Monitoring physical distancing for crowd management: real-time trajectory and group analysis

Abstract

Physical distancing, as a measure to contain the spreading of Covid-19, is defining a "new normal". Unless belonging to a family, pedestrians in shared spaces are asked to observe a minimal (country-dependent) pairwise distance. Coherently, managers of public spaces may be tasked with the enforcement or monitoring of this constraint. As privacy-respectful real-time tracking of pedestrian dynamics in public spaces is a growing reality, it is natural to leverage on these tools to analyze the adherence to physical distancing and compare the effectiveness of crowd management measurements. Typical questions are: "in which conditions non-family members infringed social distancing?", "Are there repeated offenders?", and "How are new crowd management measures performing?". Notably, dealing with large crowds, e.g. in train stations, gets rapidly computationally challenging. In this work we have a two-fold aim: first, we propose an efficient and scalable analysis framework to process, offline or in real-time, pedestrian tracking data via a sparse graph. The framework tackles efficiently all the questions mentioned above, representing pedestrian-pedestrian interactions via vector-weighted graph connections. On this basis, we can disentangle distance offenders and family members in a privacy-compliant way. Second, we present a thorough analysis of mutual distances and exposure-times in a Dutch train platform, comparing pre-Covid and current data via physics observables as Radial Distribution Functions. The versatility and simplicity of this approach, developed to analyze crowd management measures in public transport facilities, enable to tackle issues beyond physical distancing, for instance the privacy-respectful detection of groups and the analysis of their motion patterns.

12 pages

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Cited by in corpus (12)

• High-statistics pedestrian dynamics on stairways and their probabilistic fundamental diagrams
• Social aspects of collision avoidance: A detailed analysis of two-person groups and individual pedestrians
• Fluctuations in pedestrian dynamics routing choices
• Body and mind: Decoding the dynamics of pedestrians and the effect of smartphone distraction by coupling mechanical and decisional processes
• Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control
• Social Distance Characterization by means of Pedestrian Simulation
• Controlling Inter-Particle Distances in Crowds of Motile, Cognitive, Active Particles
• Identification of social groups and waiting pedestrians at railway platforms using trajectory data
• Stochastic fluctuations of diluted pedestrian dynamics along curved paths
• Model-based assessment of the risks of viral transmission in non-confined crowds
• HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection
• Avalanches of choice: how stranger-to-stranger interactions shape crowd dynamics