Advanced Intelligent Predictive Models for Urban Transportation

This book, Intelligent and Energy Aware Traffic Prediction System for Urban Transportation, emphasizes the predictive models of Big Data, genetic algorithm, and IoT with case studies. The book illustrates the predictive models with integrated fuel consumption models for smart and safe traveling. The text is a coordinated amalgamation of research contributions and industrial applications in the field of intelligent transportation systems. The advanced predictive models and research results were achieved with the case studies, deployed in real transportation environments. Features: Provides a smart traffic congestion avoidance system with an integrated fuel consumption model. Short-term and regular time interval traffic predictions are made. This is illustrated with a case study. Provides an efficient traffic light controller and deviation system in accordance with the traffic scenario. Provides an IoT-based intelligent transport systems in a global perspective. Provides an intelligent traffic light control system and ambulance control system. Provides a predictive framework that can handle the traffic on abnormal days, such as weekends and festival holidays. Gives various solutions and ideas for smart traffic development in smart cities. Focuses on advanced predictive models along with offering an efficient solution for smart traffic management system. Gives a brief idea of the available algorithms/techniques of ig ata, IoT, and genetic algorithm and guides in developing a solution for smart city applications. Offers a complete framework for ITS domain with the advanced concepts of Big Data analytics, genetic algorithm, and IoT. This book is primarily aimed at IT professionals. Undergraduates, graduates, and researchers in the area of computer science and information technology will also find this book useful.