Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city

2021 ◽  
Vol 96 ◽  
pp. 103203
Author(s):  
Caio Pieroni ◽  
Mariana Giannotti ◽  
Bianca B. Alves ◽  
Renato Arbex
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Smart Card ◽  
Low Income ◽  
Global South ◽  
Population Based ◽  
Travel Patterns ◽  
Low Income Population ◽  
Smart Card Data

Assessing the Evolution of Transit User Behavior from Smart Card Data

2019 ◽  
Vol 2673 (4) ◽  
pp. 184-194 ◽  
Author(s):  
Alexis Viallard ◽  
Martin Trépanier ◽  
Catherine Morency
Keyword(s):  
Smart Card ◽  
User Behavior ◽  
Demand Forecasting ◽  
Computation Time ◽  
Experimental Methodology ◽  
Travel Patterns ◽  
Forecasting Models ◽  
Smart Card Data ◽  
Clustering Quality ◽  
Similar Computation

There is a huge potential for exploiting information centered on individual transit users’ behavior through longitudinal smart card data. This is particularly true for cities like Gatineau, Canada, where the bus system serves passengers with different travel patterns. Understanding the evolution of these patterns marks an important point in improving transit demand forecasting models. Indeed, better models can help transit planners to create optimized networks. This paper proposes a comparison of a traditional and an experimental methodology aiming to identify the evolution of travel structure among transit users. These methodologies are based on the clustering of multi-week travel patterns derived from a large sample of smart card transactions (35.4 million). Representing users’ behavior, these patterns are constructed using the number of trips made by every card on each day of a week. Behavior vectors are defined by seven components (one for each day) and are clustered using a K-means algorithm. The experimental week-to-week method consists in clustering the population on each week, while using the clustering results from the previous week as seed. This latter approach makes it possible to observe the evolution of users’ behaviors and also has a better clustering quality in a similar computation time than the traditional method.

Transit smart card data mining for passenger origin information extraction

2012 ◽  
Vol 13 (10) ◽  
pp. 750-760 ◽  
Author(s):  
Xiao-lei Ma ◽  
Yin-hai Wang ◽  
Feng Chen ◽  
Jian-feng Liu
Keyword(s):  
Data Mining ◽  
Information Extraction ◽  
Smart Card ◽  
Smart Card Data ◽  
Transit Smart Card

scholarly journals Passenger Travel Regularity Analysis Based on a Large Scale Smart Card Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Yuan Ren ◽  
Jihui Ma ◽  
Jing Li
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Large Scale ◽  
Spatial Clustering ◽  
Reconstruction Algorithm ◽  
Travel Patterns ◽  
Node Importance ◽  
The People ◽  
Passenger Travel ◽  
Smart Card Data

Analysis of passenger travel habits is always an important item in traffic field. However, passenger travel patterns can only be watched through a period time, and a lot of people travel by public transportation in big cities like Beijing daily, which leads to large-scale data and difficult operation. Using SPARK platform, this paper proposes a trip reconstruction algorithm and adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel patterns of each Smart Card (SC) user in Beijing. For the phenomenon that passengers swipe cards before arriving to avoid the crowd caused by the people of the same destination, the algorithm based on passenger travel frequent items is adopted to guarantee the accuracy of spatial regular patterns. At last, this paper puts forward a model based on density and node importance to gather bus stations. The transportation connection between areas formed by these bus stations can be seen with the help of SC data. We hope that this research will contribute to further studies.

scholarly journals Smart Card Data Mining of Public Transport Destination: A Literature Review

Information ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 18 ◽  
Author(s):  
Tian Li ◽  
Dazhi Sun ◽  
Peng Jing ◽  
Kaixi Yang
Keyword(s):  
Data Mining ◽  
Literature Review ◽  
Smart Card ◽  
Public Transport ◽  
Smart Card Data
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