Artificial Intelligence Applications To Traffic Engineering

In recent years the applications of advanced information technologies in the field of transportation have affected both road infrastructures and vehicle technologies. The development of advanced transport telematics systems and the implementation of a new generation of technological options in the transport environment have had a significant impact on improved traffic management, efficiency and safety. This volume contains contributions from scientific and academic centres which have been active in this field of research and provides an overview of applications of AI technology in the field of traffic control and management. The topics covered are: -- current status of AI in transport -- AI applications in traffic engineering -- in-vehicle AI

With AI advancements eliciting imminent changes to our transport systems, this enlightening Handbook presents essential research on this evolution of the transportation sector. It focuses on not only urban planning, but relevant themes in law and ethics to form a unified resource on the practicality of AI use.

Building effective and user-friendly transportation systems is one of the big challenges for engineers in the 21st century. There is an increasing need to understand, model, and govern such systems at both, the individual and the society level. Traffic and transportation scenarios are extraordinarily appealing for Distributed Artificial Intelligence, and (multi-)agent technology in particular. This book gives an overview of recent advances in agent-based transportation systems.

Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle challenging transportation problems. This textbookis designed for college or graduate-level students in transportation or closely related fields to study and understand fundamentals in machine learning. Readers will learn how to develop and apply various types of machine learning models to transportation-related problems. Example applications include traffic sensing, data-quality control, traffic prediction, transportation asset management, traffic-system control and operations, and traffic-safety analysis. Introduces fundamental machine learning theories and methodologies Presents state-of-the-art machine learning methodologies and their incorporation into transportationdomain knowledge Includes case studies or examples in each chapter that illustrate the application of methodologies andtechniques for solving transportation problems Provides practice questions following each chapter to enhance understanding and learning Includes class projects to practice coding and the use of the methods

Transportation typically entails crucial “life-death” choices, delegating crucial decisions to an AI algorithm without any explanation poses a serious threat. Hence, explainability and responsible AI is crucial in the context of intelligent transportation. In Intelligence Transportation System (ITS) implementations such as traffic management systems and autonomous driving applications, AI-based control mechanisms are gaining prominence. Explainable artificial intelligence for intelligent transportation system tackling certain challenges in the field of autonomous vehicle, traffic management system, data integration and analytics and monitor the surrounding environment. The book discusses and inform researchers on explainable Intelligent Transportation system. It also discusses prospective methods and techniques for enabling the interpretability of transportation systems. The book further focuses on ethical considerations apart from technical considerations.

Machine Learning Applications in Civil Engineering discusses machine learning and deep learning models for different civil engineering applications. These models work for stochastic methods wherein internal processing is done using randomized prototypes. The book explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency. It introduces Machine Learning and its applications to different Civil Engineering tasks, including Basic Machine Learning Models for data pre-processing, models for data representation, classification models for Civil Engineering Applications, Bioinspired Computing models for Civil Engineering, and their case studies. Using this book, civil engineering students and researchers can deep dive into Machine Learning, and identify various solutions to practical Civil Engineering tasks. Introduces various ML models for Civil Engineering Applications that will assist readers in their analysis of design and development interfaces for building these applications Reviews different lacunas and challenges in current models used for Civil Engineering scenarios Explores designs for customized components for optimum system deployment Explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency

In recent years, the application of intelligent transportation systems (ITS) has steadily expanded, and has become a hot spot of common interest to universities, scientific research institutes, enterprises and institutions in the transportation field. ITS is the product of the deep integration of modern high-tech in the transportation industry, and its development has accompanied that of modern high-tech. ITS is now also becoming part of the Internet of Things (IoT), and is expected to contribute significantly to making our cities smarter and connecting with other infrastructure. Although there are many monographs and textbooks on intelligent transportation, with the advancement of technology and changes in demand, the key technologies of ITS are also rapidly changing. This book chiefly focuses on the main technologies of ITS, examining them from four perspectives: “sense” (perception and management of traffic information, chapters 2 & 3), “transmission” (interaction of traffic information, chapter 4), “prediction” (prediction of traffic states, chapter 6) and “application” (intelligent transportation applications, chapters 6 through 10). Given its scope, the book can be used as a textbook for undergraduates or graduates, as well as a reference book for research institutes and enterprises. This book emphasizes the use of basis traffic engineering principles and state-of-art methodologies to develop functional designs. It largely reflects the authors’ own experience in adapting these methodologies to ITS design. For example, the book addresses various forms of data collection, models used to predict and evaluate traffic states, comprehensive description in connected vehicles, applications for users and traffic managers, etc. The knowledge gained here will allow designers to estimate the performance differences among alternatives and gauge their potential benefits for functional design purposes. To gain the most from the book, readers should be somewhat familiar with the field of traffic engineering and interested in ITS.