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This book deals with the estimation of travel time in a very comprehensive and exhaustive way. Travel time information is and will continue to be one key indicator of the quality of service of a road network and a highly valued knowledge for drivers. Moreover, travel times are key inputs for comprehensive traffic management systems. All the above-mentioned aspects are covered in this book. The first chapters expound on the different types of travel time information that traffic management centers work with, their estimation, their utility and their dissemination. They also remark those aspects in which this information should be improved, especially considering future cooperative driving environments.Next, the book introduces and validates two new methodologies designed to improve current travel time information systems, which additionally have a high degree of applicability: since they use data from widely disseminated sources, they could be immediately implemented by many administrations without the need for large investments. Finally, travel times are addressed in the context of dynamic traffic management systems. The evolution of these systems in parallel with technological and communication advancements is thoroughly discussed. Special attention is paid to data analytics and models, including data-driven approaches, aimed at understanding and predicting travel patterns in urban scenarios. Additionally, the role of dynamic origin-to-destination matrices in these schemes is analyzed in detail.
A freeway construction work zone creates conflicts between vehicular traffic and work activity. The closure of one or more lanes of a freeway section causes a bottleneck on the freeway and reduces the capacity in the work zone, which can lead to conditions that violate the expectations of the motorists. Such a work zone situation is a challenge to one of the main objectives of a traffic management system, that is, to maintain "the safe and efficient movement of traffic." The advance warning area of a traffic control zone represents the area in which the motorists are informed as to what they can expect ahead. The information which is normally provided to the motorists include the type of construction activity, type of lane closure, extent of the work zone, and whether there are available alternate routes to avoid the construction all together. This information is given on static signs, or electronic portable changeable message signs (CMS) by way of static preprogrammed messages.
With the rapid growth of urban populations and increasing vehicular traffic, congestion has become a major challenge for transportation systems worldwide. Accurate estimation of travel time plays a crucial role in mitigating congestion and enhancing traffic management. This research focuses on developing a novel methodology that utilizes machine learning models to estimate travel time using real-time traffic data collected through Bluetooth sensors deployed at traffic intersections. The research compares five different prediction systems for replicating travel time estimation, evaluating their performance and accuracy. The results highlight the effectiveness of the machine learning models in accurately predicting travel time. Lastly, the research explores the creation of a model specifically designed to predict the travel time during peak hours, considering the impact of traffic lights on travel time between intersections. The findings of this study contribute to the development of efficient and reliable travel time prediction systems, enabling commuters to make informed decisions and improving traffic management strategies.
This book gathers together innovative research and practical findings relating to urban mobility transformation. It is especially intended to provide academicians, researchers, practitioners and decision makers with effective strategies and techniques that can support urban mobility in a sustainable way. The chapters, which report on contributions presented at the 5th Conference on Sustainable Urban Mobility, held virtually on June 17-19, 2020, from Greece, cover the thematic areas of: social networks and traveler behavior; applications of technologies in transportation and big data analytics; transport infrastructure and traffic management; and transportation modeling and impact assessment. Special attention is given to public transport and demand responsive systems, electromobility, micromobility and automated vehicles. The book addresses the challenges of the near future, highlighting the importance of knowledge transfer, and it is intended to foster communication among universities, industries and public administration.
This book constitutes the proceedings of the 6th International Conference on Intelligent Transport Systems, INTSYS 2022, which was held in Lisbon, Portugal, in December 15-16, 2022. With the globalization of trade and transportation and the consequent multi-modal solutions used, additional challenges are faced by organizations and countries. Intelligent Transport Systems make transport safer, more efficient, and more sustainable by applying information and communication technologies to all transportation modes. The 15 revised full papers in this book were selected from 45 submissions and are organized in three thematic sessions on smart city; transportation modes and AI; intelligent transportation and electric vehicles.
Database and information systems technologies have been rapidly evolving in several directions over the past years. New types and kinds of data, new types of applications and information systems to support them raise diverse challenges to be addressed. The so-called big data challenge, streaming data management and processing, social networks and other complex data analysis, including semantic reasoning into information systems supporting for instance trading, negotiations, and bidding mechanisms are just some of the emerging research topics. This volume contains papers contributed by six workshops: ADBIS Workshop on GPUs in Databases (GID 2012), Mining Complex and Stream Data (MCSD'12), International Workshop on Ontologies meet Advanced Information Systems (OAIS'2012), Second Workshop on Modeling Multi-commodity Trade: Data models and processing (MMT'12), 1st ADBIS Workshop on Social Data Processing (SDP'12), 1st ADBIS Workshop on Social and Algorithmic Issues in Business Support (SAIBS), and the Ph.D. Consortium associated with the ADBIS 2012 conference that report on the recent developments and an ongoing research in the aforementioned areas.