网约车出行分布时空模式及其时间序列模式识别

Time Series Variation Pattern Recognition of Spatiotemporal Distribution Patterns of Ridehailing

通信与移动计算技术的快速发展产生了各种出行大数据,为理解和挖掘交通时空出行特征、建设智慧城市提供了新的机会。然而,新兴移动数据规模与复杂性的显著增加也为其结构特征分析带来了挑战。本研究以六边形时空分区为基本聚类单元,提出了一种处理高维网约车出行时空模式的分析框架,通过聚类同质的出行分布群体来识别不同的时空模式。首先,将六边形分区内集计的出行分布时空特征概括为起点的需求量分布、终点的空间分布和终点的需求量分布。进一步,提出了基于时空密度峰值的快速聚类(CFSFSTDP)算法,通过计算时空相似性来识别各分区的网约车出行分布时空模式。最后,采用近邻传播算法来对各分区聚类出的出行分布时空模式的时间变化序列进行聚类分析,捕捉网约车出行分布时空模式的时间序列模式。对成都一个月的滴滴出行订单数据进行实证分析验证了该方法,分析了不同的时空模式在需求大小、位置和时间上的差异,探讨了网约车出行在不同区域的功能类型。其识别出的6类时间序列模式把握了网约车出行分布时空模式的时间连续性,有助于进一步构建网约车出行时空演化数字孪生平台。

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 ridehailing 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.