Spatial-Temporal Travel Behavior Analysis of Transit Passengers Based on Massive Smart Card Data and GPS Data

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Yuqian Li ◽  
Xiaoyu He
Keyword(s):  
Behavior Analysis ◽  
Smart Card ◽  
Travel Behavior ◽  
Gps Data ◽  
Smart Card Data

Travel behavior analysis using smart card data

2015 ◽  
Vol 20 (4) ◽  
pp. 1532-1539 ◽  
Author(s):  
Atizaz Ali ◽  
Jooyoung Kim ◽  
Seungjae Lee
Keyword(s):  
Behavior Analysis ◽  
Smart Card ◽  
Travel Behavior ◽  
Smart Card Data

scholarly journals Citywide Metro-to-Bus Transfer Behavior Identification Based on Combined Data from Smart Cards and GPS

2019 ◽  
Vol 9 (17) ◽  
pp. 3597 ◽  
Author(s):  
Zilin Huang ◽  
Lunhui Xu ◽  
Yongjie Lin ◽  
Pan Wu ◽  
Bin Feng
Keyword(s):  
Data Fusion ◽  
Smart Card ◽  
Smart Cards ◽  
P Value ◽  
Gps Data ◽  
Fusion Method ◽  
Transfer Time ◽  
Time Threshold ◽  
Smart Card Data ◽  
Combined Data

The aim of this study is to develop a fast data fusion method for recognizing metro-to-bus transfer trips based on combined data from smart cards and a GPS system. The method is intended to establish station- and time-specific elapsed time thresholds for overcoming the limitations of one-size-fits-all criterion which is not sufficiently convincing for different transfer pairs and personal characteristics. Firstly, a data fusion method with bus smart card data and GPS data is proposed to supplement absent bus boarding information in the smart card data. Then, a model for identifying metro-to-bus interchange trips is derived based on two rules about maximal allowable transfer distance and elapsed transfer time threshold. Finally, in tests that used half-monthly field smart card data and GPS data from Shenzhen, China, the results recognized by the proposed method were more consistent with the actual surveyed group transfer time with a P value of 0.17 determined by Mann–Whitney U test. The comparison analysis showed that the proposed method can be widely applied to successfully identify and interpret metro-to-bus interchange behavior beyond a static transfer time threshold of 30 min.

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 Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data

2020 ◽  
Vol 12 (12) ◽  
pp. 5010
Author(s):  
Pengfei Lin ◽  
Jiancheng Weng ◽  
Dimitrios Alivanistos ◽  
Siyong Ma ◽  
Baocai Yin
Keyword(s):  
Survey Data ◽  
Smart Card ◽  
Public Transport ◽  
Travel Behavior ◽  
Transport Systems ◽  
Behavior Patterns ◽  
Light Gradient ◽  
Smart Card Data ◽  
Departure Time ◽  
Commuting Patterns

Understanding commuting patterns could provide effective support for the planning and operation of public transport systems. One-month smart card data and travel behavior survey data in Beijing were integrated to complement the socioeconomic attributes of cardholders. The light gradient boosting machine (LightGBM) was introduced to identify the commuting patterns considering the spatiotemporal regularity of travel behavior. Commuters were further divided into fine-grained clusters according to their departure time using the latent Dirichlet allocation model. To enhance the interpretation of the behavior patterns in each cluster, we investigated the relationship between the socioeconomic characteristics of the residence locations and commuter cluster distributions. Approximately 3.1 million cardholders were identified as commuters, accounting for 67.39% of daily passenger volume. Their commuting routes indicated the existence of job–house imbalance and excess commuting in Beijing. We further segmented commuters into six clusters with different temporal patterns, including two-peak, staggered shifts, flexible departure time, and single-peak. The residences of commuters are mainly concentrated in the low housing price and high or medium population density areas; subway facilities will promote people to commute using public transport. This study will help stakeholders optimize the public transport networks, scheduling scheme, and policy accordingly, thus ameliorating commuting within cities.

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.

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