This report outlines the concepts, mechanisms and inner dynamics of the BEAM (Behavior, Energy, Autonomy, and Mobility) modeling framework. BEAM is an open-source large-scale high-resolution transportation model that harnesses the principles of the actor model of computation to build a powerful and efficient agent-based model of travel behavior. It allows a detailed microscopic view of how people make travel choices and interact with the transportation system, enabling more accurate simulations of human mobility and urban transport networks. It also allows the analysis of numerous spatially defined but interacting layers, and integrates them into a cohesive representation of a regional transportation system. This integrated picture provides invaluable insights to policy makers and other stakeholders about how changes to the transportation system result in changes to traffic congestion, mode share, energy use, and emissions throughout a modeled region. These capabilities are demonstrated with a case study of New York City that showcase BEAM's application in a very large and intricate urban transportation system, without relying on existing travel demand models. The unique ability of BEAM to simulate individual behaviors, integrate with other models, and adapt to different real-world scenarios underscores its importance in the rapidly evolving field of transportation and emphasizes its potential as a valuable proof-of-concept tool to contribute to more informed and effective policy and planning decisions.

翻译：本报告概述了BEAM（行为、能源、自主性与移动性）建模框架的概念、机制和内部动态。BEAM是一个开源的大规模高分辨率交通模型，它利用计算角色的actor模型原理，构建了一个强大且高效的基于智能体的出行行为模型。该模型能够从微观层面详细观察人们如何做出出行选择以及如何与交通系统互动，从而实现对人类移动性和城市交通网络的更精确模拟。此外，它还能分析多个空间定义但相互作用的层面，并将它们整合成一个区域交通系统的统一表示。这种整合视角为政策制定者和其他利益相关者提供了宝贵的见解，帮助他们理解交通系统的变化如何影响整个模拟区域的交通拥堵、出行方式分担率、能源使用和排放。这些能力通过纽约市的案例研究得到了验证，展示了BEAM在不依赖现有出行需求模型的情况下，在极其庞大复杂的城市交通系统中的应用。BEAM模拟个体行为、与其他模型集成以及适应不同现实场景的独特能力，凸显了其在快速发展的交通领域中的重要性，并强调了其作为有价值的验证概念工具，为更明智、更有效的政策和规划决策做出贡献的潜力。