Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data

Guang Yuan,Yanyan Chen,Lishan Sun,Jianhui Lai,Zhuo Liu,Tongfei Li

doi:10.1155/2020/8956910

Abstract

With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing’s 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method.

Highlights

In the process of urban planning and management [1], the division of urban functional areas (UFAs) is a fundamental step. e distribution of UFAs is directly related to decisionmaking regarding urban transportation, resource management, and factory relocation [2]
We consider the abovementioned factors comprehensively, including the influence of different Geographical Area Subdivisions (GASs) sizes, statistical indicators of Call Detail Record (CDR) data, data sources containing land use features, and verification methods that are both qualitative and quantitative. e purpose of this study is to develop a practicable method for UFA identification, enabling reliable decision-making for urban planning and traffic planning and improving the utilization rate of existing big data applications in the engineering field
This study demonstrates that both calculation indicators and GASs must be considered before constructing the CDR model. e size of GASs is shown to have a significant impact on the results of numerical experiments, which further affect the indicators of CDR data such as the Number of Communication Activities (NCA), CAI, and the User Density (UD). ird, points of interest (POIs) data overcome the shortcomings of CDR data in the analysis of land use characteristics

Summary

Introduction

In the process of urban planning and management [1], the division of urban functional areas (UFAs) is a fundamental step. e distribution of UFAs is directly related to decisionmaking regarding urban transportation, resource management, and factory relocation [2]. In the process of urban planning and management [1], the division of urban functional areas (UFAs) is a fundamental step. E distribution of UFAs is directly related to decisionmaking regarding urban transportation, resource management, and factory relocation [2]. As urban traffic congestion increases, it is important to alleviate this congestion to prevent an imbalance between urban traffic supply and demand caused by an unreasonable layout of urban functions. Erefore, the precise and timely identification of UFAs is urgently required. The identification of UFAs has positive significance for policy formulation, resource allocation, transportation, and enterprise development [3]. It has great significance for refining future traffic demand management

Methods

Results

Discussion

Conclusion