Our cities are increasingly becoming a complex network of systems that are instrumented and interconnected providing an opportunity for better management of vital infrastructure. An “Internet of Things” comprising sensors and mobile devices all communicating with each other to enhance infrastructure capability and resilience. In transport, the convergence of physical and digital worlds is creating unprecedented opportunities to enhance the travel experience for millions of people every day. A key to the success of these systems is a good understanding of driver behaviour under the influence of travel information. This book extends previous work by presenting a new generation of Neural Agent (Neugent) models to describe drivers’ decisions and compliance with information. The new models enhance the capabilities of existing tools in modelling the behaviour of heterogeneous drivers and dealing with the vagueness inherent in driver decision making and the information received from sensors and the road environment. This book also describes the traffic simulation and practical applications of the new models and how they serve as a valuable tool for researchers and practitioners alike
***** A must read book for researchers and professionals interested in Intelligent Transportation Systems (ITS)
By Dr Cristina Olaverri Monreal, Technische Universität München, Germany – June 9, 2014
This book addresses drivers’ route choice and other factors such as traffic conditions and driver’s heterogeneity that have so far not been considered in software paradigms modeling driving behavior. Consequently, the authors propose new artificial intelligence techniques based on neural networks and fuzzy logic to model drivers in different contexts as heterogeneous individuals. This work provides important new insights in the implementation of algorithms for dynamic behavior models: it takes into account the daily drivers’ exposure to different in-vehicle sources of information that are fundamental in the decision making processes involved in the primary driving task. This book is intended for researchers and professionals interested in a broad scientific field that includes not only basic principles of artificial intelligence and traffic simulation, but also targeted fields of application, such as Intelligent Transportation Systems (ITS). The knowledge provided in the book can positively affect delivery costs, improving the quality of service, but from a global perspective it can also significantly reduce the environmental impact, leading to a better traffic management and control, to effectively utilize the full potential of the ITS concepts.
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***** A compelling work and an absolute essential reference for anyone involved with ITS
By David E. Pickeral, JD – Transportation Sector Leader at IBM, USA – June 12, 2014
To ITS insiders worldwide, Dr. Hussein Dia has been a familiar name for decades. Now he teams with Dr. Sakda Panwai to produce an innovative work that has impact far beyond the ITS community in addressing how all of this new technology interacts with what still is—and for likely a long time will remain—the most critical and yet most unpredictable aspect of the vehicle-highway ecosystem: The Driver. The essential theme of the book involves modelling and analysis of driver behaviour around processing the flow of information provided by ITS devices and systems and the choices they make based on that process. The book focuses on the Neural Agent or “Neugent” model of behaviour in exploring how the often inconsistent behaviour patterns of drivers are affected by incomplete information on traffic and other surrounding environmental conditions, and how “fuzzy logic” can enhance the overall effectiveness of response by accounting for such factors. The book is interspersed throughout with a wealth of source material. From compendium of references and citations from multiple academic and industry authorities going back more than 50 years in this field, to case studies and other real-world examples which test their hypothesis, to equations that map out the analysis behind their conclusions, Dia and Panwai ensure that the readers are able to clearly follow exactly where their conclusions are headed, and how they got there. A thorough and varied set of illustrations throughout the book both enhance the text, and can also stand on their own in providing visual backup for the conclusions. Although written as might be expected with a high degree of technical content, the clear and precise language and clear structure and flow of the material will allow a very broad audience—even those, like myself, who do not have deep technical specialisation in this field—to absorb the work’s key findings and conclusions. I predict this work will see much use both as a handbook for practitioners, as well as, I have no doubt as a core text for multiple courses teaching the next generation of smarter transport professionals around the world.
***** I recommend this book to professionals interested in route choice and behavioral modeling of drivers
By Dr Ana Lucia C. Bazzan, Associate Professor, Universidade Federal do Rio Grande do Sul, Brasil – August 7, 2014
More and more information about traffic is becoming available. On the one hand we all want to know about traffic status in order to plan our trips. On the other hand, this raises the question about what is the influence of such information on the behavior of drivers, especially regarding route choice. In this book, Hussein Dia, a well known researcher in this field, covers the topic of route choice under incomplete and/or vague or imperfect knowledge about traffic status (e.g., road conditions). It first introduces fundamental concepts and the motivation for this work, as well as surveys the literature on route choice behavior. This way, these two chapters function as a nice introduction to this area. Then it addresses the limitations of current models by using artificial intelligence techniques based on neural networks and fuzzy logic, as well as agent-based modeling in order to describe individual driver characteristics such as route preferences. The approach (called Neugent for fuzzy neural agents) is able to model how drivers behaviors comply with the information they have, how drivers differ in terms of tolerance to delays en route, as well as individual utility functions, which map various attributes (belonging to four classes) to a route choice by each agent. The book not only describes this approach in detail, but also discusses the data used to validate it in application scenarios. In particular, one chapter of the book is dedicated to a web-based driver behavioral survey for collecting data to support the agent-based modeling. Such surveys are important not only for the development of the framework but also to calibrate and validate the simulations that result from the modeling phase. As such, this project is an example of best practice in the area of agent-based modeling and simulation in the domain of traffic and transportation.
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***** The book provides a new perspective in understanding and modelling driver route choice behaviour under traffic information
By Professor LEE Der-Horng, Department of Civil Engineering and Environmental Engineering, National University of Singapore – August 11, 2014
An innovative model framework, the Fuzzy-Neugent, is introduced in this book to describe driver route choice and compliance with traffic information which is a core in the development and implementation of Intelligent Transportation Systems (ITS). By combining artificial neural network with fuzzy logic, the authors successfully illustrate that the new model outperforms its counterpart, discrete choice model with higher prediction accuracy and in the meantime maintains the model interpretability. A spectrum of fuzzy-neural models which include 1) a driver compliance model indicating how drivers compliant with the network information, 2) a drivers delay tolerance threshold model to determine how drivers respond to delays with different duration, and 3) a route choice model reflecting drivers route choice decisions under different route attributes, personal attributes, trip attributes and network attributes. These models have been developed using data collected from a web-based driver behavioural survey. The models are interfaced and evaluated with commercial traffic simulator, AIMSUN NG, to demonstrate the additional benefits that can be obtained using the new models over what can be obtained using existing route choice models which do not take into account travel behaviour under the influence of traffic information. The simulation results show good improvements in network speeds and large time-saving for compliance drivers. This innovative model framework addressed two limitations of the existing models: drivers are homogeneous and all drivers are assumed to have perfect knowledge of the network. The developed driver behaviour models would benefit regional transportation authorities by (1) evaluating and quantifying the impacts of ITS applications, (2) providing simulation-validated guidance in incident management, (3) modelling emission and fuel consumption, and (4) supplying drivers with route guidance.
***** I believe there are very few scientific books which are that enthralling
By Dr Ralf Schleiffer, Supply Chain Director, Germany – August 12, 2014
This book is refreshingly different to other scientific texts. It does neither promote a single approach of which the authors believe it is superior to any other one nor does it leave the reader alone when it comes to detail. This book offers a cooking recipe for those who wish to work on ITS. It provides all those ingredients the reader needs to set-up state of the art microscopic traffic simulation and to understand macroscopic system behavior. The authors guide along all steps from capturing real world individual behavior and decision making towards implementing it into a computer system. In each step they discuss various approaches which due to their individual strengths and weaknesses are appropriate for specific tasks. Their central theme in this book is to implement a computer simulation that enables deriving optimized ATIS message provision for a real-world road network. While exploring the book along this theme the reader learns about survey methods using the Internet, about methodologies transforming and calibrating linguistic terms into fuzzy mathematics, about learning within artificial neural networks, about encapsulating decision making behavior into software agents, about setting-up a microscopic traffic simulation and interfacing towards existing simulators, … The outcome is a state-of-the-art dish the reader is ready to prepare by himself. As such the book is recommendable for professionals in ITS as well as it is for novice. Once I started reading it was impossible to set this book aside and I devoured it in less than two days. I believe there are very few scientific books which are that enthralling. Enjoy.
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***** A sensible treatment of a timely topic
By Dr Ghassan Abu-Lebdeh, Associate Professor (Civil Engineering), American University of Sharjah – September 15, 2014
This is a book about a timely subject with judicious treatment of its intricate technicalities and practical aspects– all in one. It fills an old void in dynamic traffic assignment where the cyclical relationship between real-time traveler information and user reactions was not adequately treated. Two key current limitations in route choice models, the assumption of homogeneous driver population and the assumption of perfect road and traffic knowledge, are treated well in the book. Despite the complexity of the subject, the clarity of the material and the ease with which the technical details are presented is noteworthy. The reader is well served by finding all relevant components of the subject (data acquisition and synthesis, modeling, evaluation and validation of results) all in one stop. Capturing the complexities inherent in human behavior (travel/route choice decisions) and natural vagueness of potential responses to information is no simple task. The use of both neural networks and fuzzy systems for that is befitting; the realism and intuition enabled by both computational/modeling paradigms add credibility and ease to a subject where capturing and understanding the complexities of travelers’ decision making in the presence of real time information is key. This book is for both practitioners and researchers who are specifically engaged in understanding, modeling, and evaluation of the interactions between new communication technologies and users’ route choices. The models were developed, calibrated, and validated based on field data obtained through web-based surveys. The accuracy of the models’ outcomes is high enough to lend much confidence to the utility of those models. Given the structure and formulation of the models, their implementation in different locals is within reach with simple investment in surveys to collect local drivers’ particulars. No one book can claim to capture all of any topic and do it perfectly, and this book is no exception. However, the relevance of topic, depth of analysis, suitability of data, and fitness and cohesion of the modeling approaches to the multiple pieces of the subject matters are all strengths of this book. It is a must read for all involved in traveler behavior, route assignment and ITS applications.
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***** An incredible contribution to understanding behaviour modelling in ITS settings
By Dr Rosaldo Rossetti (Assistant Professor, Department of Informatics Engineering and Senior Research Fellow, Artificial Intelligence and Computer Science Lab), University of Porto – December 25, 2014
For the Intelligent Transportation Systems (ITS) in the era of Smart Cities, understanding users’ preferences rather than solely focusing on their needs is crucial, not to say compulsory. This book represents a paramount contribution to practically understanding behaviour modelling in ITS settings! The methodological approach proposed paves the road for the “development of a new generation of dynamic behaviour models that can be used to describe drivers’ route choice decisions under the influence of traffic information.” Generally, it represents a wonderful interface bridging the huge gap between behaviour elicitation and the implementation of appropriate tools to foster behaviour assimilation, leveraging on robust mechanisms to practically encourage desired shifts in the behaviour of a population of drivers through information systems towards the overall improvement of the system. More specifically, the flexibility of the Fuzzy-Neural driver behaviour models (or Fuzzy-Neugent models as coined by the authors) to be integrated in traffic simulation frameworks allows for the impacts of ITS applications to be evaluated on a solid, scientifically robust and sound basis. Also, the structure of the book is suitable for different audiences as it spans the body of knowledge on choice under influence of information quite nicely. Educators may find it useful as a textbook introducing areas such as behaviour modelling, decision-making and choice under uncertainty, as well as modelling heterogeneous individuals using AI techniques based on neural networks and fuzzy logic. The scientific community will certainly benefit from the robust methodological framework presented in the book as an invaluable reference for modelling and understanding behaviour. Finally, practitioners will have a handy roadmap to devise and evaluate ITS solutions based on advanced traveller information systems to deploy appropriate incident management strategies. This book is an imperative reference to everyone interested in behaviour modelling, route choice, and ITS applications in general!
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