{"id":883,"date":"2022-10-18T14:07:36","date_gmt":"2022-10-18T14:07:36","guid":{"rendered":"https:\/\/crowdflow.phys.tue.nl\/wordpress\/?page_id=883"},"modified":"2022-10-18T14:58:33","modified_gmt":"2022-10-18T14:58:33","slug":"pedestrian-and-evacuation-dynamics-2021","status":"publish","type":"page","link":"https:\/\/crowdflow.phys.tue.nl\/wordpress\/pedestrian-and-evacuation-dynamics-2021\/","title":{"rendered":"PED21"},"content":{"rendered":"<div ><style id=\"sp_tab_dynamic_style885\">#sp-wp-tabs-wrapper_885 .sp-tab__nav {justify-content: start;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default.sp-tab__horizontal-bottom {display: flex;flex-direction: column-reverse;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default.sp-tab__horizontal-bottom > ul {border-top: 1px solid #020202;border-bottom: 0;margin-top: 0;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default.sp-tab__horizontal-bottom > ul > li label.sp-tab__active {border-color: transparent #020202#020202;margin-top: -1px;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default.sp-tab__horizontal-bottom > ul > li label,#sp-wp-tabs-wrapper_885.sp-tab__lay-default.sp-tab__horizontal-bottom > ul > li a,#sp-wp-tabs-wrapper_885.sp-tab__lay-default.sp-tab__horizontal-bottom > ul > .sp-tab__nav-item {border-top: 0;border-top-left-radius: 0;border-top-right-radius: 0;border-bottom-left-radius: 2px;border-bottom-right-radius: 2px;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default.sp-tab__horizontal-bottom > ul {border-bottom: none;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default.sp-tab__horizontal-bottom .sp-tab__tab-content .sp-tab__tab-pane {border-top: 1px solid #cccccc;border-bottom: 0;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default .sp-tab__tab-content .sp-tab-content > ul,#sp-wp-tabs-wrapper_885.sp-tab__lay-default .sp-tab__tab-content .sp-tab-content > ol {border-bottom: none;}#sp-wp-tabs-wrapper_885 > .sp-tab__nav-tabs > .sp-tab__nav-link.sp-tab__active .sp-tab__tab_title,#sp-wp-tabs-wrapper_885.sp-tab__lay-default > label .sp-tab__card-header {color: #444;}#sp-wp-tabs-wrapper_885 > .sp-tab__nav-tabs > .sp-tab__nav-item.show .sp-tab__nav-link,#sp-wp-tabs-wrapper_885 > .sp-tab__nav-tabs > .sp-tab__nav-item .sp-tab__nav-link.sp-tab__active,#sp-wp-tabs-wrapper_885.sp-tab__lay-default > label > .sp-tab__card-header {background-color: #ffffff;}#sp-wp-tabs-wrapper_885 .sp-tab__nav-tabs .sp-tab__nav-item.show .sp-tab__nav-link,#sp-wp-tabs-wrapper_885.sp-tab__lay-default ul li label.sp-tab__active {border-color: #020202#020202 transparent;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul > li > label,#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul > li > a {cursor: pointer;border-color: #020202;padding-top: 15px;padding-right: 15px;padding-bottom: 15px;padding-left: 15px;}#sp-wp-tabs-wrapper_885 > .sp-tab__nav-tabs .sp-tab__nav-link {border: 1px solid #020202;height: 100%;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul > li label,#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul > li a,#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul > .sp-tab__nav-item {border-top-left-radius: 2px;border-top-right-radius: 2px;}#sp-wp-tabs-wrapper_885 .sp-tab__nav-tabs .sp-tab__nav-item {margin-bottom: -1px; }#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul .sp-tab__nav-item,#sp-wp-tabs-wrapper_885.sp-tab__lay-default > label.collapsed .sp-tab__card-header {background-color: #a8a8a8;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul .sp-tab__nav-item {margin-right: 10px;margin-top: 5px;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul .sp-tab__nav-item:last-child {margin-right: 0;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul .sp-tab__nav-item label:hover .sp-tab__tab_title,#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul .sp-tab__nav-item a:hover .sp-tab__tab_title,#sp-wp-tabs-wrapper_885.sp-tab__lay-default label.collapsed .sp-tab__card-header:hover {color: #444;transition: .3s;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul .sp-tab__nav-item:hover,#sp-wp-tabs-wrapper_885.sp-tab__lay-default > label.collapsed .sp-tab__card-header:hover {background-color: #cecece;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > .sp-tab__tab-content .sp-tab__tab-pane {border: 1px solid #cccccc;padding-top: 20px;padding-right: 20px;padding-bottom: 20px;padding-left: 20px;border-top: 0px;background-color: #ffffff;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul {border-bottom: 1px solid #cccccc;}@media(max-width:480px) {#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul li.sp-tab__nav-item {width: 100%;margin-right: 0px;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul li.sp-tab__nav-item:last-child {margin-bottom: 1px;}}#poststuff h2.sp-tab__section_title_885, h2.sp-tab__section_title_885 ,.editor-styles-wrapper .wp-block h2.sp-tab__section_title_885{margin-bottom: 30px !important;font-weight: 600;font-style: normal;font-size: 28px;line-height: 28px;letter-spacing: 0px;color: #444444;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > ul .sp-tab__nav-item .sp-tab__tab_title,#sp-wp-tabs-wrapper_885.sp-tab__lay-default > label.collapsed .sp-tab__card-header {font-weight: 600;font-style: normal;font-size: 16px;line-height: 22px;letter-spacing: 0px;color: #444;margin: 0px;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > .sp-tab__tab-content .sp-tab__tab-pane {font-weight: 400;font-style: normal;font-size: 16px;line-height: 24px;letter-spacing: 0px;color: #444;}#sp-wp-tabs-wrapper_885.sp-tab__lay-default > .sp-tab__tab-content .sp-tab__tab-pane ul li a, #sp-wp-tabs-wrapper_885.sp-tab__lay-default .sp-tab__tab-content .sp-tab__tab-pane ol li a {color: #444;}<\/style>\t<h2 class=\"sp-tab__section_title_885\"> PED21 Contributions<\/h2>\n\t\t\t<div id=\"sp-wp-tabs-wrapper_885\" class=\"sp-tab__lay-default\" data-preloader=\"1\" data-activemode=\"tabs-activator-event-click\">\n\t\t\t\t<div class=\"sp-tab__preloader\">\n\t\t<div class=\"sp-tab__spinner\">\n\t\t\t<div class=\"rect1\"><\/div>\n\t\t\t<div class=\"rect2\"><\/div>\n\t\t\t<div class=\"rect3\"><\/div>\n\t\t\t<div class=\"rect4\"><\/div>\n\t\t\t<div class=\"rect5\"><\/div>\n\t\t<\/div>\n\t<\/div>\n\t<ul class=\"sp-tab__nav sp-tab__nav-tabs\" id=\"sp-tab__ul\" role=\"tablist\">\n\t\t\t\t<li class=\"sp-tab__nav-item\" role=\"navigation\">\n\t\t\t\t<label class=\"sp-tab__nav-link sp-tab__active\" data-sptoggle=\"tab\" for=\"#tab-8851\" role=\"tab\" aria-controls=tab-8851 aria-selected=false>\n\t\t\t\t\t<span class=\"tab_title_area\"><H6 class=\"sp-tab__tab_title\">Graph-based real-time monitoring of physical distancing<\/H6><\/span>\n\t\t\t\t<\/label>\n\t\t\t<\/li>\n\t\t\t\t\t\t<li class=\"sp-tab__nav-item\" role=\"navigation\">\n\t\t\t\t<label class=\"sp-tab__nav-link\" data-sptoggle=\"tab\" for=\"#tab-8852\" role=\"tab\" aria-controls=tab-8852 aria-selected=false>\n\t\t\t\t\t<span class=\"tab_title_area\"><H6 class=\"sp-tab__tab_title\">Benchmarking high-fidelity pedestrian tracking systems<\/H6><\/span>\n\t\t\t\t<\/label>\n\t\t\t<\/li>\n\t\t\t\t\t\t<li class=\"sp-tab__nav-item\" role=\"navigation\">\n\t\t\t\t<label class=\"sp-tab__nav-link\" data-sptoggle=\"tab\" for=\"#tab-8853\" role=\"tab\" aria-controls=tab-8853 aria-selected=false>\n\t\t\t\t\t<span class=\"tab_title_area\"><H6 class=\"sp-tab__tab_title\">Mulit-scale analysis of pedestrian inter-arrival time<\/H6><\/span>\n\t\t\t\t<\/label>\n\t\t\t<\/li>\n\t\t\t<\/ul>\n<div class=\"sp-tab__tab-content\">\n\t\t\t\t<div id=\"tab-8851\" class=\"sp-tab__tab-pane sp-tab__show sp-tab__active\" role=\"tabpanel\">\n\t\t\t\t\t<div class=\"sp-tab-content\"><h5>Graph-based real-time monitoring of physical distancing and estimation of secondary infection probability<\/h5>\n<p><iframe loading=\"lazy\" title=\"PED - Graph-based real-time monitoring of physical distancing\" width=\"840\" height=\"473\" src=\"https:\/\/www.youtube.com\/embed\/AIN97PAHotE?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen><\/iframe><\/p>\n<p>Connected material: <a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0240963\">https:\/\/doi.org\/10.1371\/journal.pone.0240963<\/a><\/p>\n<p><strong>Authors:<\/strong><br \/>\nCaspar A. S. Pouw, Andrea Di Benedetto, Federico Toschi, Alessandro Corbetta<\/p>\n<p><strong>Abstract:<\/strong><br \/>\nHow likely is a Covid-19 infection in a trafficked facility such as a train station? As of today monitoring and ensuring physical distancing, together with face masks, are the conventional mechanisms to prevent secondary infections in public facilities. In this contribution we investigate the physical-distance patterns in a train station and, on this basis, estimate the secondary infection probabilities using basic contagion models. This work relies on an improved version of the graph-based analytic framework we proposed in Pouw et al., PLoS ONE 15(10): e0240963, 2020. The method uses a sparse network that represents, in real-time, pedestrian-pedestrian interactions via vector-weighted graph connections and is capable of autonomously recognizing family-group structures. First, using privacy-respectful pedestrian data collected at a trafficked Dutch train platform, we report on the distance-time contact patterns considering different Covid-19 regulations, e.g. with or without face masks. Then, on the basis of these contact patterns, we tackle our initial question: \u201cIf a randomly picked person in our network is assumed infectious, how likely is this person infecting others?\u201d. We estimate the probability of such secondary infection by combining distance-time contact patterns with basic epidemic and physics-based models taken from the literature, encompassing both droplet and airborne transmissions. Despite the actual contagion probabilities are probably susceptible to many other factors, we believe that this study helps understand dependencies on variables related to crowd dynamics.<\/p>\n<p><strong>Keywords:<\/strong><br \/>\nCovid-19 automated physical distancing analysis &#8211; Contagion probability in public spaces \u2013 High-statistics pedestrian dynamics \u2013 Statistical mechanics of human crowds \u2013 Privacy-respectful tracking \u2013 Real-time trajectory and group analysis<\/p>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<div id=\"tab-8852\" class=\"sp-tab__tab-pane \" role=\"tabpanel\">\n\t\t\t\t\t<div class=\"sp-tab-content\"><h5>Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring, and crowd control<\/h5>\n<p>\u00a0<\/p>\n<p><iframe loading=\"lazy\" title=\"PED - Benchmarking high-fidelity pedestrian tracking systems\" width=\"840\" height=\"473\" src=\"https:\/\/www.youtube.com\/embed\/EnAt_zHgF28?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen><\/iframe><\/p>\n<p><strong>Connected material:<\/strong>\u00a0 <a href=\"https:\/\/arxiv.org\/abs\/2108.11719\">https:\/\/arxiv.org\/abs\/2108.11719<\/a><\/p>\n<p><strong>Authors:<\/strong><br \/>\nCaspar A.S. Pouw, Joris Willems, Frank van Schadewijk, Jasmin Thurau,<br \/>\nFederico Toschi, Alessandro Corbetta<\/p>\n<p><strong>Abstract:<\/strong><br \/>\nHigh-fidelity pedestrian tracking has been an important tool in fundamental pedestrian dynamics research allowing us to quantify the statistics of relevant observables such as walking velocities, mutual distances, and body orientations. As this technology advances, it is becoming increasingly useful also in societal applications connected to the public domain. Continued urbanization is overwhelming existing pedestrian infrastructures like transportation hubs and stations, generating an urgent need for real-time highly-accurate individual tracking, aiming both at flow monitoring and dynamics understanding. To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy. This is not only necessary to guarantee data quality, but also to identify systematic errors. Currently, there is no established policy in this context. In this contribution, we present a benchmark suite for privacy-respectful pedestrian tracking techniques. The suite is universal as it is technology independent and it is applicable for scientific, open-source and commercial pedestrian tracking systems, which operate both in lab environments and real-life conditions. The benchmark suite consists of 5 tests addressing specific aspects of pedestrian tracking quality and includes accurate line-based crowd-flux estimation, density estimation, position detection, and path registration. The tests follow from an effort to systematically identify the most accurate large-scale pedestrian tracking system. We provide the benchmark results for two tracking systems, both operating in real-life, one commercial, and the other based on overhead depth maps developed in academia. We discuss the results on the basis of the synthetic indicators and report on the typical sensor behavior. This enables us to highlight the current state of the art, its limitations and provide installation recommendations (with specific attention to multi-sensor setups and data stitching). We conclude with an outlook for further refinements of the benchmark suit.<\/p>\n<p><strong>Keywords:<\/strong><br \/>\nHigh-fidelity pedestrian tracking \u2013 Sensor benchmarking \u2013 Crowd monitoring \u2013 Real-life pedestrian measurements \u2013 Industrial and societal applications<\/p>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<div id=\"tab-8853\" class=\"sp-tab__tab-pane \" role=\"tabpanel\">\n\t\t\t\t\t<div class=\"sp-tab-content\"><h5>Multi-scale analysis of pedestrian inter-arrival time in large-scale public facilities<\/h5>\n<p><iframe loading=\"lazy\" title=\"PED - Multi-scale analysis of pedestrian inter-arrival times in large-scale public facilities\" width=\"840\" height=\"473\" src=\"https:\/\/www.youtube.com\/embed\/QsWju2vqtzU?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen><\/iframe><\/p>\n<p><strong>Authors:<\/strong><br \/>\nCaspar A. S. Pouw, Alessandro Gabbana, Alessandro Corbetta, Federico Toschi<\/p>\n<p><strong>Abstract:<\/strong><br \/>\nPedestrians are commuting continuously around the globe traveling to and from different destinations e.g. to work, school, supermarket, sports club. The exact moments and the frequency in which pedestrians enter and leave certain places or facilities defines a complex random arrival process inherently connected to the crowd flows and their many timescales. The arrival process is often modulated by characteristics such as office hours, train frequencies, and queue formations, besides, pedestrian groups generate very frequent and correlated arrivals. However, all this complexity is usually disregarded: in the practice, pedestrian arrival processes are often simplified by Poisson models, i.e. arrivals are completely defined by an average inter-arrival time. This is most likely due to the absence of high-resolution data sets that span sufficiently large time scales. Thanks to the recent emergence of accurate privacy-respectful pedestrian tracking we can build sophisticated high-statistics data sets which enable us to analyze the arrival process far more accurately. For this purpose, we present an in-depth analysis of the pedestrian arrival process from a privacy-respectful data set measured at a real-life Dutch train platform. From the high-statistics trajectory data, we determine the frame-interpolated time of entering or leaving a train platform. The signal contains approximately 13 million pedestrian arrivals between 2017 and 2019. The timescales in this data set range from sub-second intervals to seasonal changes. Guided by a renormalization flow principle we analyze the signal at different timescales seeking patterns and self-similarities. The practical implications of this work are arrival models that go beyond Poisson processes and are thus capable of describing the statistics of real-life arrival processes with far higher accuracy.<\/p>\n<p><strong>Keywords:<\/strong><br \/>\nReal-life pedestrian arrival processes \u2013 Pedestrian models \u2013 Coarse-grained inter-arrival times<\/p>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_mi_skip_tracking":false,"ngg_post_thumbnail":0},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>PED21 - Crowdflow Research Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/crowdflow.phys.tue.nl\/wordpress\/pedestrian-and-evacuation-dynamics-2021\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PED21 - 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