When TEDDY meets GrizzLY: temporal dependency discovery for triggering road deicing operations

Abstract

Temporal dependencies between multiple sensor data sources link two types of events if the occurrence of one is repeatedly followed by the appearance of the other in a certain time interval. TEDDY algorithm aims at discovering such dependencies, identifying the statically significant time intervals with a chi2 test. We present how these dependencies can be used within the GrizzLY project to tackle an environmental and technical issue: the deicing of the roads. This project aims to wisely organize the deicing operations of an urban area, based on several sensor network measures of local atmospheric phenomena. A spatial and temporal dependency-based model is built from these data to predict freezing alerts.