Identification of Urban Functional Zones Based on the Spatial Specificity of Online Car-Hailing Traffic Cycle

Abstract

The study of urban functional zoning is not only important for analyzing urban spatial structure but also for optimizing urban management and promoting scientific urban planning. Different areas undertaking different urban functions correspond to different traffic patterns and specific cycles. Here, a method named Urban Functional Zoning based on the Spatial Specificity (UFZ-SS) is proposed. The core of this method is to obtain urban spatial zoning through the specific cycles of traffic flows. First, UFZ-SS uses the Ensemble Empirical Modal Decomposition (EEMD) method to extract the specific periodic signal characteristics of traffic flows. Second, UFZ-SS calculates the contribution of online car-hailing traffic of different cycles in each zone. Then, the Gaussian Mixture Model (GMM) is utilized to classify all spatial zones into different spatial partitions based on the contribution of each periodic signal. Finally, this study validates UFZ-SS with the online car-hailing traffic volume in northeast Chengdu, China. The results show that the periodic characteristics of traffic can be effectively extracted and analyzed by the EEMD method, and highly distinct and accurate urban spatial partitioning results can be derived by spatial clustering based on the measures of specific cycles. Moreover, with the assistance of Point of Interest (POI) data, we verify the functional zones and structural patterns, which further demonstrates the validity and rationality of urban functional zones identified by UFZ-SS. This study provides a new potential perspective for the identification of urban functional zones, which may lead to a better understanding of the urban spatial structure and even urban planning.

Method for the Identification and Classification of Zones with Vehicular Congestion

“…The exploration of vehicular trajectory data streams in research has been thorough, with numerous studies devising clustering methods tailored to diverse applications [2][3][4]. Through the use of data streams, alternatives have been proposed to improve traffic management by analyzing the spatial structure and extracting traffic-related features [5]. The improvement in efficient traffic management is evidenced by the enriched analysis of data streams when users actively participate, providing feedback that contributes to improve the systems [6].…”