arXiv:2607.01021v1 Announce Type: new 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 c
The increasing scale and complexity of urban environments and large events necessitate more sophisticated crowd management tools, driving demand for efficient simulation methods.
This development allows for more accurate and scalable pedestrian flow simulations, which is critical for urban planning, event management, and emergency response, particularly in smart city contexts.
The introduction of an open-source, Python-native, macroscopic simulator for pedestrian networks provides a more accessible and adaptable tool for researchers and practitioners, improving upon existing, often limited, proprietary or microscopic solutions.
- · Urban planners
- · Event management companies
- · Smart city initiatives
- · Traffic engineering firms
- · Inefficient manual crowd management systems
- · Microscopic simulation tools for large-scale applications
- · Proprietary, closed-source simulation developers
Improved simulation tools lead to more robust pedestrian traffic management strategies.
Reduced congestion and enhanced safety in high-density areas and during large public gatherings.
Enhanced urban resilience and economic efficiency through better infrastructure utilization and crisis response planning.
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Read at arXiv cs.AI