Determine spatiotemporal causal interactions in data

摘要

Techniques for detecting outliers in data and determining spatiotemporal causal interactions in the data are discussed. A process collects global positioning system (GPS) points in logs and identifies geographical locations to represent the area where the service vehicles travelled with a passenger. The process models traffic patterns by: partitioning the area into regions, segmenting the GPS points from the logs into time bins, and identifying the GPS points associated with transporting the passenger. The process projects the identified GPS points onto the regions to construct links connecting GPS points located in two or more regions. Furthermore, the process builds a three-dimensional unit cube to represent features of each link. The points farthest away from a center of data cluster are detected as outliers, which represent abnormal traffic patterns. The process constructs outlier trees to evaluate relationships of the outliers and determines the spatiotemporal causal interactions in the data.

主权项

1. A method implemented at least partially by a processor, the method comprising:
accessing, from a database associated with a content service provider, logs of global positioning system (GPS) points collected by geolocation sensors associated with service vehicles; identifying geographical locations from the GPS points to represent an area where the service vehicles travelled as recorded in the logs; generating a graph of regions associated with the area based at least in part on the logs associated with the area in a plurality of time frames; detecting outliers in the GPS points based at least in part on the graph; and providing, via a user interface that is presented via a display of a device, recommendations for travelling in the area based at least in part on the outliers.