Understanding temporal and spatial travel patterns of individual passengers by mining smart card data

2014 ◽  
Author(s):  
Juanjuan Zhao ◽  
Chen Tian ◽  
Fan Zhang ◽  
Chengzhong Xu ◽  
Shengzhong Feng
Keyword(s):  
Smart Card ◽  
Travel Patterns ◽  
Smart Card Data ◽  
Temporal And Spatial

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.

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

scholarly journals An Analysis of Time-Varying Crowding on Subway Platforms using AFC Data in Seoul Metropolitan Subway Network

2020 ◽  
Author(s):  
Seongil Shin Shin ◽  
Sangjun Lee
Keyword(s):  
Big Data ◽  
Real Time ◽  
Smart Card ◽  
Accurate Estimation ◽  
Time Varying ◽  
Spatial Constraints ◽  
Passenger Flow ◽  
Passenger Safety ◽  
Smart Card Data ◽  
Temporal And Spatial

Management of crowding at subway platform is essential to improving services, preventing train delays and ensuring passenger safety. Establishing effective measures to mitigate crowding at platform requires accurate estimation of actual crowding levels. At present, there are temporal and spatial constraints since subway platform crowding is assessed only at certain locations, done every 1~2 years, and counting is performed manually Notwithstanding, data from smart cards is considered real-time big data that is generated 24 hours a day and thus, deemed appropriate basic data for estimating crowding. This study proposes the use of smart card data in creating a model that dynamically estimates crowding. It first defines crowding as demand, which can be translated into passengers dynamically moving along a subway network. In line with this, our model also identifies the travel trajectory of individual passengers, and is able to calculate passenger flow, which concentrates and disperses at the platform, every minute. Lastly, the level of platform crowding is estimated in a way that considers the effective waiting area of each platform structure.

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