Multi-View Interactive Visual Exploration of Individual Association for Public Transportation Passengers

More and more people in mega cities are choosing to travel by public transportation due to its convenience and punctuality. It is widely acknowledged that there may be some potential associations between passengers. Their travel behavior may be working together, shopping together, or even some abnor...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2020-01, Vol.10 (2), p.628
Main Authors: Lv, Di, Zhang, Yong, Lin, Jiongbin, Wan, Peiyuan, Hu, Yongli
Format: Article
Language:English
Subjects:
associated passenger
Behavior
Data analysis
ic card data
interactive visualisation
Passenger services
Passengers
Public safety
Public transportation
Smart cards
Social networks
Three dimensional motion
Traffic accidents & safety
Traffic congestion
Travel
Travel time
visual analytics
Visualization
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:More and more people in mega cities are choosing to travel by public transportation due to its convenience and punctuality. It is widely acknowledged that there may be some potential associations between passengers. Their travel behavior may be working together, shopping together, or even some abnormal behaviors, such as stealing or begging. Thus, analyzing association between passengers is very important for management departments. It is very helpful to make operational plans, provide better services to passengers and ensure public transport safety. In order to quickly explore the association between passengers, we propose a multi-view interactive exploration method that provides five interactive views: passenger 3D travel trajectory view, passenger travel time pixel matrix view, passenger origin-destination chord view, passenger travel vehicle bubble chart view and passenger 2D travel trajectory view. It can explore the associated passengers from multiple aspects such as travel trajectory, travel area, travel time, and vehicles used for travel. Using Beijing public transportation data, the experimental results verified that our method can effectively explore the association between passengers and deduce the relationship.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10020628