PedNStream: Scalable Network Flow Simulation for Pedestrian Traffic Management

2026-07-01

AI Summary

This study addresses the challenge that existing macroscopic pedestrian simulation tools struggle to simultaneously achieve the computational efficiency required for large-scale crowd management and compatibility with closed-loop control. Building upon the Link Transmission Model (LTM), this work introduces, for the first time, stochastic dynamics incorporating diffusion and activity variability, and replaces conventional dynamic user equilibrium with utility-driven route choice. The result is an open-source, natively Python-based, modular simulation framework capable of seamlessly integrating intervention strategies—such as gating, flow separation, and path guidance—within a closed-loop control architecture. The framework effectively reproduces real-world phenomena including queue formation, spillback, dissipation, and adaptive rerouting, offering a scalable and computationally efficient paradigm for pedestrian network control under uncertain intervention scenarios.

Abstract

Large-scale crowd management requires pedestrian simulations that are both computationally efficient and compatible with feedback-based control. However, most open-source tools are either microscopic or not designed for network-scale closed-loop evaluation. This paper presents PedNStream (Pedestrian Network Flow Simulation), an open-source, Python-native simulator for macroscopic pedestrian network loading based on the Link Transmission Model (LTM). The framework extends LTM-based pedestrian models by incorporating stochastic link dynamics that capture diffusion and activity-induced variability, and replaces dynamic user equilibrium route choice with a utility-based formulation suited to uncertain, intervention-driven settings. PedNStream is implemented as a modular framework with built-in controller interfaces for interventions such as gating, flow separation, and route guidance. We evaluate the framework in a staged manner. Synthetic scenarios verify key mechanisms, including queue formation, spillback, congestion dissipation, and adaptive rerouting. Real-network experiments assess large-scale behavior and consistency with observed pedestrian counts. A closed-loop case study demonstrates controller integration, and a runtime analysis quantifies scalability. These results establish PedNStream as an efficient and practical testbed for large-scale pedestrian network simulation and control.

Problem

• pedestrian simulation
• network-scale
• closed-loop control
• crowd management
• macroscopic modeling

Innovation

• Link Transmission Model
• stochastic pedestrian dynamics
• utility-based route choice
• closed-loop control
• scalable simulation