An analysis on movement patterns between zones using smart card data in subway networks

Kim, Kwanho; Oh, Kyuhyup; Lee, Yeong Kyu; Kim, Sungho; Jung, Jae‐Yoon

doi:10.1080/13658816.2014.898768

Cited by 37 publications

(38 citation statements)

References 24 publications

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“…Some researchers have considered the travel behavior for a geographical location. Kim et al clustered subway stops and created zones having similar directions of travel [15]. Du et al clustered regions and studied travel patterns between regions regarding direction and destination of travel [16].…”

Section: Related Workmentioning

confidence: 99%

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Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data

Singh

Jung

2017

IJGI

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Analyzing public movement in transportation networks in a city is significant in understanding the life of citizen and making improved city plans for the future. This study focuses on investigating the flow orientation of major activity regions based on smart card transit data. The flow orientation based on the real movements such as transit data can provide the easiest way of understanding public movement in the complicated transportation networks. First, high inflow regions (HIRs) are identified from transit data for morning and evening peak hours. The morning and evening HIRs are used to represent major activity regions for major daytime activities and residential areas, respectively. Second, the directional orientation of flow is then derived through the directional inflow vectors of the HIRs to show the bias in directional orientation and compare flow orientation among major activity regions. Finally, clustering analysis for HIRs is applied to capture the main patterns of flow orientations in the city and visualize the patterns on the map. The proposed methodology was illustrated with smart card transit data of bus and subway transportation networks in Seoul, Korea. Some remarkable patterns in the distribution of movements and orientations were found inside the city. The proposed methodology is useful since it unfolds the complexity and makes it easy to understand the main movement patterns in terms of flow orientation.

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Section: Related Workmentioning

confidence: 99%

“…Travelers for age occupation wise travel behavior [11] k-means 277 consecutive days Travelers for regularity in boarding [12] k-means 277 consecutive days Mining travel patterns [13] DBSCAN 5 consecutive weekdays Origin-destination pairs for discovering zones based on movement patterns [15] Clustering 5 consecutive weekdays…”

Section: Geographical Clusteringmentioning

confidence: 99%

Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data

Singh

Jung

2017

IJGI

Self Cite

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“…In previous studies, zones were identified to meet different goals such as travel pattern recognition [4] and tariff system planning [8]. In our research, a zone is considered a group of adjacent regions that are functionally related based on passenger trips.…”

Section: Od Data and Geographical Datamentioning

confidence: 99%

“…Many smart card-based transit logs are accumulated during this process, and these data can be used to analyze the movement patterns of citizens and identify functional regions in a city [4], [5], [10].…”

Section: Introductionmentioning

confidence: 99%

Urban Zone Discovery from Smart Card-Based Transit Logs

Jung

Heo

2017

IEICE Trans. Inf. & Syst.

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SUMMARYSmart card payment systems provide a convenient billing mechanism for public transportation providers and passengers. In this paper, a smart card-based transit log is used to reveal functionally related regions in a city, which are called zones. To discover significant zones based on the transit log data, two algorithms, minimum spanning trees and agglomerative hierarchical clustering, are extended by considering the additional factors of geographical distance and adjacency. The hierarchical spatial geocoding system, called Geohash, is adopted to merge nearby bus stops to a region before zone discovery. We identify different urban zones that contain functionally interrelated regions based on passenger trip data stored in the smart card-based transit log by manipulating the level of abstraction and the adjustment parameters. key words: smart card-based transit log, zone discovery, public transportation, Geohash

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“…The integration of geographic information system (GIS), Internet, information and communication technology (ICT) generates more and more human related data, i.e., mobile phone data (Sevtsuk and Ratti, 2010;Becker et al, 2013;Cao et al, 2015), vehicle GPS data (Tu et al, 2010;Luo et al, 2015), smart card data (Kim et al, 2014;Tu et al, 2016). Such useful data have both spatial location (longitude and latitude ) and time stamp, which give us new insights on human movements in the city (Yue et al, 2014;Pan et al, 2013;Li and Li, 2014;Li et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Urban Human Movements Inferring From Multi-Source Spatial-Temporal Data

Cao¹,

Tu²,

Cao³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ) level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatialtemporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatialtemporal data.

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An analysis on movement patterns between zones using smart card data in subway networks

Cited by 37 publications

References 24 publications

Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data

Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data

Urban Zone Discovery from Smart Card-Based Transit Logs

Comparison of Urban Human Movements Inferring From Multi-Source Spatial-Temporal Data

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