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.
Highlights
Crowd management is a challenging scientific topic directly impacting on the functioning of trafficked urban infrastructures such as, e.g., train or metro stations
We focus on contact times and mutual distances considering statistical observables as the radial distribution functions (RDFs), which can conveniently be employed to quantify average exposure times
In theoretical physics and molecular dynamics, the radial distribution function, g(r) (RDF), and the radial cumulative distribution function (RCDF), G(r), are established tools to characterize the distribution of pairwise distances between particles, i.e., in our case, pedestrians
Summary
Crowd management is a challenging scientific topic directly impacting on the functioning of trafficked urban infrastructures such as, e.g., train or metro stations. Large-volumes of experimental data, in the order of hundred of thousands real-life trajectories, are essential in order to analyze quantitatively and systematically the physics of pedestrian motion, disentangling the high variations in individual behaviors from average patterns, and characterizing typical fluctuations and universal features [19, 20]. Monitoring physical distancing for crowd management: Real-time trajectory and group analysis pedestrians, whereas edges underlie distance-based interactions, that are characterized by a weight function with values in a real vector space of pre-fixed dimension.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have