A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians' intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase driving safety. Based on the image sequence collected by onboard monocular cameras, we use the Long Short-Term Memory (LSTM) based network with an enhanced attention mechanism to realize the intention and trajectory prediction of pedestrians and surrounding vehicles. However, although the fully automatic driving era still seems far away, human drivers are still a crucial part of the road‒driver‒vehicle system under current circumstances, even dealing with low levels of automatic driving vehicles. Considering that more than 90 percent of fatal traffic accidents were caused by human errors, thus it is meaningful to recognize the secondary task while driving, as well as the driving style recognition, to develop a more personalized advanced driver assistance system (ADAS) or intelligent vehicle. We use the graph convolutional networks for spatial feature reasoning and the LSTM networks with the attention mechanism for temporal motion feature learning within the image sequence to realize the driving secondary-task recognition. Moreover, aggressive drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Thus, we will focus on the driving style classification for the longitudinal car-following scenario. Also, based on the Structural Equation Model (SEM) and Strategic Highway Research Program 2 (SHRP 2) naturalistic driving database, the relationships among drivers' demographic characteristics, sensation seeking, risk perception, and risky driving behaviors are fully discussed. Results and conclusions from this short book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.

The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations. The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.

The book contains 35 chapters, in which you can find various examples of the development of methods and/or systems supporting medical diagnostics and therapy, related to biomedical imaging, signal and image processing, biomaterials and artificial organs, modelling of biomedical systems, which were presented as current research topics at the 23rd Polish Biocybernetics and Biomedical Engineering Conference, held at the Institute of Electronics, Lodz University of Technology in September 2023. The ongoing and dynamic development of AI-based data processing and analysis methods plays an increasingly important role in medicine. This book addresses these issues by presenting applications of such methods in various areas, such as disease diagnosis and prediction, particularly through the use of image data analysis algorithms. Other topics covered include personalized medicine, where multimodal patient data is acquired and analyzed, as well as robotic surgery and clinical decision support. The book is of interest to an advanced and broad readership, including researchers and engineers representing both medical, biological, and engineering viewpoints. Its readers may also be graduate and postgraduate students in various fields such as biomedical engineering, artificial intelligence, biomaterials, and medical electronics, as well as software developers in R&D departments working in the field of intelligent healthcare engineering.

Given its effective techniques and theories from various sources and fields, data science is playing a vital role in transportation research and the consequences of the inevitable switch to electronic vehicles. This fundamental insight provides a step towards the solution of this important challenge. Data Science and Simulation in Transportation Research highlights entirely new and detailed spatial-temporal micro-simulation methodologies for human mobility and the emerging dynamics of our society. Bringing together novel ideas grounded in big data from various data mining and transportation science sources, this book is an essential tool for professionals, students, and researchers in the fields of transportation research and data mining.

This book presents the new development of computation intelligence for traffic, transportation and mobility, the main contents include traffic safety, mobility analysis, intelligent transportation system, smart vehicle, transportation behavior, driver modeling and assistance, transportation risk analysis and reliability system analysis, vehicle operation and active safety, urban traffic management and planning.

Information and communication technology has helped to provide a more effective network infrastructure and development platform for logistics and service operations. In order to meet the needs of consumers, and particularly to promote low-carbon development processes, new types of services will also emerge. LISS 2012 is a prime international forum for both researchers and industry practitioners to exchange the latest fundamental advances in the state of the art and practice of logistics, informatics, service operations and service science. Experts and researchers from related fields will discuss current issues and future development opportunities, discuss and analyze developing trends and exchange the latest research and academic thought. The theme of the conference is Logistics and Service Science based on the Internet of Things.

This volume gives an overview of the trends in Traffic and Transport Psychology. It reflects the considerable development of the most important factors for driving a road vehicle, and the variety of international research approaches. The first part contains basic approaches and integrated models as well as general theories and their implementation into Traffic and Transport Psychology. The second part deals with the driver, especially cognition, performance, social and differential effects and impairment. Important aspects are treated, such as speed perception, reaction times, interaction, risk acceptance, aggression and gender differences. Special chapters refer to performance and fatigue. The third part focuses on safety, driver support, selection and influencing drivers by enforcement, training and programs for the rehabilitation of traffic offenders. Classic ergonomic methods are discussed as well as modern telematic devices, or trends regarding driver-assessment. In the last part, current developments are presented in relation to better mobility and the protection of the environment. Questions are asked, such as whether we could reduce the use of cars, how travel behaviour can be modified or to what extent the use of alternatives to motor vehicles benefits safety as well as the environment. This work is not only important for psychologists. It should be read by all transport professionals interested in the application of psychology to traffic.

The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not only being capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller.

This comprehensive textbook/reference provides an in-depth overview of the key aspects of transportation analysis, with an emphasis on modeling real transportation systems and executing the models. Topics and features: presents comprehensive review questions at the end of each chapter, together with detailed case studies, useful links, references and suggestions for further reading; supplies a variety of teaching support materials at the book’s webpage on Springer.com, including a complete set of lecture slides; examines the classification of models used for multimodal transportation systems, and reviews the models and evaluation methods used in transportation planning; explains traffic assignment to road networks, and describes computer simulation integration platforms and their use in the transportation systems sector; provides an overview of transportation simulation tools, and discusses the critical issues in the design, development and use of the simulation models.

This title was first published in 2003. Research on driver behaviour over the past two decades has demonstrated that driver characteristics, goals and motivations are important determinants of driving behaviour. We are now in a position to apply this knowledge to driver training programs and evaluate their effectiveness in improving safety. The main objective for the First International Conference on Driver Behaviour and Training and this book, is to describe and discuss recent advances in this field. The book bridges the gap between practitioners in road safety, and theoreticians investigating driving behaviour from a number of different perspectives and related disciplines. It will encourage research in driver training to combat erroneous or deviant driving behaviour and/or reduce the effects of human error at source. This book will be of interest to road safety researchers and road safety practitioners in the private and public sector.