Comprehensive Probability Map-matching Method for Digital Track Map Validation

Global Navigation Satellite System (GNSS) has been widely studying in train positioning. GNSS based train positioning can realize the transformation from 3-dimensional (3D) GNSS position to 1-dimensional (1D) kilometer mark by map-matching, in which the accurate digital track map (DTM) is the key input for map-matching. This paper proposes a complete method for DTM generation and validation. The DTM generation process is introduced firstly, in which data reduction is an important step for reducing storage space and improving data utilization. The 3 track representation principles (discrete point method, curve method and mixed types of line method) are all available with the reduction technical specification. To compare performance of them, the 3 representation principles and their corresponding data reduction methods are introduced. Then, to obtain the projection distance for validating the quality of the generated different DTMs, the comprehensive probability map-matching method is proposed. In this method, a candidate set of track segments is determined according to range of track segment and confidence region of train positioning result, and the track segment where the train is having highest confidential is selected based on the maximum comprehensive probability calculated by distance / heading / topology information. The matching position and projection distance are obtained by the vertical projection method or the shortest distance method at last. The measured data from Beijing Round Track demonstrated that the DTMs generated by above 3 principles meets the technical specification, in which the DTM generated by curve method is with the least segmentation points, and the DTM generated by mixed types of line method is with the highest overall accuracy. The map-matching method can be efficiently applied to the validation of different DTMs.