Procuring usage of the public transportation system enhances the promising effect of limiting the number of own vehicles usage in the contemporary world. The present research advocates a new paradigm of the Intelligent Transportation System (ITS) in the near future, to rescue fossil fuel and to maintain a healthy environment for the current generation. To provide this facility, Long Short Term Memory (LSTM) based intelligent learner has been proposed. This intelligent learner is mainly used to predict high vehicle demand requests in order to utilize a public transport system effectively. In this way, excess usages of vehicles are reduced from low vehicle demand request locations to the locations where high vehicles demand requests are generated. Moreover, a new enhanced approach has also been designed to establish communication between the onboard vehicles and the passengers for instant reservation of their seats based on real-time sensors. To achieve the effective usage of the public transportation system, an effective dynamic scheduling algorithm that dedicates more convenient travel in the complex transportation system, has been proposed. The proposed system results are evaluated using real-time transport data, which are collected from major cities and they are implemented to predict the exact vehicles demand. The performance results are compared with various existing methods and the proposed system has proved its efficiency than the existing methods. When the proposed system is implemented, it improves 87% usage of public transportation as well as the usage of taxis and own vehicles would be reduced drastically in the city.

中文翻译：

---

一种改进的公共交通系统，以在智能交通系统中有效地使用车辆

采购公共交通系统的使用增强了在当代世界限制自有车辆使用数量的有希望的效果。目前的研究主张在不久的将来采用智能交通系统 (ITS) 的新范式，以拯救化石燃料并为当代人维持健康的环境。为了提供这种便利，已经提出了基于长短期记忆 (LSTM) 的智能学习器。该智能学习器主要用于预测高车辆需求请求，以有效利用公共交通系统。以这种方式，车辆的过度使用从低车辆需求请求位置减少到产生高车辆需求请求的位置。而且，还设计了一种新的增强方法来建立车载车辆和乘客之间的通信，以便基于实时传感器即时预订座位。为实现公共交通系统的有效利用，提出了一种有效的动态调度算法，在复杂的交通系统中为出行提供更便捷的服务。使用从主要城市收集的实时交通数据评估提议的系统结果，并实施这些数据以预测准确的车辆需求。将性能结果与各种现有方法进行了比较，并且所提出的系统证明了其比现有方法的效率。当提议的系统实施时，