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.

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.

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 Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

2019 ◽  
Vol 8 (10) ◽  
pp. 434 ◽  
Author(s):  
Tong Zhang ◽  
Jianlong Wang ◽  
Chenrong Cui ◽  
Yicong Li ◽  
Wei He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Card ◽  
Travel Behavior ◽  
Public Transit ◽  
Travel Patterns ◽  
Mobility Patterns ◽  
Learning Methods ◽  
Transit Network ◽  
Machine Learning Methods ◽  
Smart Card Data

Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.

Mining Combined Smart Card Data and Survey Data for Transit Users’ Travel Patterns and Demands

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Jia Yuan ◽  
Dongyuan Yang ◽  
Jian Li ◽  
Shichao Sun ◽  
Weifeng Li
Keyword(s):  
Survey Data ◽  
Smart Card ◽  
Travel Patterns ◽  
Smart Card Data

Mining smart card data for transit riders’ travel patterns

2013 ◽  
Vol 36 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaolei Ma ◽  
Yao-Jan Wu ◽  
Yinhai Wang ◽  
Feng Chen ◽  
Jianfeng Liu
Keyword(s):  
Smart Card ◽  
Travel Patterns ◽  
Smart Card Data
Sign in / Sign up

Export Citation Format

Share Document