Complex Dynamics Of Traffic Management

This volume in the Encyclopedia of Complexity and Systems Science (ECSS) covers such fascinating and practical topics as (i) Vehicular traffic flow theory, (ii) Studies of real field traffic data, (iii) Complex phenomena of self-organization in vehicular traffic, (iv) Effect of automatic driving (self-driving vehicles) on traffic flow, v) Complex dynamics of city traffic, (vi) Dynamic control and optimization of traffic and transportation networks, including dynamic traffic assignment in the network, (vii) Pedestrian traffic, (viii) Evacuation scenarios, and (ix) Network characteristics of air control. Review articles are written by international experts covering the diverse and complex dynamics of traffic management. Topics new to the Second Edition of ECSS include microscopic traffic flow models, self-driving, complex dynamics of bus, tram and elevator delays, and breakdown minimization.

The understanding of empirical traf?c congestion occurring on unsignalized mul- lane highways and freeways is a key for effective traf?c management, control, or- nization, and other applications of transportation engineering. However, the traf?c ?ow theories and models that dominate up to now in transportation research journals and teaching programs of most universities cannot explain either traf?c breakdown or most features of the resulting congested patterns. These theories are also the - sis of most dynamic traf?c assignment models and freeway traf?c control methods, which therefore are not consistent with features of real traf?c. For this reason, the author introduced an alternative traf?c ?ow theory called three-phase traf?c theory, which can predict and explain the empirical spatiot- poral features of traf?c breakdown and the resulting traf?c congestion. A previous book “The Physics of Traf?c” (Springer, Berlin, 2004) presented a discussion of the empirical spatiotemporal features of congested traf?c patterns and of three-phase traf?c theory as well as their engineering applications. Rather than a comprehensive analysis of empirical and theoretical results in the ?eld, the present book includes no more empirical and theoretical results than are necessary for the understanding of vehicular traf?c on unsignalized multi-lane roads. The main objectives of the book are to present an “elementary” traf?c ?ow theory and control methods as well as to show links between three-phase traf?c t- ory and earlier traf?c ?ow theories. The need for such a book follows from many commentsofcolleaguesmadeafterpublicationofthebook“ThePhysicsofTraf?c”.

Investigates the nature and history of dynamic processes essential to understanding the need for flexibility and adaptability as well as the requirements to improve solutions.

This book addresses the reader interested in vehicular traffic phenomena, who have not learned about them before. It presents traffic phenomena like traffic breakdown and the emergence of moving traffic jams by showcasing empirical traffic data measured in real-world traffic. The author explains how these empirical traffic studies have led to the three-phase traffic theory and why this new theory is in conflict with standard traffic theories developed before. Moreover, he presents the reason for the failure of applications of standard traffic theories in real-world traffic and discusses why understanding real traffic has caused a paradigm shift in traffic and transportation science. The book examines why understanding real traffic breakdown is the basis for an explanation for the autonomous driving effects on traffic flow. It shows that understanding real traffic is possible from real-world traffic data without the need of mathematical traffic models. This makes the book intuitive for non-specialists, who can qualitatively understand all the basic features of traffic dynamics. In turn, experienced traffic researchers can grasp concepts and ideas made here easily accessible by the author, one of the leading pioneers in the field of vehicular traffic.

This book develops a methodology for designing feedback control laws for dynamic traffic assignment (DTA) exploiting the introduction of new sensing and information-dissemination technologies to facilitate the introduction of real-time traffic management in intelligent transportation systems. Three methods of modeling the traffic system are discussed: partial differential equations representing a distributed-parameter setting; continuous-time ordinary differential equations (ODEs) representing a continuous-time lumped-parameter setting; and discreet-time ODEs representing a discrete-time lumped-parameter setting. Feedback control formulations for reaching road-user-equilibrium are presented for each setting and advantages and disadvantage of using each are addressed. The closed-loop methods described are proposed expressly to avoid the counter-productive shifting of bottlenecks from one route to another because of driver over-reaction to routing information. The second edition of Feedback Control Theory for Dynamic Traffic Assignment has been thoroughly updated with completely new chapters: a review of the DTA problem and emphasizing real-time-feedback-based problems; an up-to-date presentation of pertinent traffic-flow theory; and a treatment of the mathematical solution to the traffic dynamics. Techinques accounting for the importance of entropy are further new inclusions at various points in the text. Researchers working in traffic control will find the theoretical material presented a sound basis for further research; the continual reference to applications will help professionals working in highway administration and engineering with the increasingly important task of maintaining and smoothing traffic flow; the extensive use of end-of-chapter exercises will help the graduate student and those new to the field to extend their knowledge.

Data-Driven Traffic Engineering: Understanding of Traffic and Applications Based on Three-Phase Traffic Theory shifts the current focus from using modeling and simulation data for traffic measurements to the use of actual data. The book uses real-world, empirically-derived data from a large fleet of connected vehicles, local observations and aerial observation to shed light on key traffic phenomena. Readers will learn how to develop an understanding of the empirical features of vehicular traffic networks and how to consider these features in emerging, intelligent transport systems. Topics cover congestion patterns, fuel consumption, the influence of weather, and much more. This book offers a unique, data-driven analysis of vehicular traffic in traffic networks, also considering how to apply data-driven insights to the intelligent transport systems of the future. Provides an empirically-driven analysis of traffic measurements/congestion based on real-world data collected from a global fleet of vehicles Applies Kerner’s three-phase traffic theory to empirical data Offers a critical scientific understanding of the underlying concerns of traffic control in automated driving and intelligent transport systems

Air traffic management (ATM) comprises a highly complex socio-technical system that keeps air traffic flowing safely and efficiently, worldwide, every minute of the year. Over the last few decades, several ambitious ATM performance improvement programmes have been undertaken. Such programmes have mostly delivered local technological solutions, whilst corresponding ATM performance improvements have fallen short of stakeholder expectations. In hindsight, this can be substantially explained from a complexity science perspective: ATM is simply too complex to address through classical approaches such as system engineering and human factors. In order to change this, complexity science has to be embraced as ATM's 'best friend'. The applicability of complexity science paradigms to the analysis and modelling of future operations is driven by the need to accommodate long-term air traffic growth within an already-saturated ATM infrastructure. Complexity Science in Air Traffic Management is written particularly, but not exclusively, for transport researchers, though it also has a complementary appeal to practitioners, supported through the frequent references made to practical examples and operational themes such as performance, airline strategy, passenger mobility, delay propagation and free-flight safety. The book should also have significant appeal beyond the transport domain, due to its intrinsic value as an exposition of applied complexity science and applied research, drawing on examples of simulations and modelling throughout, with corresponding insights into the design of new concepts and policies, and the understanding of complex phenomena that are invisible to classical techniques.

The problems of urban traffic in the industrially developed countries have been at the top of the priority list for a long time. While making a critical contribution to the economic well being of those countries, transportation systems in general and highway traffic in particular, also have detrimental effects which are evident in excessive congestion, high rates of accidents and severe pollution problems. Scientists from different disciplines have played an important role in the development and refinement of the tools needed for the planning, analysis, and control of urban traffic networks. In the past several years, there were particularly rapid advances in two areas that affect urban traffic: 1. Modeling of traffic flows in urban networks and the prediction of the resulting equilibrium conditions; 2. Technology for communication with the driver and the ability to guide him, by providing him with useful, relevant and updated information, to his desired destination.

This book focuses on modelling and simulation, control and optimization, signal processing, and forecasting in selected nonlinear dynamical systems, presenting both literature reviews and novel concepts. It develops analytical or numerical approaches, which are simple to use, robust, stable, flexible and universally applicable to the analysis of complex nonlinear dynamical systems. As such it addresses key challenges are addressed, e.g. efficient handling of time-varying dynamics, efficient design, faster numerical computations, robustness, stability and convergence of algorithms. The book provides a series of contributions discussing either the design or analysis of complex systems in sciences and engineering, and the concepts developed involve nonlinear dynamics, synchronization, optimization, machine learning, and forecasting. Both theoretical and practical aspects of diverse areas are investigated, specifically neurocomputing, transportation engineering, theoretical electrical engineering, signal processing, communications engineering, and computational intelligence. It is a valuable resource for students and researchers interested in nonlinear dynamics and synchronization with applications in selected areas.

This edited book focuses on recent developments in Dynamic Network Modeling, including aspects of route guidance and traffic control as they relate to transportation systems and other complex infrastructure networks. Dynamic Network Modeling is generally understood to be the mathematical modeling of time-varying vehicular flows on networks in a fashion that is consistent with established traffic flow theory and travel demand theory. Dynamic Network Modeling as a field has grown over the last thirty years, with contributions from various scholars all over the field. The basic problem which many scholars in this area have focused on is related to the analysis and prediction of traffic flows satisfying notions of equilibrium when flows are changing over time. In addition, recent research has also focused on integrating dynamic equilibrium with traffic control and other mechanism designs such as congestion pricing and network design. Recently, advances in sensor deployment, availability of GPS-enabled vehicular data and social media data have rapidly contributed to better understanding and estimating the traffic network states and have contributed to new research problems which advance previous models in dynamic modeling. A recent National Science Foundation workshop on “Dynamic Route Guidance and Traffic Control” was organized in June 2010 at Rutgers University by Prof. Kaan Ozbay, Prof. Satish Ukkusuri , Prof. Hani Nassif, and Professor Pushkin Kachroo. This workshop brought together experts in this area from universities, industry and federal/state agencies to present recent findings in this area. Various topics were presented at the workshop including dynamic traffic assignment, traffic flow modeling, network control, complex systems, mobile sensor deployment, intelligent traffic systems and data collection issues. This book is motivated by the research presented at this workshop and the discussions that followed.