Effects of Controlling Parameters on Performance of a Decision-Rule Map-Matching Algorithm

Abstract

Advanced map-matching algorithms resolve spatial ambiguities between differential global positioning system (DGPS) and roadway centerline data. Most of these algorithms need further research to assess their performances with respect to their controlling parameters and their relationships with spatial data and temporal resolution. This paper presents an analysis of the effects of three parameters controlled by the user and two variables dominated externally through simulated data on the performance of a postprocessing decision-rule map-matching algorithm previously developed by the writers. The algorithm is tested against three different digital roadway map scales from counties in Wisconsin and Iowa, and two automatic vehicle location (AVL)/DGPS technologies mounted on intelligent winter maintenance vehicles. Sensitivity analyses indicate that the algorithm is sensitive to controlling parameter values depending on the data being tested. The algorithm satisfactorily resolves spatial ambiguities given different spatial data qualities, AVL/DGPS technologies, and temporal resolutions. Statistical analysis suggests a direct relationship between data collection frequency and spatial mismatch resolution. Parameter values are presented for minimizing false negatives and maximizing solved cases, thus enhancing the performance of the map-matching algorithm.