population ◽  
Transport Systems ◽  
Living Standard ◽  
Transit System ◽  
Advantages And Disadvantages ◽  
Smart Card Data ◽  
The Impact

The urban population in 2014 accounted for 54% of the total global population, up from 34% in 1960, and continues to grow. The global urban population is expected to grow approximately 1.84%, 1.63% and 1.44% between 2015 and 2020, 2020 and 2025, and 2025 and 2030 respectively. This growing population puts pressure on government not only to accommodate the current and potential citizens but also provide them facilities and services for a better living standard. Providing a sustainable growing environment for the citizens is the biggest challenge for the government. As the populations increase, complexity network of transportation, water and sanitation, emergency services, etc. will increase many folds. SMART CITY Mission is being implemented to resolve this issue. As the cities turn smart, so should the transportation facilities. India on June 2018 had only 20 cities with populations of over 500,000 have organized public transport systems, pointing to the large gap to be bridged in their journey to turn smart. The aim of this paper is to examine the impact of smart card data from public transport for improving the predictions and planning of public transport usage and congestions. The mobile apps like M-Indicator, Google Maps don’t interlink, do not have a real time tracking of vehicles, fare distribution, congestion-based route mapping for public transportation. These factors are addressed in the paper with its advantages and disadvantages. This paper also talks about how information from smart card is to be extracted, how Big Data is to be managed and finally come to a smart, sustainable Urban Transit System. This paper also brings into light the data security issues and measures to curb those issues. This paper proposes and emphasizes on a single smart card for all modes of public transport

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