Spatial flow data represent meaningful spatial interaction (SI) phenomena between
geographic regions that are often highly dynamic. However, most existing flow analytical …

Clustering the trajectories of vehicles moving on road networks is a key data mining
technique for understanding human mobility patterns, as well as their interactions with urban …

Accurate estimation of ride-hailing travel mobility is significant for demand management, and
transportation planning. Although existing intervening opportunities models based on …

The travel source–sink phenomenon is a typical urban traffic anomaly that reflects the
imbalanced dissipation and aggregation of human mobility activities. It is useful for …

Against the backdrop of the strong market expansion of free-floating bikeshare systems
(FFBS), the future of government-funded station-based bikeshare system (SBBS) is a matter …

With the rapid development of sensors and communication technologies, it has become
easy to collect large-scale and long-term crowd movement positioning data, which brings …

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 …

Network-constrained spatial flows are usually used to describe movements between two
spatial places on a road network. The analysis of the spatial associations between different …

Measuring bivariate spatial association plays a key role in understanding the spatial
relationships between two types of geographical flow (hereafter referred to as “flow”) …

Flows can reflect the spatiotemporal interactions or movements of geographical objects
between different locations. Measuring the spatiotemporal autocorrelation of flows can help …