Abstract
Gradual deviation in track gauge of tram systems resulted from tram traffic is unavoidable. Tram gauge deviation is considered as an important parameter in poor ride quality and the risk of train derailment. In order to decrease the potential problems associated with excessive gauge deviation, implementation of preventive maintenance activities is inevitable. Preventive maintenance operation is a key factor in development of sustainable rail transport infrastructure. Track degradation prediction modelling is the basic prerequisite for developing efficient preventive maintenance strategies of a tram system. In this study, the data sets of Melbourne tram network is used and straight rail tracks sections are examined. Two model types including plain Support Vector Machine (SVM) and SVM optimised by Genetic Algorithm (GA- SVM) have been applied to the case study data. Two assessment indexes including Mean Squared Error (MSE) and the coefficient of determination (R2) are employed to evaluate the performance of the proposed models. Based on the results, GA-SVM model produces more accurate outcomes than plain SVM model.
Highlights
Nowadays public transport is regarded as an effective alternative to private transport
Track gauge deviation is represented as a valuable measure of ride quality and the potential of train derailment
Practices for predication of gauge deviation is a principal prerequisite in establishment of the preventive maintenance operations
Summary
Nowadays public transport is regarded as an effective alternative to private transport. As an accessible approach of public transport option, tram has different benefits compared to conventional transit systems. They are more economically efficient due to lower cost of repair and purchase than heavy rail trains. Comparative analysis conducted for light rail transport demonstrated that designing strategies and programs for tram infrastructure maintenance is a multifaceted problem in which the behaviour modelling of track degradation is an essential part. Without developing rail track degradation prediction models, designing preventive maintenance strategies is not possible [7, 8].
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