.png){kind=icon}
返回
A network-constrained clustering method for bivariate origin-destination movement data
.png){kind=icon}
PRE
AI
DOI：10.1080/13658816.2022.2137879.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
For bivariate origin-destination (OD) movement data composed of two types of individual OD movements, a bivariate cluster can be defined as a group of two types of OD movements, at least one of which has a high density. The identification of such bivariate clusters can provide new insights into the spatial interactions between different movement patterns. Because of spatial heterogeneity, the effective detection of inhomogeneous and irregularly shaped bivariate clusters from bivariate OD movement data remains a challenge. To fill this gap, we propose a network-constrained method for clustering two types of individual OD movements on road networks. To adaptively estimate the densities of inhomogeneous OD movements, we first define a new network-constrained density based on the concept of the shared nearest neighbor. A fast Monte Carlo simulation method is then developed to statistically estimate the density threshold for each type of OD movements. Finally, bivariate clusters are constructed using the density-connectivity mechanism. Experiments on simulated datasets demonstrate that the proposed method outperformed three state-of-the-art methods in identifying inhomogeneous and irregularly shaped bivariate clusters. The proposed method was applied to taxi and ride-hailing service datasets in Xiamen. The identified bivariate clusters successfully reveal competition patterns between taxi and ride-hailing services.
Keyword:
Origin-destination movement data
bivariate clustering
road network
spatial heterogeneity
期刊
IF:
5.1
论文数:
2.7K
被引数:
9.3K
机构
引用论文
.png){kind=icon}
A multidimensional spatial scan statistics approach to movement pattern comparison运动模式比较的多维空间扫描统计方法
PREAI
分享收藏Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization使用蚁群优化检测起点-目的地流中任意形状的簇
PREAI
分享收藏
查看更多内容