Discovering Functional Zones Using Bus Smart Card Data and Points of Interest in Beijing

Long, Ying; Shen, Zhenjiang

doi:10.1007/978-3-319-19342-7_10

Cited by 55 publications

(43 citation statements)

References 20 publications

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“…To effectively link POI data with urban functions, many scholars have classified POIs according to their contribution to urban functions [32,33,35]. Consistent with these studies, we also grouped the collected POI data into a few categories from the perspective of urban functions.…”

Section: Data Classification Layermentioning

confidence: 90%

“…Due to this benefit, POI data have been widely used to analyze the spatial distributions of urban functions in previous studies [32][33][34]. Compared with statistical data (e.g., population and housing prices), large volumes of POI data can be easily achieved in real time [35], which enables researchers to accurately capture the dynamics of urban function evolution.…”

Section: Points-of-interest (Pois) and Their Links With Urban Functionsmentioning

confidence: 99%

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Rethinking the Identification of Urban Centers from the Perspective of Function Distribution: A Framework Based on Point-of-Interest Data

et al. 2020

Sustainability

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Urban spatial structure has a significant impact on the sustainable development of cities. An important step of urban spatial structure analysis is the identification of urban centers. From the perspective of urban function distribution, this study developed a theoretical framework of three layers for urban center identification. In the first layer, point-of-interest data were collected from geospatial databases and utilized to capture the spatial distribution of urban functions. In the second layer, the density-based spatial clustering of application with noise (DBSCAN) algorithm was employed to group points of interest into urban centers according to their inter-distances and urban functions. In the third layer, the spatial distribution of the identified urban centers was visualized by the ArcGIS platform. This framework was applied in the urban center analysis of Beijing. The results showed that Beijing is in the process of transitioning from monocentric to polycentric with urban functions distributed unevenly throughout the city. To facilitate this transition, strategies such as the construction of super-large communities, the development of branch campuses and higher-education parks, and the enhancement of modern culture industries should be considered for the 11 new towns of Beijing.

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Section: Data Classification Layermentioning

confidence: 90%

Section: Points-of-interest (Pois) and Their Links With Urban Functionsmentioning

confidence: 99%

Rethinking the Identification of Urban Centers from the Perspective of Function Distribution: A Framework Based on Point-of-Interest Data

et al. 2020

Sustainability

View full text Add to dashboard Cite

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“…Recent studies have analyzed movement patterns of people from one area to another using smart card data and have characterized the areas or have enabled segmentation of the areas [9] [19]. These studies solely assume that an area falls into some pre-defined demographics based on people flow in the area.…”

Section: Modeling Characteristics Of Geographical Areas Using Mobilitmentioning

confidence: 99%

Geospatial Area Embedding Based on the Movement Purpose Hypothesis Using Large-Scale Mobility Data from Smart Card

Ochi¹,

Nakashio²,

Ruttley³

et al. 2016

IJCNS

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With the deployment of modern infrastructure for public transportation, several studies have analyzed movement patterns of people using smart card data and have characterized different areas. In this paper, we propose the "movement purpose hypothesis" that each movement occurs from two causes: where the person is and what the person wants to do at a given moment. We formulate this hypothesis to a synthesis model in which two network graphs generate a movement network graph. Then we develop two novel-embedding models to assess the hypothesis, and demonstrate that the models obtain a vector representation of a geospatial area using movement patterns of people from large-scale smart card data. We conducted an experiment using smart card data for a large network of railroads in the Kansai region of Japan. We obtained a vector representation of each railroad station and each purpose using the developed embedding models. Results show that network embedding methods are suitable for a large-scale movement of data, and the developed models perform better than existing embedding methods in the task of multi-label classification for train stations on the purpose of use data set. Our proposed models can contribute to the prediction of people flows by discovering underlying representations of geospatial areas from mobility data.

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“…These systems could collect residents' mobility data every day, including longitude, latitude, boarding time, and dropping off time [14]. In the last decade, various researches based on these data have been carried out, for example, mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data [14], comparing accessibility in urban slums using smart card and bus GPS data [15,16], discovering functional zones using bus smart card data [17], and partitioning bus operating hours into time of day intervals based on bus GPS data [18], which makes the data based transportation research to be a hot spot of transportation field [19].…”

Section: Introductionmentioning

confidence: 99%

Discovering Public Transit Riders’ Travel Pattern from GPS Data: A Case Study in Harbin

Wang

Yang

et al. 2017

Journal of Sensors

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This paper proposes a public transit riders' travel pattern measuring method based on divided cells and public transit vehicle's GPS data. The method consists of two parts: detecting urban origin and destination areas and measuring the public transit riders' travel pattern. Moreover, a series of indicators are proposed to reflect the public transit riders' travel pattern. A case study is carried out to evaluate the methods, which use the GPS data collected from taxis and buses in Harbin, China. The study is expected to provide a better understanding of public transit riders' travel patterns.

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Discovering Functional Zones Using Bus Smart Card Data and Points of Interest in Beijing

Cited by 55 publications

References 20 publications

Rethinking the Identification of Urban Centers from the Perspective of Function Distribution: A Framework Based on Point-of-Interest Data

Rethinking the Identification of Urban Centers from the Perspective of Function Distribution: A Framework Based on Point-of-Interest Data

Geospatial Area Embedding Based on the Movement Purpose Hypothesis Using Large-Scale Mobility Data from Smart Card

Discovering Public Transit Riders’ Travel Pattern from GPS Data: A Case Study in Harbin

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