Soft computing methods applied to train station parking in urban rail transit

This paper presents three models – a linear model, a generalized regression neural network (GRNN) and an adaptive network based fuzzy inference system (ANFIS) – to estimate the train station parking (TSP) error in urban rail transit. We also develop some statistical indices to evaluate the reliability of controlling parking errors in a certain range. By comparing modeling errors, the subtractive clustering method other than grid partition method is chosen to generate an initial fuzzy system for ANFIS. Then, the collected TSP data from two railway stations are employed to identify the parameters of the proposed three models. The three models can make the average parking errors under an acceptable error, and tuning the parameters of the models is effective in dynamically reducing parking errors. Experiments in two stations indicate that, among the three models, (1) the linear model ranks the third in training and the second in testing, nevertheless, it can meet the required reliability for two stations, (2) the GRNN based model achieves the best performance in training, but the poorest one in testing due to overfitting, resulting in failing to meet the required reliability for the two stations, (3) the ANFIS based model obtains better performance than model 1 both in training and testing. After analyzing parking error characteristics and developing a parking strategy, finally, we confirm the effectiveness of the proposed ANFIS model in the real-world application.

A model for predicting train station parking errors is generated from the field parking data by combining ANFIS and substractive clustering. The model can predict the parking error precisely from the position and speed of a train near a station both in simulation and application.Figure optionsDownload as PowerPoint slideHighlights
► We deal with train station parking in a new view of soft computing by learning underlying rules from field data set.
► We develop three models to estimate train station parking errors and find ANFIS based model the best one by comparing their performance.
► We develop a parameter updating method which can dynamically reduce the parking errors and avoid costly investment in sensors.