Cesar Quiroga

Geovisualization of Archived ITS Data-Case Studies

Although transportation data have a spatial component by nature, this component generally has not been fully utilized when it comes to intelligent transportation system (ITS) archived data. This paper demonstrates the use of geographic information system (GIS) to spatially visualize and analyze transportation management center (TMC)-related performance measures derived from archived ITS data from a traffic management center in Texas, namely, San Antonios TransGuide. The paper discusses three case studies. The first case study involved a performance evaluation of an automatic incident detection algorithm deployed by many traffic management centers after a few years of implementation in the field. The overall detection rate was found to be about 20%, and the overall false rate was about 0.005%. The second case study summarizes work completed to address quality control issues associated with a very large archived ITS data set composed of some 3.4 billion 20-s loop detector data records. There were some 1.6 billion (about 48%) speed, volume, and occupancy records that had a quality control flag. Approximately 1.5 billion flagged records were “valid” records, and the remaining 126 million (about 3.7%) flagged records were “abnormal” records. In the third case study, the researchers evaluated the data completeness both at the aggregate level (by server) and at a more detailed individual detector level. At the server level, the completeness rate varied from 95% to 100%. At the individual detector level, the analysis showed that, on average, the completeness rate for all detectors was about 80%. The researchers utilized GIS to prepare maps showing the spatial distribution of incident detection rate, false alarm rate, quality control flags, and data completeness rate. It is worth mentioning that all of the analysis was performed at a detailed segment-by-segment level, which is by itself a unique addition. The researchers recommend implementing the demonstrated spatial analysis of the archived ITS data. This will allow TMC officials to identify spots with any abnormal behavior, whether it is at the corridor level or even at the segment level.