PedNStream: Scalable Network Flow Simulation for Pedestrian Traffic Management
2026-07-01 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors created PedNStream, a Python tool to simulate how large groups of pedestrians move through networks like city streets using a big-picture approach. Unlike many existing tools, PedNStream is designed to efficiently handle whole networks and work with control methods that respond to real-time changes. It models pedestrian behavior with some randomness to reflect real movement variability and uses a decision-making system for route choice suited to uncertain conditions. The authors tested PedNStream on simple and real-world networks, showing it can simulate crowd dynamics and adapt to control actions, while running quickly enough for large-scale use.
macroscopic pedestrian simulationLink Transmission Modelstochastic link dynamicsdynamic user equilibriumutility-based route choiceclosed-loop controlqueue formationspillbackflow separationroute guidance
Authors
Weiming Mai, Dorine Duives, Serge Hoogendoorn
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.