Konferensartikel

This paper presents a combined method of fuzzy theory and rough sets theory for the early warning of high-speed railway (HSR) under adverse weather conditions. Based on the monitoring data of meteorological indicators along the railway, a fuzzy c-means (FCM) clustering is first applied in order to figure out the fuzzy distribution of sample data and to fit the corresponding membership function of every indicator. According to the clustering results, every original sample is transformed into its cluster level as string data for the subsequent application of rough sets theory. Then a series of effective rough rules between conditional indicators and the decision indicator is extracted after attribute reduction by the Rosetta toolkit, where the decision indicator is represented by the train deceleration rate. Since the value of an indicator may correspond to several fuzzy levels, the multiple combinations of different conditional indicators will activate multiple rough rules. In order to forecast a clear value of the decision indicator, a centroid-based Max-Min compound arithmetic is applied to clarify relevant rules and determine the warning level. Using the designed algorithm, a case analysis of an HSR line section is conducted to verify the feasibility of the combined method, all meteorological data and operation records are provided by the Shanghai Railway Bureau in China. The results prove that the hybrid algorithm can be applied in the real-time forewarning of HSR train operation, with a global accuracy over 86%.

High-speed railway, Forewarning algorithm, Adverse wheather, Fuzzy theory, Rough sets