A parallel method for assessing track segment quality in railway maintenance

Track Quality Index (TQI) is a crucial indicator for assessing the condition of rail and rapid transit tracks. With the rapid increase of railway mileage and the complexity of processing large volumes of track inspection data, traditional methods that rely on a single device to process track geometry inspection data and calculate TQI no longer meet the efficiency and accuracy requirements of modern railways. This paper aims to propose a parallel method for assessing TQI track maintenance. The method is based on a multi-node data platform, using parallel computing technology to distribute different tasks in TQI calculation process such as track inspection data mileage correction, invalid data identification, and standard deviation calculation to multiple nodes. This approach enables standardized and rapid processing of large-scale track geometry inspection data. Using track geometry inspection data from various lengths of high-speed railway lines in China to test the proposed method, the results show that the new method significantly improves computational efficiency compared to traditional methods while maintaining high accuracy. The application of this method will greatly enhance the efficiency and accuracy of railway maintenance, providing robust technical support for the management and maintenance of railway infrastructure.