Models For Vehicular Traffic On Networks

In spite of their importance and potential societal impact, there is currently no comprehensive source of information about vehicular ad hoc networks (VANETs). Cohesively integrating the state of the art in this emerging field, Vehicular Networks: From Theory to Practice elucidates many issues involved in vehicular networking, including traffic engineering, human factors studies, and novel computer science research. Divided into six broad sections, the book begins with an overview of traffic engineering issues, such as traffic monitoring and traffic flow modeling. It then introduces governmental and industrial efforts in the United States and Europe to set standards and perform field tests on the feasibility of vehicular networks. After highlighting innovative applications enabled by vehicular networks, the book discusses several networking-related issues, including routing and localization. The following section focuses on simulation, which is currently the primary method for evaluating vehicular networking systems. The final part explores the extent and impact of driver distraction with in-vehicle displays. Encompassing both introductory and advanced concepts, this guide covers the various areas that impact the design of applications for vehicular networks. It details key research challenges, offers guidance on developing future standards, and supplies valuable information on existing experimental studies.

This brief presents a stochastic microscopic mobility model that describes the temporal changes of intervehicle distances. The model is consistent with simulated and empirical vehicle traffic patterns. Using stochastic lumpability methods, the proposed mobility model is mapped into an aggregated mobility model that describes the mobility of a group of vehicles. In addition, the proposed mobility model is used to analyze the spatiotemporal VANET topology. Two metrics are proposed to characterize the impact of vehicle mobility on VANET topology: the time period between successive changes in communication link state (connection and disconnection) and the time period between successive changes in node’s one-hop neighborhood. Using the proposed lumped group mobility model, the two VANET topology metrics are probabilistically characterized for different vehicular traffic flow conditions. Furthermore, the limiting behavior of a system of two-hop vehicles and the overlap-state of their coverage ranges is modeled, and the steady-state number of common vehicle neighbors between the two vehicles is approximately derived. The proposed mobility model will facilitate mathematical analysis in VANETs. The spatiotemporal VANET topology analysis provides a useful tool for the development of mobility-aware vehicular network protocols. Mobility Modeling for Vehicular Communication Networks is designed for researchers, developers, and professionals involved with vehicular communications. It is also suitable for advanced-level students interested in communications, transport infrastructure, and infotainment applications.

Over the last few years vehicular networks have been receiving a lot of attention from academia, industry, standardization bodies, and the various transportation agencies and departments of many governments around the world. It is envisaged in the next decade that the Intelligent Transportation System (ITS) will become an essential part of our daily life. This book describes models and/or algorithms designed to investigate evolutionary solutions to overcome important issues such as congestion control, routing, clustering, interconnection with long-term evolution (LTE) and LTE advanced cellular networks, traffic signal control and analysis of performances through simulation tools and the generation of vehicular mobility traces for network simulations. It provides an up-to-date progress report on the most significant contributions carried out by the specialized research community in the various fields concerned, in terms of models and algorithms. The proposals and new directions explored by the authors are highly original, and a rather descriptive method has been chosen, which aims at drawing up complete states of the art as well as providing an overall presentation of the personal contributions brought by the authors and clearly illustrating the advantages and limitations as well as issues for future work. Contents 1. Introduction 2. Congestion Control for Safety Vehicular Ad-Hoc Networks 3. Inter-Vehicle Communication for the Next Generation of Intelligent Transport System: Trends in Geographic Ad Hoc Routing Techniques 4. CONVOY: A New Cluster-Based Routing Protocol for Vehicular Networks 5. Complementarity between Vehicular Networks and LTE Networks 6. Gateway Selection Algorithms in a Hybrid VANET-LTE Advanced Network 7. Synthetic Mobility Traces for Vehicular Networking 8. Traffic Signal Control Systems and Car-to-Car Communications About the Authors André-Luc Beylot is Professor in the Telecommunication and Network Department of the ENSEEIHT of IRIT-T, University of Toulouse in France. Houda Labiod is Associate Professor at Telecom ParisTech in the INFRES (Computer Science and Network) Department, France.

Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring Uses methods based on video and time series data for traffic modeling and forecasting Includes case studies, key processes guidance and comparisons of different methodologies

With the advancement of wireless technology, vehicular ad hoc networks (VANETs) are emerging as a promising approach to realizing "smart cities" and addressing many important transportation problems such as road safety, efficiency, and convenience. This brief provides an introduction to the large trace data set collected from thousands of taxis and buses in Shanghai, the largest metropolis in China. It also presents the challenges, design issues, performance modeling and evaluation of a wide spectrum of VANET research topics, ranging from realistic vehicular mobility models and opportunistic routing, to real-time vehicle tracking and urban sensing applications. In addition to the latest research and techniques, the reader will also learn the trace-driven methodologies and tools of performance modeling and analysis, network protocol design and optimization, and network simulation, thus keeping pace with the fast moving VANET research and development.

Intelligent Vehicular Network and Communications: Fundamentals, Architectures and Solutions begins with discussions on how the transportation system has transformed into today’s Intelligent Transportation System (ITS). It explores the design goals, challenges, and frameworks for modeling an ITS network, discussing vehicular network model technologies, mobility management architectures, and routing mechanisms and protocols. It looks at the Internet of Vehicles, the vehicular cloud, and vehicular network security and privacy issues. The book investigates cooperative vehicular systems, a promising solution for addressing current and future traffic safety needs, also exploring cooperative cognitive intelligence, with special attention to spectral efficiency, spectral scarcity, and high mobility. In addition, users will find a thorough examination of experimental work in such areas as Controller Area Network protocol and working function of On Board Unit, as well as working principles of roadside unit and other infrastructural nodes. Finally, the book examines big data in vehicular networks, exploring various business models, application scenarios, and real-time analytics, concluding with a look at autonomous vehicles. Proposes cooperative, cognitive, intelligent vehicular networks Examines how intelligent transportation systems make more efficient transportation in urban environments Outlines next generation vehicular networks technology