| Abstract |
The rapid development of information and communication technologies and mobile computing has generated a variety of mobility big data, providing new opportunities for understanding and exploring the spatiotemporal distribution and mobility characteristics of resident travel, and further contributing to the construction of smart cities. However, the emerging mobile data have experienced significant growth in both scale and complexity compared to traditional data, posing challenges for its structural characteristic analysis. To address these issues, this paper proposes an analytical framework to deal with the spatiotemporal distribution characteristics of high-dimensional ride-hailing travel pattern. Compared to traditional square partitions, a regular hexagon is closer to a circle, and the six adjacent hexagons connected to its edges are symmetrically equivalent, which can be more advantageous in aggregating demands with similar travel characteristics into the same partition. Therefore, hexagonal partition is selected as the basic clustering unit, and different spatiotemporal patterns are identified by clustering homogeneous travel distribution groups. Firstly, the spatiotemporal characteristics of travel distribution aggregated in the hexagonal partition are summarized into three main components: the departure demand distribution at the origin partition, the spatial distribution at the destination partition, and the arrival demand distribution at the destination partition. The spatiotemporal similarity between two partitions can be expressed as the product of these three types of distribution similarity. Furthermore, a Clustering Algorithm with Fast Search and Find of Spatiotemporal Density Peaks (CFSFSTDP) is proposed to identify the spatiotemporal patterns of ride-hailing travel distribution in each partition. The spatiotemporal distances between different partitions are obtained through the calculation of spatiotemporal similarity. Finally, affinity propagation clustering algorithm is used to perform clustering analysis on the time series variation pattern of spatiotemporal pattern of travel distribution in each partition. The time series similarity of spatiotemporal patterns between different partitions is represented by the sum of Euclidean distances between time series of each interval, and the model converges through continuous updates of attractiveness and affiliation indices. Through the empirical analysis of Didi Chuxing order data in Chengdu for one month, the validity of the method is verified. Based on the identified seven spatiotemporal distribution patterns, the differences of spatiotemporal patterns in the size, location, and time of demand are analyzed, and the functional types of ride-hailing travel in different partitions are discussed. The identified six time series patterns better grasp the time continuity of spatiotemporal patterns of ride-hailing travel distribution and help to better build the corresponding spatiotemporal evolution digital. © 2024 Science Press. All rights reserved. |
