Revealing Taxi Interaction Network of Urban Functional Area Units in Shenzhen, China

Lai, Guijun; Shang, Yuzhen; He, Binbao; Zhao, Guanwei; Yang, Ming

doi:10.3390/ijgi11070377

Cited by 6 publications

(4 citation statements)

References 49 publications

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“…The recognition of public services and commercial and related sub-classes has positive significance for urban facilities construction improvement and hot spot detection [ 57 , 58 ]. In addition, the distribution of fine functional areas has certain reference value for the analysis of the causes of traffic congestion [ 59 ], the evaluation of regional functional use intensity [ 23 ], and the planning calibration [ 60 ] and renewal of cities [ 61 ]. Some scholars have also finely classified UFAs, but the data sources were different [ 5 , 23 , 62 ].…”

Section: Discussionmentioning

confidence: 99%

Sub-Block Urban Function Recognition with the Integration of Multi-Source Data

Liu

Deng

et al. 2022

Sensors

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The recognition of urban functional areas (UFAs) is of great significance for the understanding of urban structures and urban planning. Due to the limitation of data sources, early research was characterized by problems such as singular data, incomplete results, and inadequate consideration of the socioeconomic environment. The development of multi-source big data brings new opportunities for dynamic recognition of UFAs. In this study, a sub-block function recognition framework that integrates multi-feature information from building footprints, point-of-interest (POI) data, and Landsat images is proposed to classify UFAs at the sub-block level using a random forest model. The recognition accuracies of single- and mixed-function areas in the core urban area of Guangzhou, China, obtained by this framework are found to be significantly higher than those of other methods. The overall accuracy (OA) of single-function areas is 82%, which is 8–36% higher than that of other models. The research conclusions show that the introduction of the three-dimensional (3D) features of buildings and finer land cover features can improve the recognition accuracy of UFAs. The proposed method that uses open access data and achieves comprehensive results provides a more practical solution for the recognition of UFAs.

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Section: Discussionmentioning

confidence: 99%

Sub-Block Urban Function Recognition with the Integration of Multi-Source Data

Liu

Deng

et al. 2022

Sensors

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“…Using various traffic flow data and spatial network approaches, researchers have extensively explored urban travel patterns and traffic networks from the perspective of traffic demand [54][55][56]. By leveraging big data, including mobile phone, smart card, floating vehicle, and social media data, researchers can analyze user movement data as traffic flows on networks, thereby creating weighted networks that reveal the structure and related properties of UTNs [57][58][59]. Some studies have shown that complex network analysis based on various types of emerging traffic flow data can effectively reveal urban traffic demand and its dynamics [11,60].…”

Section: Related Workmentioning

confidence: 99%

Exploring Public Transportation Supply–Demand Structure of Beijing from the Perspective of Spatial Interaction Network

Jian

Meng

et al. 2023

IJGI

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A comprehensive understanding of the relationship between public transportation supply and demand is crucial for the construction and sustainable development of urban transportation. Due to the spatial and networked nature of public transportation, revealing the spatial configuration and structural disparities between public transportation supply and demand networks (TSN and TDN) can provide significant insights into complex urban systems. In this study, we explored the spatial configuration and structural disparities between TSN and TDN in the complex urban environment of Beijing. By constructing subdistrict-scale TSN and TDN using urban public transportation operation data and mobile phone data, we analyzed the spatial characteristics and structural disparities of these networks from various dimensions, including global indicators, three centralities, and community structure, and measured the current public transportation supply and demand matching pattern in Beijing. Our findings revealed strong structural and geographic heterogeneities of TSN and TDN, with significant traffic supply–demand mismatch being observed in urban areas within the Sixth Ring Road. Moreover, based on the percentage results of supply–demand matching patterns, we identified that the current public transportation supply–demand balance in Beijing is approximately 64%, with around 18% of both excess and shortage of traffic supply. These results provide valuable insights into the structure and functioning of public transportation supply–demand networks for policymakers and urban planners; these can be used to facilitate the development of a sustainable urban transportation system.

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“…Currently, there are many studies on spatial interaction patterns between provinces, cities, within provinces and cities [14][15][16], or urban agglomeration [17], while there is less research on spatiotemporal interaction patterns at the local scale of functional areas [18]. Existing methods mainly focus on the detection of explicit spatial interaction patterns [3], such as clusters in origin-destination (OD) flow data [19][20][21], without gaining insight into the implicit underlying travel demands of the residents moving between different urban areas [19,22].…”

Section: Introductionmentioning

confidence: 99%

Exploring Crowd Travel Demands Based on the Characteristics of Spatiotemporal Interaction between Urban Functional Zones

Peng

Liu

Tang

et al. 2023

IJGI

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As a hot research topic in urban geography, spatiotemporal interaction analysis has been used to detect the hotspot mobility patterns of crowds and urban structures based on the origin-destination (OD) flow data, which provide useful information for urban planning and traffic management applications. However, existing methods mainly focus on the detection of explicit spatial interaction patterns (such as spatial flow clusters) in OD flow data, with less attention to the discovery of underlying crowd travel demands. Therefore, this paper proposes a framework to discover the crowd travel demands by associating the dynamic spatiotemporal interaction patterns and the contextual semantic features of the geographical environment. With urban functional zones (UFZs) as the basic units of human mobility in urban spaces, this paper gives a case study in Wuhan, China, to detect and interpret the human mobility patterns based on the characteristics of spatiotemporal interaction between UFZs. Firstly, we build the spatiotemporal interaction matrix based on the OD flows of different UFZs and analyze the characteristics of the interaction matrix. Then, hotspot poles, defined as the local areas where people gather significantly, are extracted using the Gi-statistic-based spatial hotspot detection algorithm. Next, we develop a frequent interaction pattern mining method to detect the frequent interaction patterns of the hotspot poles. Finally, based on the detected frequent interaction patterns, we discover the travel demands of crowds with semantic features of corresponding urban functional zones. The characteristics of crowd travel distance and travel time are further discussed. Experiments with floating car data, road networks, and POIs in Wuhan were conducted, and results show that the underlying travel demands can be better discovered and interpreted by the proposed framework and methods in this paper. This study helps to understand the characteristics of human movement and can provide support for applications such as urban planning and facility optimization.

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Revealing Taxi Interaction Network of Urban Functional Area Units in Shenzhen, China

Cited by 6 publications

References 49 publications

Sub-Block Urban Function Recognition with the Integration of Multi-Source Data

Sub-Block Urban Function Recognition with the Integration of Multi-Source Data

Exploring Public Transportation Supply–Demand Structure of Beijing from the Perspective of Spatial Interaction Network

Exploring Crowd Travel Demands Based on the Characteristics of Spatiotemporal Interaction between Urban Functional Zones

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