Identifying Region-Wide Functions Using Urban Taxicab Trajectories

Zhang, Daqiang; He, Zongjian; Zhao, Shengjie; Fan, Ke; Park, Sang Oh; Jiang, Zhibin

doi:10.1145/2821507

Cited by 19 publications

(14 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) TAZ: the area is divided based on the urban traffic flow, considering the landform, road distribution, administrative division, and sampling convenience [2,47]; (4) clustering unit: the unit, such as Voronoi cell, is formed by clustering location-based service data [4,48]; (5) geoscene: the optimal boundary of the functional zone is obtained by multiscale segmentation, resulting in the geoscene (image object) used as the basic unit [49,50]; (6) building unit: taking buildings as the basic object of urban space cognition, the identification of functional zones can be completed by identifying the functions of buildings [51].…”

Section: The Basic Unit Of Urban Functional Zone Identificationmentioning

confidence: 99%

“…The clustering unit is determined by the radiation range of location-based data. In underdeveloped areas, the density of location-based data is low, and there are only one or several clustering units in a large area of land, reducing the identification accuracy [48,59]. The geoscene represents actual objects.…”

Section: The Basic Unit Of Urban Functional Zone Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Liu

Deng

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Urbanization is accelerating due to economic and societal development. The accurate identification of urban functional zones is significant for urban structure optimization, urban planning, and resource allocation. This paper reviews the scholarly literature on urban functional zone identification. Based on the retrieval results of databases, we analyzed the overview and current status. The identification methods and classification schemes are summarized from the existing research. The following results were obtained: (1) point of interest (POI) data are widely used for functional zone identification; (2) the block is the most common unit for functional zone identification; (3) cluster analysis is the main approach for urban functional zone identification; (4) most of the classification schemes are based on the dominant land use and characteristics of data sources. We predict future trends of urban functional zone identification based on the reviewed literature. Our findings are expected to be valuable for urban studies.

show abstract

Section: The Basic Unit Of Urban Functional Zone Identificationmentioning

confidence: 99%

Section: The Basic Unit Of Urban Functional Zone Identificationmentioning

confidence: 99%

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Liu

Deng

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In fact, Voronoi-based techniques have been presented as effective solutions for disseminating and querying data in decentralized, distributed systems. This kind of techniques have been successfully applied in the IoT [20], wireless sensor networks [21], [22], underwater networks [23], [24], embedded computing systems [25], vehicular networks [26], and even distributed virtual environments [27], [28].…”

Section: A Contributionmentioning

confidence: 99%

AoI-Based Multicast Routing Over Voronoi Overlays With Minimal Overhead

2020

View full text Add to dashboard Cite

The increasing pervasive and ubiquitous presence of devices at the edge of the Internet is creating new scenarios for the emergence of novel services and applications. This is particularly true for location and context-aware services. These services call for new decentralized, self-organizing communication schemes that are able to face issues related to demanding resource consumption constraints, while ensuring efficient locality-based information dissemination and querying. Voronoi-based communication techniques are among the most widely used solutions in this field. However, when used for forwarding messages inside closed areas of the network (called Areas of Interest, AoIs), these solutions generally require a significant overhead in terms of redundant and/or unnecessary communications. This fact negatively impacts both the devices' resource consumption levels, as well as the network bandwidth usage. In order to eliminate all unnecessary communications, in this paper we present the MABRAVO (Multicast Algorithm for Broadcast and Routing over AoIs in Voronoi Overlays) protocol suite. MABRAVO allows to forward information within an AoI in a Voronoi network using only local information, reaching all the devices in the area, and using the lowest possible number of messages, i.e., just one message for each node included in the AoI. The paper presents the mathematical and algorithmic descriptions of MABRAVO, as well as experimental findings of its performance, showing its ability to reduce communication costs to the strictly minimum required. INDEX TERMS Area of Interest, multicast, Voronoi networks 1 I. INTRODUCTION We are witnessing a fast and vast expansion of the Internet at its edges [1]. This is mainly due to the pervasive diffusion in the environment of smart objects, such as sensors, Internet of Things (IoT) devices, user personal devices, etc. This scenario allows the emergence of novel services and applications [2]-[8], supported by potentially large networks of highly distributed and autonomous devices. Traditional centralized control and communication techniques do not suit the needs and requirements of such an environment. In particular, these devices are usually equipped with computing and communication capabilities, that allow them to create and exchange information both among themselves and with other remote services. One of the most challenging problems is related to the fact that this kind of systems typically requires frequent exchanges of information among a large number of geographically dispersed devices. The communication complexity is further increased by the fact that devices cannot always count on the support of central communication infrastructures, posing the need to apply autonomous, self-organizing forms of communication and interaction among devices [9]-[13]. Location and context-aware services [14] in this scenario are faced with additional issues. In fact, these services are characterized by the fact that most of the messages are directed (and of interest) only to limited/specific are...

show abstract

“…Therefore, in this paper Granger Causality is used to enhance the accuracy and computation simpleness for multivariate time serie mode.Granger Causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another. Compared with prediction based on entropy theory [23][24][25][26] in the latest researches, more direct correlation of different traffic series are considered in the prediction based on Granger causality.…”

Section: Related Workmentioning

confidence: 99%

Integrating Granger Causality and Vector Auto-Regression for Traffic Prediction of Large-Scale WLANs

Zheng¹,

Zhou²,

Wu³

et al. 2016

KSII TIIS

View full text Add to dashboard Cite

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method-Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

show abstract

Identifying Region-Wide Functions Using Urban Taxicab Trajectories

Cited by 19 publications

References 39 publications

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

AoI-Based Multicast Routing Over Voronoi Overlays With Minimal Overhead

Integrating Granger Causality and Vector Auto-Regression for Traffic Prediction of Large-Scale WLANs

Contact Info

Product

Resources

About