Advanced Intelligent Predictive Models for Urban Transportation

Advanced intelligent predictive models for urban transportation emphasizes the predictive models of Big Data, Genetic Algorithm, and IoT with a case study. It 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 inreal transportation environments. Features: provides a smart traffic congestion avoidance system with an integrated fuel consumption model, predicts traffic in short-term and regular (this is illustrated with a case study), explores efficient traffic light controller and deviation system in accordance with the traffic scenario, considers IoT based Intelligent Transport Systems in a Global perspective and intelligent Traffic Light Control System and Ambulance Control System. The text also provides a predictive framework that can handle the traffic on abnormal days, such as weekends, festival holidays. In addition to that, this book focuses on advanced predictive models along with offering an efficient solution for smart traffic management systems. It is acomplete framework for ITS domain with the advanced concepts of Big Data Analytics, Genetic Algorithm and IoT. Advanced intelligent predictive models for urban transportation 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--