Due to the recent advances in intelligent transportation systems (ITS), Automatic Train System (ATS) gained significant attention among the research community. An effective ATS offers the whole railway network to operate in a safe, cost-effective and proficient manner against sudden disturbances like temporary platform blockages. Numerous Train Timetable Rescheduling (TTR) models have been presented for managing unforeseen events which might disturb the timetable. The main aim of an effective TTR model is to reduce power utilization by consuming the entire benefits of reproductive braking energy under a random situation. In this view, this paper presents a new TTR model to optimize the energy of metro systems by the incorporation of improved genetic algorithm (IGA) and long short term memory (LSTM) based recurrent neural network (RNN). The proposed method incorporates three different models, namely controller, timetable, and energy models. The proposed method requires minimum time to recompute a new schedule and offers effective solutions instantly after a random disturbance happens. The performance validation of the proposed IGSA-LSTM model is simulated using Chennai Metro Train Station. The proposed method significantly reduces the energy consumption of metro train and reaches to a minimum average energy utilization of 696 kWh.

由于智能交通系统（ITS）的最新发展，自动火车系统（ATS）在研究界引起了广泛关注。有效的空中交通服务可为整个铁路网络提供安全，经济高效的服务，以应对诸如临时站台堵塞之类的突发干扰。已经提出了许多列车时刻表重新安排（TTR）模型，用于管理可能干扰时刻表的不可预见的事件。有效的TTR模型的主要目的是通过在随机情况下消耗生殖制动能量的全部益处来降低功率利用率。因此，本文提出了一种新的TTR模型，该模型通过结合改进的遗传算法（IGA）和基于长期短期记忆（LSTM）的递归神经网络（RNN）来优化地铁系统的能量。所提出的方法结合了三种不同的模型，即控制器，时间表和能源模型。所提出的方法需要最少的时间来重新计算新的时间表，并在发生随机干扰后立即提供有效的解决方案。使用钦奈地铁站模拟了所提出的IGSA-LSTM模型的性能验证。所提出的方法显着降低了地铁列车的能耗，并达到了696 kWh的最低平均能耗。