{"id":613,"date":"2025-05-18T21:21:01","date_gmt":"2025-05-18T21:21:01","guid":{"rendered":"https:\/\/conferences.css-fr.org\/?page_id=613"},"modified":"2025-06-23T22:55:28","modified_gmt":"2025-06-23T22:55:28","slug":"parallel-session-2","status":"publish","type":"page","link":"https:\/\/conferences.css-fr.org\/?page_id=613","title":{"rendered":"Parallel session 2"},"content":{"rendered":"<div id='full_slider_1'  class='avia-fullwidth-slider main_color avia-shadow   avia-builder-el-0  el_before_av_one_half  avia-builder-el-first   container_wrap fullsize'  ><div  class='avia-slideshow av-mau5rqzz-3dc91619af02c82a533dc3bba47ed57c avia-slideshow-featured av_slideshow_full avia-slide-slider av-slideshow-ui av-control-default av-slideshow-manual av-loop-once av-loop-manual-endless av-default-height-applied   avia-slideshow-1' data-slideshow-options=\"{&quot;animation&quot;:&quot;slide&quot;,&quot;autoplay&quot;:false,&quot;loop_autoplay&quot;:&quot;once&quot;,&quot;interval&quot;:5,&quot;loop_manual&quot;:&quot;manual-endless&quot;,&quot;autoplay_stopper&quot;:false,&quot;noNavigation&quot;:false,&quot;bg_slider&quot;:false,&quot;keep_padding&quot;:false,&quot;hoverpause&quot;:false,&quot;show_slide_delay&quot;:0}\"  itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><ul class='avia-slideshow-inner ' style='padding-bottom: 28.666666666667%;'><li  class='avia-slideshow-slide av-mau5rqzz-3dc91619af02c82a533dc3bba47ed57c__0  av-single-slide slide-1 slide-odd'><div data-rel='slideshow-1' class='avia-slide-wrap '   ><div class='av-slideshow-caption av-mau5rqzz-3dc91619af02c82a533dc3bba47ed57c__0 caption_fullwidth caption_bottom'><div class=\"container caption_container\"><div class=\"slideshow_caption\"><div class=\"slideshow_inner_caption\"><div class=\"slideshow_align_caption\"><h2 class='avia-caption-title '  itemprop=\"name\" >Parallel Session 2 - Monday 23th, 14:45-16:25<\/h2><div class='avia-caption-content '  itemprop=\"description\" ><p>Biology \/ Urban Systems<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><img decoding=\"async\" fetchpriority=\"high\" class=\"wp-image-20 avia-img-lazy-loading-not-20\"  src=\"https:\/\/conferences.css-fr.org\/wp-content\/uploads\/2024\/11\/Neurones-1500x430.png\" width=\"1500\" height=\"430\" title='Neurones' alt=''  itemprop=\"thumbnailUrl\"   \/><\/div><\/li><\/ul><\/div><\/div><div id='after_full_slider_1'  class='main_color av_default_container_wrap container_wrap fullsize'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-613'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-1ov25-fe409213eadffc81be8c35e4aaee5ccc av_one_half  avia-builder-el-1  el_after_av_slideshow_full  el_before_av_one_half  avia-builder-el-first  first flex_column_div  '     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-mc3xrzzj-c1886c94f2e9411c320960e5a97d5ff9\">\n#top .av-special-heading.av-mc3xrzzj-c1886c94f2e9411c320960e5a97d5ff9{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-mc3xrzzj-c1886c94f2e9411c320960e5a97d5ff9 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-mc3xrzzj-c1886c94f2e9411c320960e5a97d5ff9 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-mc3xrzzj-c1886c94f2e9411c320960e5a97d5ff9 av-special-heading-h3  avia-builder-el-2  el_before_av_textblock  avia-builder-el-first '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Social systems<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><br \/>\n<section  class='av_textblock_section av-bowh9-1f37734191731d7475a60b12fb432d18 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><p>Auditorium<\/p>\n<p><strong>Chair: <\/strong>Laura Hernandez<\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-mc3zhbnr-03808848f68bf99ea01b2e2867714c71  avia-builder-el-4  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-mc4gnpnb-6f8cf9682636c89418197cc4a106da33'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-1' data-fake-id='#toggle-id-1' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-1' data-slide-speed=\"200\" data-title=\"  Computational learning mechanisms of information propagating  in social networks, Jean-Claude Dreher \" data-title-open=\"\" data-aria_collapsed=\"Click to expand:   Computational learning mechanisms of information propagating  in social networks, Jean-Claude Dreher \" data-aria_expanded=\"Click to collapse:   Computational learning mechanisms of information propagating  in social networks, Jean-Claude Dreher \">  Computational learning mechanisms of information propagating  in social networks, Jean-Claude Dreher <span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-1' aria-labelledby='toggle-toggle-id-1' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p><strong>Author:<\/strong> Jean-Claude Dreher<\/p>\n<blockquote>\n<p>What are the mechanisms underlying how we integrate disparate and redundant information spreading in social networks? According to the DeGroot (DG) model, we adjust our beliefs based on the weighted average opinions of our social connections. In contrast, reinforcement learning (RL) models assume that learning occurs sequentially, driven by error-driven beliefs updating. Here, we directly compare these models based on two datasets of information propagating in social networks and determine which learning rule best accounts for the integration of information spreading in social networks. We found that variants of the DG model provided a better overall fit across participants and were more effective in building consensus and generating accurate estimates of the true state of the world. Finally, simulated models showed how social network topology affect choice accuracy and consensus depending on the learning rule. Together, our findings shed light on the computational mechanisms underlying information propagation in social networks.<\/p>\n<\/blockquote>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mc3zh5i1-8ffc9dd9f1fd4f46e7af63475f08e351'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-2' data-fake-id='#toggle-id-2' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-2' data-slide-speed=\"200\" data-title=\"Preserving friendships in school contacts: an algorithm to construct synthetic temporal networks for epidemic modelling, Lucille Calmon\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Preserving friendships in school contacts: an algorithm to construct synthetic temporal networks for epidemic modelling, Lucille Calmon\" data-aria_expanded=\"Click to collapse: Preserving friendships in school contacts: an algorithm to construct synthetic temporal networks for epidemic modelling, Lucille Calmon\">Preserving friendships in school contacts: an algorithm to construct synthetic temporal networks for epidemic modelling, Lucille Calmon<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-2' aria-labelledby='toggle-toggle-id-2' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Lucille Calmon, Elisabetta Colosi, Giulia Bassignana, Alain Barrat and Vittoria Colizza<\/p>\n<blockquote>\n<p>High-resolution temporal data on contacts between hosts provide crucial information on the mixing patterns underlying infectious disease transmission. Publicly available data sets of contact data are however typically recorded over short time windows with respect to the duration of an epidemic. To inform models of disease transmission, data are thus often repeated several times, yielding synthetic data covering long enough timescales. Looping over short term data to approximate contact patterns on longer timescales can lead to unrealistic transmission chains because of the deterministic repetition of all contacts, without any renewal of the contact partners of each individual between successive periods. Real contacts indeed include a combination of regularly repeated contacts (e.g., due to friendship relations) and of more casual ones. We propose an algorithm to longitudinally extend contact data recorded in a school setting, taking into account this dual aspect of contacts and in particular the presence of repeated contacts due to friendships.<\/p>\n<p>To illustrate the interest of such an algorithm, we simulate the spread of SARS-CoV-2 on our synthetic contacts using an agent-based model specific to the school setting. We compare the results with simulations performed on synthetic data extended with simpler algorithms to determine the impact of preserving friendships in the data extension method. Notably, the preservation of friendships does not strongly affect transmission routes between classes in the school but leads to different infection pathways between individual students. Our results moreover indicate that gathering contact data during two days in a population is sufficient to generate realistic synthetic contact sequences between individuals in that population on longer timescales. The proposed tool will allow modellers to leverage existing contact data, and contributes to the design of optimal future field data collection.<\/p>\n<\/blockquote>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mc3zj825-e25b169b7b002327d2fce4ee34347d84'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-3' data-fake-id='#toggle-id-3' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-3' data-slide-speed=\"200\" data-title=\"A Hypergraph Analysis of the European Commission Lobby Network, Amina Azaiez and Antoine Mandel\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: A Hypergraph Analysis of the European Commission Lobby Network, Amina Azaiez and Antoine Mandel\" data-aria_expanded=\"Click to collapse: A Hypergraph Analysis of the European Commission Lobby Network, Amina Azaiez and Antoine Mandel\">A Hypergraph Analysis of the European Commission Lobby Network, Amina Azaiez and Antoine Mandel<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-3' aria-labelledby='toggle-toggle-id-3' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p><strong>authors:<\/strong> Amina Azaiez and Antoine Mandel<\/p>\n<blockquote>\n<p>We use transparency data published by the European Commission (EC) to perform a<br \/>\nquantitative analysis of the structure and dynamics of stakeholder consultation in EU policy- making process. We analyze the dataset through the prism of network theory by constructing a hypergraph whose nodes are EC officials and stakeholders, and hyperedges connect entities that participate in the same meetings. Our analysis highlights the presence of a hierarchical core-periphery structure, with a few well-connected entities that occupy the center of the network and enjoy a stable integration in the EC policy-making process. Examination of the core composition reveals that companies and trade associations maintain closer relationships with the EC. A regression analysis shows that lobbying efforts and company size are significant predictors of company centrality, independent of other objective characteristics. Our findings provide quantitative evidence supporting the perception of lobbying as a tool dominated by well-connected actors, while also revealing heterogeneous lobbying strategies across stakeholder groups.<\/p>\n<\/blockquote>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mc3zo6ku-4d786c8d182d93d02954ba5dccec6924'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-4' data-fake-id='#toggle-id-4' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-4' data-slide-speed=\"200\" data-title=\"Emotional Waves in the Aftermath of Natural Disasters: A Sentiment Analysis of the 2023 Morocco Earthquake, Zakariae Benchrif\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Emotional Waves in the Aftermath of Natural Disasters: A Sentiment Analysis of the 2023 Morocco Earthquake, Zakariae Benchrif\" data-aria_expanded=\"Click to collapse: Emotional Waves in the Aftermath of Natural Disasters: A Sentiment Analysis of the 2023 Morocco Earthquake, Zakariae Benchrif\">Emotional Waves in the Aftermath of Natural Disasters: A Sentiment Analysis of the 2023 Morocco Earthquake, Zakariae Benchrif<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-4' aria-labelledby='toggle-toggle-id-4' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p><strong>Authors:<\/strong> Zakariae Benchrif, Floriana Gargiulo and Dominique Guillo<\/p>\n<blockquote>\n<p>Understanding emotional responses in crisis events is crucial, as they shape public discourse, influence policy decisions, and affect community resilience [1]. Social media serve both as repositories of collective emotions and as vehicles for emotional contagion, providing therefore valuable insights into how societies process and react to disasters [2]. This study investigates the evolution of collective emotions following the 8th September<br \/>\n2023 Haouz earthquake, in Morocco, through sentiment analysis of user-generated content on two distinct online platforms: YouTube and Hespress, a prominent Moroccan news website. By analyzing comments before and after the disaster, we identify patterns of emotional expression and their temporal dynamics. Using a baseline established from comments posted in the month preceding the earth quake, our findings indicate that YouTube content generally exhibits a higher emotional intensity compared to Hespress. However, immediately following the earthquake, sentiment levels on both platforms converge, suggesting a shared emotional response across different online communities. Our analysis reveals the presence of distinct emotional waves. In the immediate aftermath (September 8\u201312), sentiment shifts towards a more positive tone, dominated by themes of solidarity, prayer, and communal support. This trend is further amplified, particularly on YouTube, on September 12, coinciding with the Moroccan king\u2019s visit to the disaster<br \/>\nzone, which redirected discourse towards national unity and leadership support. However, from September 17 onward, sentiment turns negative on both platforms, correlating with growing criticisms of the government\u2019s crisis management. A temporary recovery occurs on September 20 following the announcement of a new relief fund, but sentiment declines again from September 25, in parallel with a decreasing volume of comments and a discourse<br \/>\nincreasingly focused on governmental accountability.<br \/>\nOur findings illustrate how natural disasters trigger significant shifts in communication patterns, leading to a temporary convergence of sentiment across platforms. Moreover, they highlight the presence of intrinsic emotional rhythms\u2014alternating between solidarity and frustration\u2014alongside exogenous fluctuations driven by external events, such as political actions and relief measures. To further investigate these emotional waves, and in particular to isolate the endogenous emotional responses, we are extending our analysis by comparing the Moroccan case to 62 other natural disasters that occurred between 2016 and 2019, using a large-scale database of Twitter content [3]. This comparative approach aims to uncover commonalities in emotional<br \/>\nresponses to crises across different socio-political contexts, offering deeper insights into the mechanisms of collective sentiment formation in times of distress.<\/p>\n<\/blockquote>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mc3zjzhh-c81734903f34cf1923e657b064f88c48'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-5' data-fake-id='#toggle-id-5' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-5' data-slide-speed=\"200\" data-title=\"Syst\u00e8mes hyper-complexes, Reste causal et climat, Marc Delepouve, Ada Diaconescu and Emmanuel Ferrand\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Syst\u00e8mes hyper-complexes, Reste causal et climat, Marc Delepouve, Ada Diaconescu and Emmanuel Ferrand\" data-aria_expanded=\"Click to collapse: Syst\u00e8mes hyper-complexes, Reste causal et climat, Marc Delepouve, Ada Diaconescu and Emmanuel Ferrand\">Syst\u00e8mes hyper-complexes, Reste causal et climat, Marc Delepouve, Ada Diaconescu and Emmanuel Ferrand<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-5' aria-labelledby='toggle-toggle-id-5' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>authors: Marc Delepouve, Ada Diaconescu and Emmanuel Ferrand<\/p>\n<blockquote>\n<p>Nous appelons Syst\u00e8me hyper-complexe un syst\u00e8me complexe o\u00f9 des \u00e9mergences in\u00e9dites et impr\u00e9visibles prolif\u00e8rent et impactent l\u2019\u00e9tat d\u2019ensemble du syst\u00e8me, conf\u00e9rant \u00e0 ce dernier une \u00e9volution constante. Dans le contexte d\u2019un Syst\u00e8me hyper-complexe, nous d\u00e9finissons le Reste causal associ\u00e9 \u00e0 une repr\u00e9sentation de l\u2019\u00e9volution future d\u2019un ph\u00e9nom\u00e8ne donn\u00e9, comme \u00e9tant l\u2019ensemble des ph\u00e9nom\u00e8nes qui influenceront ou pourraient influencer cette \u00e9volution mais qui ne sont pas pris en compte dans cette repr\u00e9sentation. Nous appliquons cet outil aux projections donn\u00e9es par les mod\u00e8les climatiques et aux sc\u00e9narios publi\u00e9s par le GIEC. Nous montrons que des ph\u00e9nom\u00e8nes du Reste causal associ\u00e9 \u00e0 ces projections et \u00e0 ces sc\u00e9narios pourraient avoir une action cons\u00e9quente sur le climat. Pourtant, du 2e au 5e rapport d\u2019\u00e9valuation du GIEC, les R\u00e9sum\u00e9s \u00e0 l\u2019intention des d\u00e9cideurs (RID) du GT1 ont pr\u00e9sent\u00e9 des sc\u00e9narios tout en ne citant aucun des ph\u00e9nom\u00e8nes du Reste causal associ\u00e9. Nous concluons sur la n\u00e9cessit\u00e9 de publier, avec les projections et les sc\u00e9narios climatiques, le Reste causal associ\u00e9, tant pour des raisons scientifiques que politiques. Puis nous \u00e9largissons cette conclusion au-del\u00e0 du domaine du climat.<\/p>\n<\/blockquote>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div>\n<div  class='flex_column av-ayxal-7901d6b1f2fb56a4f1b464fb87c5a72b av_one_half  avia-builder-el-5  el_after_av_one_half  avia-builder-el-last  flex_column_div  '     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-mau5qcsb-8c5542d0f2b1788d713f788b09cfb32e\">\n#top .av-special-heading.av-mau5qcsb-8c5542d0f2b1788d713f788b09cfb32e{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-mau5qcsb-8c5542d0f2b1788d713f788b09cfb32e .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-mau5qcsb-8c5542d0f2b1788d713f788b09cfb32e .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-mau5qcsb-8c5542d0f2b1788d713f788b09cfb32e av-special-heading-h3  avia-builder-el-6  el_before_av_textblock  avia-builder-el-first '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Urban Systems<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><br \/>\n<section  class='av_textblock_section av-mau5sg9b-0c21b5535e1a35ef29f10b44413e00a9 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><p>Conference room<\/p>\n<p><strong>Chair: <\/strong>Julien Randon-Furling<\/p>\n<\/div><\/section><br \/>\n<div  class='togglecontainer av-mau5uuk4-3a29a476b9953b381b623e8bfcce464d  avia-builder-el-8  el_after_av_textblock  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-mau5t86o-b6df1d4622819647cd464fc0e81b9c3f'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-6' data-fake-id='#toggle-id-6' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-6' data-slide-speed=\"200\" data-title=\"Dense traffic and parking search in urban areas: Peering underneath the veil of complexity, Alexandre Nicolas, Antoine Tordeux, Enock Ndunda and Oscar Dufour\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Dense traffic and parking search in urban areas: Peering underneath the veil of complexity, Alexandre Nicolas, Antoine Tordeux, Enock Ndunda and Oscar Dufour\" data-aria_expanded=\"Click to collapse: Dense traffic and parking search in urban areas: Peering underneath the veil of complexity, Alexandre Nicolas, Antoine Tordeux, Enock Ndunda and Oscar Dufour\">Dense traffic and parking search in urban areas: Peering underneath the veil of complexity, Alexandre Nicolas, Antoine Tordeux, Enock Ndunda and Oscar Dufour<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-6' aria-labelledby='toggle-toggle-id-6' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Vehicular traffic in cities causes negative externalities in the real world, but it also represents a theoretical conundrum, insofar as it mingles multiple layers of complexity: the complexity of human behaviour, that of the street network, and the collective effects that emerge from the interactions between cars. Our contribution aims to underline that, despite this complexity, some salient features of traffic can be quantitatively rationalised, by introducing adequate stochastic processes to render uncertain real behaviours.<\/p>\n<p>We will first consider the emergence of traffic oscillations (i.e., stop-and-go waves) when traffic gets dense, even without external perturbation. A widespread idea has it that this emergence is related to a deterministic linear instability, caused e.g. by response delays. In contrast, we will show that inaccuracy in drivers&#8217; perceptions and responses, modelled by stochastic noise and termed `human error&#8217; in (Laval et al., 2014), gives rise to realistic traffic oscillations; a physics-based reasoning will provide quantitative insight into their emergence.<\/p>\n<p>In the second part, we will turn to parking search, which is a central issue for metropolitan transport authorities and for individual drivers: it plays a key role in mode choice and cars cruising for on-street parking may represent more than 10\\% of the traffic<br \/>\nin many large cities (Hampshire et al., 2018).<br \/>\nVast swaths of literature in Transport Engineering have thus been dedicated to parking and, more recently, a couple of works have shown that, in extremely stylised settings, parking search can be addressed analytically \\cite{krapivsky2019simple}.<br \/>\nOur goal, here, is to evince that even in realistic settings &#8212; with a large-scale street network and heterogeneous drivers&#8217; behaviours &#8212; the machinery of statistical physics can give quantitative insight into parking search (Dutta et al., 2023) and help explore smart-parking solutions. (See attached PDF)<\/p>\n<p>Recently, we have started extending this approach to assess the potential of parking-related measures, such as a reduction of the parking supply (see inset) or smart parking applications, guiding users towards probably vacant parking areas.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau5thjd-0d7e7b71cac950dd2b69fc873931b517'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-7' data-fake-id='#toggle-id-7' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-7' data-slide-speed=\"200\" data-title=\"Anticipation beyond the imminent future in pedestrian crowds, Alexis Raulin-Foissac and Alexandre Nicolas \" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Anticipation beyond the imminent future in pedestrian crowds, Alexis Raulin-Foissac and Alexandre Nicolas \" data-aria_expanded=\"Click to collapse: Anticipation beyond the imminent future in pedestrian crowds, Alexis Raulin-Foissac and Alexandre Nicolas \">Anticipation beyond the imminent future in pedestrian crowds, Alexis Raulin-Foissac and Alexandre Nicolas <span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-7' aria-labelledby='toggle-toggle-id-7' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Pedestrians demonstrate remarkable navigational capabilities in highly constrained environments, such as dense crowds or cluttered urban settings. When multiple individuals interact, complex collective behaviors emerge, including lane formation and stop-and-go waves. While many modeling approaches aim to replicate these familiar behaviors, few account for the pedestrians\u2019 ability to mutually anticipate their non-linear moves over finite time horizon. Here we fill this gap by introducing a new crowd modeling branch inspired by game theory. Our work integrates methods and insights from diverse research fields: condensed-matter physics, through an analogy between space-time trajectories and polymers; biological studies, particularly regarding energy expenditure; and robotics.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau5tu55-05ef4ca7485e193b4cf45b5ed101f85b'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-8' data-fake-id='#toggle-id-8' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-8' data-slide-speed=\"200\" data-title=\"Towards realistic crowd simulation: incorporating medical data-driven body shapes for more realistic collisions and avoidance, Oscar Dufour, David Rodney and Alexandre Nicolas\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Towards realistic crowd simulation: incorporating medical data-driven body shapes for more realistic collisions and avoidance, Oscar Dufour, David Rodney and Alexandre Nicolas\" data-aria_expanded=\"Click to collapse: Towards realistic crowd simulation: incorporating medical data-driven body shapes for more realistic collisions and avoidance, Oscar Dufour, David Rodney and Alexandre Nicolas\">Towards realistic crowd simulation: incorporating medical data-driven body shapes for more realistic collisions and avoidance, Oscar Dufour, David Rodney and Alexandre Nicolas<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-8' aria-labelledby='toggle-toggle-id-8' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Mass gatherings like Lyon\u2019s F\u00eate des Lumi\u00e8res, which draw millions annually, highlight the need for better crowd dynamics modeling to ensure safety and optimise pedestrian flow. Traditional models struggle in medium-density scenarios, oversimplifying pedestrian behavior and physical interactions. I propose an enhanced framework addressing these limitations by integrating anisotropic pedestrian shapes, realistic mechanical interactions, and advanced decision-making processes.<br \/>\nConventional models often use isotropic circular shapes, failing to account for sneaking behaviour. They also inadequately replicate high-density scenarios observed at events like F\u00eate des Lumi\u00e8res. Existing approaches typically separate mechanical and decisional behaviors\u2014mechanical models handle collisions but lack sophisticated avoidance strategies, while decision-making models underestimate collision frequencies.<br \/>\nThe proposed framework combines a decision-making layer (governing translational velocity and rotational speed based on constraints) with a mechanical layer (modeling physical interactions inspired by granular dynamics). It uses anisotropic shapes based on anthropometric data to reflect individual heterogeneity. Constraints include destination goals, biomechanical limits, personal space preservation, and time-to-collision strategies.<br \/>\nValidated through simulations of real-world scenarios, the model improves accuracy across density ranges. Its advancements have critical implications for enhancing safety and optimising pedestrian flow during large-scale events.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau5sx9h-166729367371ac9a9bb6027dcdc08b10'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-9' data-fake-id='#toggle-id-9' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-9' data-slide-speed=\"200\" data-title=\"Land use change in 1800+ world cities since 1975 through the lens of radial scaling laws, Thibaud Rivet, R\u00e9mi Lemoy, Axel P\u00e9cheric and Ga\u00ebtan Laziou\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Land use change in 1800+ world cities since 1975 through the lens of radial scaling laws, Thibaud Rivet, R\u00e9mi Lemoy, Axel P\u00e9cheric and Ga\u00ebtan Laziou\" data-aria_expanded=\"Click to collapse: Land use change in 1800+ world cities since 1975 through the lens of radial scaling laws, Thibaud Rivet, R\u00e9mi Lemoy, Axel P\u00e9cheric and Ga\u00ebtan Laziou\">Land use change in 1800+ world cities since 1975 through the lens of radial scaling laws, Thibaud Rivet, R\u00e9mi Lemoy, Axel P\u00e9cheric and Ga\u00ebtan Laziou<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-9' aria-labelledby='toggle-toggle-id-9' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>The ever-increasing urbanization of the world meets us with pressing socio-environmental challenges. The sprawl of human settlements all over the planet leads to losses of arable land and biodiversity, and increases flood risks. Furthermore, this expansion is concerning with regard to climate change. In this context and considering the developing will of limiting urban sprawl (see for example the No Net Land Take objective [5]), we are faced with the task of understanding the fundamental structure and dynamics of cities.<br \/>\nCities are more than just points on a map. They have an internal structure which unfolds radially, from center to periphery, revealing patterns that shape urban dynamics. To understand this spatial organization, we analyze how the share of built-up land evolves as we move outward. Since cities present a wide variety of sizes, scaling laws provide a powerful framework for modeling such behavior, capturing how a system\u2019s properties shift with its size. Viewing cities as systems and population as their defining scale, we study how cities sprawl as population grows, at the global scale.<\/p>\n<p>In order to do so, we establish a robust radial scaling law which quantifies the connection between the distance r to the city center and the amount of built-up land share sN (r), and how this relation scales with city size N.<br \/>\nWe extend the homothetic scaling obtained in previous work [6,7] to a global sample of cities and at different dates to study the evolution over time. We focus our work on the 1860 cities of the world whose population is greater than 300,000 inhabitants in 2020. This sample presents a large diversity in terms of population size, topology, land use, urbanization policies and more. Despite such a wide variety, the scaling law still applies with surprising regularity. Furthermore, looking at the data at different points in time \u2014 from 1975 to 2020, with a 5 year step \u2014 allows us to analyze the evolution of this internal urban structure and scaling law of built-up land.<br \/>\nThe data used in the study come from the Global Human Settlement Layer (GHSL), produced by the European Commission. It provides high-resolution and high-quality, globally consistent distributions of built-up areas, which we combine with the World Urbanization Prospect database from the United Nations for trustworthy population statistics. For each city of choice and each date, we analyze this GHS BUILT-S raster layer at 100 meters resolution and compute the average built-up land share in concentric rings of 200 meters width around the city center. To ensure the viable comparability between cities, we use for each city, with population N , a rescaled distance to the center r\u2032 = r (N_Tokyo\/N)^(1\/2) which makes all cities comparable to the largest one, Tokyo (with population N_Tokyo \u2243 3.7 \u00d7 10^7 in 2020).<\/p>\n<p>We analyze the evolution of the mean rescaled profile on Figure 1, and observe that built-up land increases over time all along the center-periphery profile, even when the size effect is controlled by the homothetic scaling law. In linear scale, the change is especially visible near the center, while it appears more clearly in the periphery on a semilog graph (Figure 1). This result means that the built-up surface per capita increases over time globally. We link this urban sprawl phenomenon with economic development and further analyze its geographical variations at national scale on the planet. This clearly questions the sustainability of urban expansion.<\/p>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-613","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages\/613","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=613"}],"version-history":[{"count":15,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages\/613\/revisions"}],"predecessor-version":[{"id":872,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages\/613\/revisions\/872"}],"wp:attachment":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=613"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}