Short communication: An integrated error-detecting method based on expert knowledge for GPS data points measured in Qinghai-Tibet Railway

Authors:
Dewang Chen;Tao Tang;Fang Cao;Baigen Cai
Affiliations:
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

As there are huge amounts of Global Positioning System (GPS) data points measured in the Qinghai-Tibet Railway (QTR) with a length of 1142km, it was inevitable that some measuring errors existed due to various situations in measurement. It is very important to develop a method to automatically detect the possible errors in all data points so as to modify them or measure them again to improve the reliability of GPS data. Four error patterns, including redundant measurement, sparse measurement, back-and-forth measurement, and big angle change, were obtained based on expert knowledge. Based on the four error patterns, four algorithms were developed to detect the corresponding possible errors in data points. To delete the repetitive errors by different algorithms and effectively display the possible errors, an integrated error-detecting method was developed by reasonably assembling the four algorithms. After four performance indices were given to evaluate the performance of the error-detecting method, six GPS track data sets between seven railway stations in the QTR were used to validate the method. Thirty-eight segments of some sequential points that are possibly wrong were found by the method and fourteen of them were confirmed by measurement experts. The detecting rate of the method was 100% and the duration time of the detecting process was less than half an hour compared with the 94h manual workload. The validation results show that the method is effective not only in decreasing workload, but also in ensuring correctness by integrating the domain expert knowledge to make the final decision.