发表机构
Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于链路传输模型（LTM）的行人网络流模拟器PedNStream，通过随机链路动力学和效用路由选择实现大规模闭环控制评估，验证了队列形成、拥堵消散和自适应重路由等机制。
AI 中文摘要
大规模人群管理需要既计算高效又兼容反馈控制的行人模拟。然而，大多数开源工具要么是微观的，要么不适用于网络规模的闭环评估。本文提出PedNStream（行人网络流模拟），一个基于链路传输模型（LTM）的开源、Python原生宏观行人网络加载模拟器。该框架通过引入捕获扩散和活动诱导变异性的随机链路动力学扩展了基于LTM的行人模型，并用适用于不确定、干预驱动环境的效用公式替代了动态用户均衡路径选择。PedNStream实现为模块化框架，内置用于干预（如门控、流分离和路线引导）的控制器接口。我们分阶段评估该框架。合成场景验证了关键机制，包括队列形成、回溢、拥堵消散和自适应重路由。真实网络实验评估了大规模行为以及与观测行人计数的一致性。闭环案例研究展示了控制器集成，运行时分析量化了可扩展性。这些结果确立了PedNStream作为大规模行人网络模拟与控制的高效实用测试平台。
英文摘要
Evaluating operational crowd management at network scale requires simulations that can be run repeatedly while adapting interventions to changing conditions. Microscopic models can represent detailed individual movement, but their computational cost may limit their use in such repeated, network-scale evaluations. This paper presents PedNStream (Pedestrian Network Flow Simulation), an open-source, Python-native simulator for macroscopic pedestrian network simulation based on the Link Transmission Model (LTM). PedNStream extends LTM-based pedestrian models with stochastic link dynamics that represent local variation in pedestrian flow. It uses a utility-based route-choice model to capture how pedestrians adjust their route choices in response to congestion and control interventions as conditions change over time. The modular framework provides controller interfaces for gating, flow separation, and route guidance. We evaluate PedNStream in a staged manner. Synthetic scenarios verify key crowd-dynamics mechanisms, including queue formation, spillback, congestion dissipation, and adaptive rerouting. Real-network experiments assess large-scale behavior against observed pedestrian counts. A closed-loop case study demonstrates controller integration, and a runtime analysis quantifies scalability. These results position PedNStream as an efficient and practical testbed for large-scale pedestrian network simulation and crowd management research.
Comments14 pages, 14 figures
