An improved public transportation system for effective usage of vehicles in intelligent transportation system

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.