Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data

2017 ◽  
Vol 18 (11) ◽  
pp. 3135-3146 ◽  
Author(s):  
Juanjuan Zhao ◽  
Qiang Qu ◽  
Fan Zhang ◽  
Chengzhong Xu ◽  
Siyuan Liu
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Yuan Ren ◽  
Jihui Ma ◽  
Jing Li

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.


2020 ◽  
Vol 12 (9) ◽  
pp. 3525
Author(s):  
Zijia Wang ◽  
Hao Tang ◽  
Wenjuan Wang ◽  
Yang Xi

Transit smart card records detail travel information of passengers, which provides abundant data for analyzing public travel patterns. Regular travelers’ information extracted from smart card data (SCD) have been extensively analyzed. However, rare studies have been devoted to non-roundtrips, which account for a relatively large portion of the overall transit ridership, especially in metropolises such as Beijing. This study aimed to reveal the non-roundtrip pattern using the passenger travel data obtained from SCD. Weekly non-roundtrip SCD were used to analyze the spatiotemporal distribution patterns of overall and typical non-roundtrips’ origins and destinations (ODs). Also, subway data and bus data were combined and visualized in geographic information system (GIS). The reasons for frequent non-roundtrips generated in the metropolitan city were inferred. The results demonstrate some detected spatiotemporal patterns of non-roundtrips. It is not surprising that a large proportion of non-roundtrips serve as a rail access to intercity, but there are still many trips of this kind showing a commuting pattern. Merging SCD with bus data, the results also reveal that passengers may choose other modes as a substitute return transportation option due to rail fare or overcrowding problem. This study focused on irregular trips normally neglected in the literature and found that the number of these trips is too large to be ignored in a diversified city like Beijing. Meanwhile, the travel patterns of non-roundtrips extracted can be used to direct the operation strategies for both rail and bus. The research framework raised here could be applied in other cities and comparative analysis could be done in the future.


Author(s):  
Alexis Viallard ◽  
Martin Trépanier ◽  
Catherine Morency

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.


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