Measuring Road Safety Using Surrogate Events provides researchers and practitioners with the tools they need to quickly and effectively measure traffic safety. As traditional crash-based safety analyses are being undermined by today's growing use of intelligent vehicular and road safety technologies, crash surrogates--or near misses--can be more effectively used to measure the future risk of crashes. This book advances the idea of using these near-crash techniques to deliver quicker and more adequate measurements of safety. It explores the relationships between traffic conflicts and crashes using an extrapolation of observed events rather than post-crash data, which is significantly slower to obtain. Readers will find sound estimation methods based on rigorous scientific principles, offering compelling new tools to better equip researchers to understand road safety and its factors. - Consolidates the latest updates/ideas from disparate places into a single resource - Establishes a consistent use of key terms, definitions and concepts to help codify this emerging field - Contains numerous application-oriented case studies throughout - Includes learning aids, such as chapter objectives, a glossary, and links to data used in examples

Highway Safety Analytics and Modeling comprehensively covers the key elements needed to make effective transportation engineering and policy decisions based on highway safety data analysis in a single. reference. The book includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating analysis results. It discusses the challenges of working with crash and naturalistic data, identifies problems and proposes well-researched methods to solve them. Finally, the book examines the nuances associated with safety data analysis and shows how to best use the information to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes. - Complements the Highway Safety Manual by the American Association of State Highway and Transportation Officials - Provides examples and case studies for most models and methods - Includes learning aids such as online data, examples and solutions to problems

Artificial Intelligence in Highway Safety provides cutting-edge advances in highway safety using AI. The author is a highway safety expert. He pursues highway safety within its contexts, while drawing attention to the predictive powers of AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. It is suitable for anyone interested in highway safety and AI and it provides an illuminating and accessible introduction to this fast-growing research trend. Material supplementing the book can be found at https://github.com/subasish/AI_in_HighwaySafety. It offers a variety of supplemental materials, including data sets and R codes.

This book is the tenth volume of a sub-series on Road Vehicle Automation, published as part of the Lecture Notes in Mobility. It gathers contributions to the Automated Road Transportation Symposium (ARTS 2022), held on July 18-21, 2022, in Garden Grove, USA, CA. Written by researchers, engineers and analysts from around the globe, this book offers a multidisciplinary perspectives on the opportunities and challenges associated with automating road transportation. It highlights innovative strategies, including public policies, infrastructure planning and automated technologies, which are expected to foster sustainable and automated mobility in the near future, thus addressing industry, government and research communities alike.

Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems (ITS), including the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. It presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies. All fundamentals/concepts presented in this book are explained in the context of ITS. Users will learn everything from the basics of different ITS data types and characteristics to how to evaluate alternative data analytics for different ITS applications. They will discover how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning. Data Analytics for Intelligent Transportation Systems will prepare an educated ITS workforce and tool builders to make the vision for safe, reliable, and environmentally sustainable intelligent transportation systems a reality. It serves as a primary or supplemental textbook for upper-level undergraduate and graduate ITS courses and a valuable reference for ITS practitioners. - Utilizes real ITS examples to facilitate a quicker grasp of materials presented - Contains contributors from both leading academic and commercial domains - Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications - Includes exercise problems in each chapter to help readers apply and master the learned fundamentals, concepts, and techniques - New to the second edition: Two new chapters on Quantum Computing in Data Analytics and Society and Environment in ITS Data Analytics

In today's tumultuous and fast-changing times, digitalisation and technology are game changers in a wide range of sectors and have a tremendous impact on infrastructure. Roads, railways, electricity grids, aviation, and maritime transport are deeply affected by the digital and technological transition, with gains in terms of competitiveness, cost-reduction, and safety. Digitalisation is also a key tool for fostering global commitment towards sustainability, but the race for digital infrastructure is also a geopolitical one. As the world's largest economies are starting to adopt competitive strategies, a level playing field appears far from being agreed upon. Why are digitalisation and technology the core domains of global geopolitical competition? How are they changing the way infrastructure is built, operated, and maintained? To what extent will road, rail, air, and maritime transport change by virtue of digitalisation, artificial intelligence, and the Internet of Things? How to enhance cyber protection for critical infrastructure? What are the EU's, US' and China's digital strategies?

What challenges do pedestrians and cyclists face in cities of the developing world? What opportunities do these cities have to provide for walking and cycling? Based on in-depth research conducted in Cape Town (South Africa), Dar es Salaam (Tanzania) and Nairobi (Kenya), this book explores these questions by presenting work on walking and cycling travel behaviour, the status of road safety in these cities, as well as an analysis of the infrastructure for walking and cycling, and the workings of the institutions responsible for planning for these modes. The book also presents case studies relating to particular opportunities and challenges, such as the development and evaluation of ‘walking bus’ interventions, and the opportunities micro-simulation of pedestrian interventions offers within a data-scarce environment. Non-motorized Transport Integration into Urban Transport Planning in Africa demonstrates that transport and urban planning remains situated in a logic of automobile-dependent transport planning and global city development. This logic of practice does not pay adequate attention to walking and cycling. It argues that a significant shift in both policy as well as political commitment is needed so as to prioritize walking and cycling as strategies for sustainable transport policy in urban Africa. This book will be a key text for practitioners and policy makers working in planning, transport policy and urban development in Africa, as well as students and scholars of African studies, development studies, urban geography, transport studies and sustainable development.

This book increases the level of knowledge on road safety contexts, issues and challenges; shares what can currently be done to address the variety of issues; and points to what needs to be done to make further gains in road safety.

This book gathers contributions on a variety of flowing collective systems. While primarily focusing on pedestrian dynamics, it also reflects the latest developments in areas such as vehicular traffic and granular flows and addresses related emerging topics such as self-propelled particles, data transport, swarm behaviour, intercellular transport, and individual interactions to complex systems. Combining fundamental research and practical applications in the various fields discussed, the book offers a valuable asset for researchers and professionals in areas such as civil and transportation engineering, mechanical engineering, electrical engineering, physics, computer science, and mathematics.

Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts