Modeling and Algorithms of GPS Data Reduction for the Qinghai–Tibet Railway

Satellites are currently being used to track the positions of trains. Positioning systems using satellites can help reduce the cost of installing and maintaining trackside equipment. This paper develops a nonlinear combinatorial data reduction model for a large amount of railway Global Positioning System (GPS) data to decrease the memory space and, thus, speed up train positioning. Three algorithms are proposed by employing the concept of looking ahead, using the dichotomy idea, or adopting the breadth-first strategy after changing the problem into a shortest path problem to obtain an optimal solution. Two techniques are developed to substantially cut down the computing time for the optimal algorithm. The surveyed GPS data of the Qinghai-Tibet railway (QTR) are used to compare the performance of the algorithms. Results show that the algorithms can extract a few data points from the large amount of GPS data points, thus enabling a simpler representation of the train tracks. Furthermore, these proposed algorithms show a tradeoff between the solution quality and computation time of the algorithms.