Balise arrangement optimization for train station parking via expert knowledge and genetic algorithm

Abstract With the rapid development of urban rail transit, train parking accuracy has received much attention, especially for subway lines with platform screen doors. In actual operation, several balises are mounted on the track to enhance the parking accuracy by providing exact positioning data for the train. Currently, the number and positions of the balises are determined by experience and iterative experiments that may greatly increase the costs. Combining expert knowledge and train dynamics, this paper formulates a balise arrangement optimization (BAO) model to study the relationship between the number & locations of balises and parking errors. The resistances, nonlinearity and time delay in train braking system and variable initial speeds that a train enters the parking area are considered in the formulation of BAO model. Moreover, we propose a genetic algorithm (GA) to solve the BAO model and present numerical experiments based on field data collected from Beijing Subway Yizhuang Line. The results indicate that the BAO model can enhance the parking accuracy to about 0.10 m via changing the positions of the balises. Furthermore, we found that: (1) more balises lead to better performance of train parking accuracy; (2) four or five balises are appropriate for balancing the device cost and parking errors.