Matthias Winkenbach, Stefan Spinler, Julian Pachon, Karthik Konduri
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引用次数: 0
Abstract
In this paper, we introduce the Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems, which draws its inspiration from the academic community’s positive reception of the 2021 Amazon Last Mile Routing Research Challenge. We provide a structured overview of the papers featured in this special issue, and briefly discuss other noteworthy contributions to the research challenge. Further, we point the reader to a number of peer-reviewed publications outside of this special issue that directly or indirectly emerged from the research challenge. We conclude by highlighting a number of important priorities for future research into applications of machine learning to real-world route planning problems.
期刊介绍:
Transportation Science, published quarterly by INFORMS, is the flagship journal of the Transportation Science and Logistics Society of INFORMS. As the foremost scientific journal in the cross-disciplinary operational research field of transportation analysis, Transportation Science publishes high-quality original contributions and surveys on phenomena associated with all modes of transportation, present and prospective, including mainly all levels of planning, design, economic, operational, and social aspects. Transportation Science focuses primarily on fundamental theories, coupled with observational and experimental studies of transportation and logistics phenomena and processes, mathematical models, advanced methodologies and novel applications in transportation and logistics systems analysis, planning and design. The journal covers a broad range of topics that include vehicular and human traffic flow theories, models and their application to traffic operations and management, strategic, tactical, and operational planning of transportation and logistics systems; performance analysis methods and system design and optimization; theories and analysis methods for network and spatial activity interaction, equilibrium and dynamics; economics of transportation system supply and evaluation; methodologies for analysis of transportation user behavior and the demand for transportation and logistics services.
Transportation Science is international in scope, with editors from nations around the globe. The editorial board reflects the diverse interdisciplinary interests of the transportation science and logistics community, with members that hold primary affiliations in engineering (civil, industrial, and aeronautical), physics, economics, applied mathematics, and business.