Advanced Intelligent Predictive Models For Urban Transportation
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Advanced Intelligent Predictive Models for Urban Transportation
The book emphasizes the predictive models of Big Data, Genetic Algorithm, and IoT with a case study. 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. Predicts traffic in short-term and regular. This is illustrated with a case study. Efficient Traffic light controller and deviation system in accordance with the traffic scenario. IoT based Intelligent Transport Systems in a Global perspective. Intelligent Traffic Light Control System and Ambulance Control System. Provides a predictive framework that can handle the traffic on abnormal days, such as weekends, festival holidays. Bunch of solutions and ideas for smart traffic development in smart cities. This book focuses on advanced predictive models along with offering an efficient solution for smart traffic management system. This book will give a brief idea of the available algorithms/techniques of big data, IoT, and genetic algorithm and guides in developing a solution for smart city applications. This book will be 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.
Urban Mobility and Challenges of Intelligent Transportation Systems
Intelligent Transportation Systems (ITS) are transforming urban mobility by integrating advanced technologies to improve traffic flow, safety, and sustainability. By leveraging data-driven solutions such as adaptive traffic signals, real-time monitoring, and smart parking, ITS reduces congestion and enhances commuter efficiency. These systems also play a crucial role in public safety, with applications like collision avoidance and emergency response coordination. Furthermore, ITS supports environmental sustainability by promoting public transportation and integrating with electric and autonomous vehicle technologies. As cities continue to grow, ITS offers a scalable and intelligent approach to building more efficient, safe, and eco-friendly transportation networks. Urban Mobility and Challenges of Intelligent Transportation Systems provides a comprehensive, up-to-date, and accessible resource that bridges the gap between theoretical concepts, practical applications, and emerging trends in ITS. It provides insights on the design and implementation of ITS for smart urban mobility. Covering topics such as artificial intelligence (AI), energy forecasting, and urban development, this book is an excellent resource for transportation professionals, academicians, policymakers, technology developers, and more.
Artificial Intelligence for Autonomous Vehicles
Author: Sathiyaraj Rajendran
language: en
Publisher: John Wiley & Sons
Release Date: 2024-02-27
With the advent of advanced technologies in AI, driverless vehicles have elevated curiosity among various sectors of society. The automotive industry is in a technological boom with autonomous vehicle concepts. Autonomous driving is one of the crucial application areas of Artificial Intelligence (AI). Autonomous vehicles are armed with sensors, radars, and cameras. This made driverless technology possible in many parts of the world. In short, our traditional vehicle driving may swing to driverless technology. Many researchers are trying to come out with novel AI algorithms that are capable of handling driverless technology. The current existing algorithms are not able to support and elevate the concept of autonomous vehicles. This addresses the necessity of novel methods and tools focused to design and develop frameworks for autonomous vehicles. There is a great demand for energy-efficient solutions for managing the data collected with the help of sensors. These operations are exclusively focused on non-traditional programming approaches and depend on machine learning techniques, which are part of AI. There are multiple issues that AI needs to resolve for us to achieve a reliable and safe driverless technology. The purpose of this book is to find effective solutions to make autonomous vehicles a reality, presenting their challenges and endeavors. The major contribution of this book is to provide a bundle of AI solutions for driverless technology that can offer a safe, clean, and more convenient riskless mode of transportation.