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首页> 外文期刊>International journal of communication systems >An improved public transportation system for effective usage of vehicles in intelligent transportation system
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An improved public transportation system for effective usage of vehicles in intelligent transportation system

机译：一种改进的公共交通系统，用于智能运输系统中的车辆有效使用

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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.
机译：采购公共交通系统的利用增强了限制当代世界中自有车辆数量的有希望的效果。目前的研究在不久的将来倡导了智能交通系统（ITS）的新范式，以拯救化石燃料，并为当前一代保持健康的环境。提供该设施，已经提出了长期的短期内存（LSTM）智能学习者。这种智能学习者主要用于预测高车辆需求请求，以便有效地利用公共交通系统。通过这种方式，从低车辆需求位置到产生高车辆需求请求的位置，车辆的过度使用量减少到生成高车辆需求请求的位置。此外，还设计了一种新的增强方法，以便在车载车辆和乘客之间建立通信，以基于实时传感器即时预留座椅。为了实现公共交通系统的有效使用，提出了一种有效的动态调度算法，该算法在复杂的运输系统中推动了更方便的旅行。使用实时传输数据评估所提出的系统结果，该数据由主要城市收集，并实施以预测确切的车辆需求。将性能结果与各种现有方法进行比较，拟议的系统已证明其效率比现有方法。当拟议的制度实施时，它可以提高公共交通的87％，以及出租车的使用情况和自有车辆在城市中会减少。

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