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World-wide trends such as globalization, demographic shifts, increased customer demands, and shorter product lifecycles present a significant challenge to the road freight transport industry: meeting the growing road freight transport demand economically while striving for sustainability. Artificial intelligence, particularly machine learning, is expected to empower transport planners to incorporate more information and react quicker to the fast-changing decision environment. Hence, using machine learning can lead to more efficient and effective transport planning. However, despite the promising prospects of machine learning in road freight transport planning, both academia and industry struggle to identify and implement suitable use cases to gain a competitive edge. In her dissertation, Sandra Lechtenberg explores how machine learning can enhance decision-making in operational and real-time road freight transport planning. She outlines an implementation guideline, which involves identifying decision tasks in planning processes, assessing their suitability for machine learning, and proposing steps to follow when implementing respective algorithms.
With a focus on cargo transportation, this book addresses the development of approaches intended to secure an infrastructure of smart services to support the adaptive implementation of online multi-modal freight transport management processes. It discusses the development of multi-criteria decision-making components and their integration into the multi-layered computer-based information management of intelligent systems. Through detailed descriptions of various components of intelligent transport management systems, the book demonstrates how to develop the services needed in the right place and at the right time, and how to properly adapt to user needs, making necessary interventions to ensure the safety of the transportation process. Further, it describes the main ways to increase the autonomy and efficiency of user-vehicle interaction and shows how Information and Communications Technology (ICT) structural support for current and past situations in AI-based systems can help to anticipate future developments in freight transportation.
Urban Freight Analytics examines the key concepts associated with the development and application of decision support tools for evaluating and implementing city logistics solutions. New analytical methods are required for effectively planning and operating emerging technologies including the Internet of Things (IoT), Information and Communication Technologies (ICT), and Intelligent Transport Systems (ITS). The book provides a comprehensive study of modelling and evaluation approaches to urban freight transport. It includes case studies from Japan, the US, Europe, and Australia that illustrate the experiences of cities that have already implemented city logistics, including analytical methods that address the complex issues associated with adopting advanced technologies such as autonomous vehicles and drones in urban freight transport. Also considered are future directions in urban freight analytics, including hyperconnected city logistics based on the Physical Internet (PI), digital twins, gamification, and emerging technologies such as connected and autonomous vehicles in urban areas. An integrated modelling platform is described that considers multiple stakeholders or agents, including emerging organisations such as PI companies and entities such as crowd-shippers as well as traditional stakeholders such as shippers, receivers, carriers, administrators, and residents. This book Presents procedures for evaluating city logistics technologies and policy measures Provides an overview of advanced modelling approaches, including agent-based model and machine learning Highlights the essential features of optimisation and simulation models applied to city logistics Discusses how models incorporating more uncertainty and dynamic data can be used to improve the sustainability and resilience of urban freight systems The book is ideal for graduate students in civil and environmental engineering and logistics management, urban planners, transport engineers, and logistics specialists.
This book represents the compilation of several research approaches on opera tional freight carrier planning carried out at the Chair of Logistics, University of Bremen. It took nearly three years from the first ideas to the final version, now in your hands. During this time, several persons helped me all the time to keep on going and to re-start when I got stuck in a dead end or when I could not see the wood for the trees. I am deeply indebted to them for their encouragement and comments. Prof. Dr. Herbert Kopfer, holder of the Chair of Logistics, introduced me into the field of operational transport planning. He motivated and supervised me. Furthermore, he supported me constantly and allowed me to be as free as possible in my research and encouraged me to be as creative as necessary. In addition, I have to thank Prof. Dr. Hans-Dietrich Haasis, Prof. Dr. Martin G. Mohrle and Prof. Dr. Thorsten Poddig. On behalf of all my colleagues, who supported me in numerous ways, I have to say thank you to Prof. Dr. Dirk C. Mattfeld, Prof. Dr. Christian Bierwirth, Henner Gratz, Prof. Dr. Elmar Erkens, Nadja Shigo and Katrin Dorow. They all helped me even with my most obscure and dubious problems. My family supported me all the time. They always showed me their trust and encouraged me continuously. Special thanks are dedicated to my parents Monika and Heinz-Jiirgen.
This book is designed as a popular science book on big data analytics in intelligent transportation systems. It aims to provide an introduction to big-data transportation starting from an overview on the development of big data transportation in various countries. This is followed by a discussion on the blueprint strategies of big data transportation which include innovative models, planning, transportation logistics, and application case studies. Finally, the book discusses applications of big data transportation platforms.
Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning. Includes case studies in each chapter that illustrate the application of concepts covered Presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies Contains contributors from both leading academic and commercial researchers Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications
This is a collection of state-of-the-art surveys on topics at the interface between transportation modeling and operations research given by leading international experts. Based on contributions to a NATO workshop, the surveys are up-to-date and rigorous presentations or applications of quantitative methods in the area. The subjects covered include dynamic traffic simulation techniques and dynamic routing in congested networks, operation and control of traffic management tools, optimized transportation data collection, and vehicle routing problems.
Two new dynamic planning approaches, incorporating all important real-life restrictions, such as regulations on driving and working hours, are developed and evaluated. Extensive numerical tests are carried out with a five-week real-life data set from an international freight forwarding company.
This book explores the many challenges faced by the development and implementation of automated freight transport systems. It offers a unique overview of current applications, developments and future perspectives. The subject of automation is not covered extensively in the existing literature on freight transport and this book aims to fill the gap.
The second edition of Multi-Objective Management in Freight Logistics builds upon the first, providing a detailed study of freight transportation systems, with a specific focus on multi-objective modelling. It offers decision-makers methods and tools for implementing multi-objective optimisation models in logistics. The second edition also includes brand-new chapters on green supply chain and hybrid fleet management problems. After presenting the general framework and multi-objective optimization, the book analyses green logistic focusing on two main aspects: green corridors and network design; next, it studies logistic issues in a maritime terminal and route planning in the context of hazardous material transportation. Finally, heterogeneous fleets distribution and coordination models are discussed. The book presents problems providing the mathematics, algorithms, implementations, and the related experiments for each problem. It offers a valuable resource for postgraduate students and researchers in transportation, logistics and operations, as well as practitioners working in service systems.