Spatial and Temporal Characteristics of Passenger Travel Modes: A Study Based on Shanghai Smart Card Data

2021 ◽  
pp. 233-242
Author(s):  
Huanxi Xu ◽  
Shaozhi Hong
Keyword(s):  
Smart Card ◽  
Temporal Characteristics ◽  
Passenger Travel ◽  
Smart Card Data ◽  
Spatial And Temporal Characteristics

Exploring Temporal Characteristics of Bus Trips in Tian’anmen Area Using Smart Card Data

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Ying Xiong ◽  
Guangquan Lu ◽  
Xiaolei Ma ◽  
Haode Liu ◽  
Xianglong Liu
Keyword(s):  
Smart Card ◽  
Temporal Characteristics ◽  
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.

scholarly journals Optimized Skip-Stop Metro Line Operation Using Smart Card Data

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Peitong Zhang ◽  
Zhanbo Sun ◽  
Xiaobo Liu
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Low Cost ◽  
Optimal Solution ◽  
Cost Savings ◽  
Passenger Demand ◽  
Passenger Travel ◽  
Smart Card Data ◽  
Transit Agencies ◽  
The Right

Skip-stop operation is a low cost approach to improving the efficiency of metro operation and passenger travel experience. This paper proposes a novel method to optimize the skip-stop scheme for bidirectional metro lines so that the average passenger travel time can be minimized. Different from the conventional “A/B” scheme, the proposed Flexible Skip-Stop Scheme (FSSS) can better accommodate spatially and temporally varied passenger demand. A genetic algorithm (GA) based approach is then developed to efficiently search for the optimal solution. A case study is conducted based on a real world bidirectional metro line in Shenzhen, China, using the time-dependent passenger demand extracted from smart card data. It is found that the optimized skip-stop operation is able to reduce the average passenger travel time and transit agencies may benefit from this scheme due to energy and operational cost savings. Analyses are made to evaluate the effects of that fact that certain number of passengers fail to board the right train (due to skip operation). Results show that FSSS always outperforms the all-stop scheme even when most passengers of the skipped OD pairs are confused and cannot get on the right train.

Variability of passenger travel patterns observed using smart card data in Japan

2021 ◽  
pp. 507-514
Author(s):  
Toshiyuki Yamamoto ◽  
Shasha Liu ◽  
Toshiyuki Nakamura
Keyword(s):  
Smart Card ◽  
Travel Patterns ◽  
Passenger Travel ◽  
Smart Card Data

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
Keyword(s):  
Smart Card ◽  
Temporal Analysis ◽  
Travel Patterns ◽  
Passenger Travel ◽  
Smart Card Data ◽  
Spatio Temporal

scholarly journals The Pattern of Non-Roundtrip Travel on Urban Rail and Its Application in Transit Improvement

2020 ◽  
Vol 12 (9) ◽  
pp. 3525
Author(s):  
Zijia Wang ◽  
Hao Tang ◽  
Wenjuan Wang ◽  
Yang Xi
Keyword(s):  
Smart Card ◽  
Distribution Patterns ◽  
Travel Patterns ◽  
Travel Information ◽  
Transit Ridership ◽  
Passenger Travel ◽  
Smart Card Data ◽  
Urban Rail ◽  
In Transit ◽  
Abundant Data

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.

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