Methodology of Identifying Causes of Abnormal Map Matching

Abstract:

Abnormal map matching refers to that partial vehicle trajectory is matched to a road segment incorrectly， typically caused by physical occlusion， road missing， and complex road configuration. This study proposes adata-driven methodology of identifying the causes ofabnormal map matching， which supports fast identification and classification of abnormal matching results. First， we summarize the statistical features of abnormal matching results with different causes and present criteria for representation. Second， we propose a segmentation algorithm to distinguish the abnormal results from the trajectory. Third， a classification algorithm is developed based on the random forest model. Finally， the proposed methodology is validated based on the real-world data of urban taxi operations in Shanghai. The case study reveals that the algorithms are effective in identifying the abnormal matching results caused by physical occlusion， local road missing， and complex road configuration， with a precision of 93.5%. In addition， the experiments show the high spatial suitability of the proposed methodology.