Unraveling Latent Transfer Patterns Between Metro and Bus From Large-Scale Smart Card Data

2020 ◽  
pp. 1-15
Author(s):  
Enhui Chen ◽  
Wenbo Zhang ◽  
Zhirui Ye ◽  
Min Yang
Keyword(s):  
Smart Card ◽  
Large Scale ◽  
Smart Card Data

Application of smart card data in validating a large-scale multi-modal transit assignment model

2017 ◽  
Vol 10 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Ahmad Tavassoli ◽  
Mahmoud Mesbah ◽  
Mark Hickman
Keyword(s):  
Smart Card ◽  
Large Scale ◽  
Transit Assignment ◽  
Assignment Model ◽  
Smart Card Data

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 ◽  
...  
Keyword(s):  
Smart Card ◽  
Large Scale ◽  
Pattern Analysis ◽  
Travel Pattern ◽  
Smart Card Data

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.

Investigating physical encounters of individuals in urban metro systems with large-scale smart card data

2020 ◽  
Vol 545 ◽  
pp. 123398 ◽  
Author(s):  
Kang Liu ◽  
Ling Yin ◽  
Zhanwu Ma ◽  
Fan Zhang ◽  
Juanjuan Zhao
Keyword(s):  
Smart Card ◽  
Large Scale ◽  
Smart Card Data ◽  
Metro Systems

Algorithm of Estimating Alighting Bus Stops of Smart Card Passengers Based on Trip-Chain

2012 ◽  
Vol 253-255 ◽  
pp. 1918-1921
Author(s):  
Jun Chen ◽  
Zhao Fei Wang
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Large Scale ◽  
Estimation Algorithm ◽  
Transportation Systems ◽  
Trip Chain ◽  
Smart Card Data ◽  
Bus Stops ◽  
Public Transportation Planning ◽  
Public Transportation Systems

In order to apply smart card data in decision-making of public transportation planning and management, the paper researched estimating method of alighting bus stops of smart card passengers. Based on Trip-chain thought, the paper presented estimation algorithm applying the three hypotheses of “Next Trip”, “Last Trip” and “Return Trip”. Then, the algorithm was tested and analyzed using large-scale actual data of Advanced Public Transportation Systems of Nanning City in China. The results show that Trip-chain Method can estimate majority of alighting bus stops.

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