FAULTY LOOP DATA ANALYSIS/CORRECTION AND LOOP FAULT DETECTION

Abstract

Inductive Loops are widely used in California for traffic detection and monitoring. This paper reviews previous work on faulty loop data analysis for correction and faulty loop diagnosis. The authors emphasize that it is necessary to distinguish faulty loop data analysis and loop fault detection according to the data level. According to the level of data used, they divide the work in three levels: the macroscopic level as in TMC (Transportation Management Center) or PeMS (Performance Measurement System) in California, which uses highly aggregated data to look at loop problems related wide range; the mesoscopic level, which involves synchronized data for a section of freeways involving several control cabinet such as Berkeley Highway Lab (BHL); and the microscopic level or control cabinet level including all the loop stations involved. They also review the corresponding data correction methods. The authors conclude that higher level data analysis could be effective for systematic errors which are caused by communication system and power outage. Data analysis in the highway section/corridor level could be used to locate suspicious loop stations if the communication system can be made reliable. Those two methods can only be called indirect since they are affected by the communication system for data passing.