Research On Train Station Parking Algorithm For Urban Rail Transit

With the fast development of the urban rail transit, the efficiency and safety of subway systems are paid more and more attention. In recent years, precise train station parking (PTSP) has been acting as an important part in urban rail transit, since platform screen doors (PSD) are installed in most new established stations. However, there are some factors that may impair the accuracy of train station parking, such as the variable initial velocities, inaccurate positioning data and uncertain braking characteristics. Therefore, it is necessary to study the problem of PTSP for a subway system.First, we establish a train braking model, as well as an urban rail transit parking simulation platform according to the braking characteristics and basic resistances. The platform contains the train braking model and it can simulate the process of train station parking. In addition, we can test different parking strategies on this platform.Second, we propose five different braking policies by using soft computing method, which are treated as action vectors of the reinforcement learning (RL). The state vector of RL is defined as the location and velocity of the train while the reward of RL is defined by parking error. We first propose the idea to combine the train parking process with RL. Thus, the PTSP based on RL can be described as follows:we need to find out the optimal policy sequences that correspond to the minimum parking error by using stochastic selection, i.e., Q-learning algorithm.Third, two algorithms are developed to find out the optimal braking policy in urban rail transit, which are optimal stochastic selection algorithm (OSSa) and fuzzy function based Q-learning algorithm (FQLa). After that, we propose three braking rate fusion methods to approximate the parking errors under different initial velocities, which haven’t appeared in OSSa or FQLa. The three braking rate fusion methods are linear braking rate fusion method (LRm), fitting braking rate fusion method (FRm) and interpolating braking rate fusion method (IRm). We also evaluate the two algorithms and three methods by setting different performance evaluation indices.Finally, numerical experiments are developed to test the effectiveness of the algorithms on the parking simulation platform. The results indicate that, both OSSa and FQLa can minimize the parking errors by training and learning repeatedly on different initial velocities, which can keep the parking error within±30cm that meet the parking requirement of urban rail transit. In addition, the two algorithms can also ensure accurate parking probability in the required range of more than99.5%. Furthermore, the FQLa performs better than OSSa. By using braking rate fusion methods, we can calculate the parking errors at any initial velocity without training and keep the parking error within±30cm. The LRm performs the best among the three methods at average parking errors and maximum errors.