Route Reconstruction from Floating Car Data with Low Sampling Rate Based on Feature Matching

Abstract

Floating Car Technology is widely used to collect traffic information. To reappear the actual trips of drivers, a bi-level probability method is proposed to reconstruct routes from floating car data, address two issues: the first one is incorrect map matching caused by GPS accuracy and complexity of road network; and the second one is the link missing duo the low sampling rate of floating car. Using confidence region, GPS points are divided into three types: zero-feature points with no feature matching, single-feature points that have a unique matched link or node and multiple-features points that have multiple features. The matching probability for GPS points to the possible feature according to the distance between GPS point and the link, which is assumed to be normal distribution. The missing links between two single-feature points are reconstructed by the shortest path algorithm with consideration of the probability of multiple matched features. A case study of Guangzhou floating car data shows that the proposed method can produce reasonable routes on complicated urban road network.