Inferring temporal motifs for travel pattern analysis using large scale smart card data

2020 ◽  
Vol 120 ◽  
pp. 102810
Author(s):  
Da Lei ◽  
Xuewu Chen ◽  
Long Cheng ◽  
Lin Zhang ◽  
Satish V. Ukkusuri ◽  
...  
2017 ◽  
Vol 10 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Ahmad Tavassoli ◽  
Mahmoud Mesbah ◽  
Mark Hickman

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 545 ◽  
pp. 123398 ◽  
Author(s):  
Kang Liu ◽  
Ling Yin ◽  
Zhanwu Ma ◽  
Fan Zhang ◽  
Juanjuan Zhao

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