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{"title"=>"Traffic instabilities in self-organized pedestrian crowds", "type"=>"journal", "authors"=>[{"first_name"=>"Mehdi", "last_name"=>"Moussaïd", "scopus_author_id"=>"26028667700"}, {"first_name"=>"Elsa G.", "last_name"=>"Guillot", "scopus_author_id"=>"55221121200"}, {"first_name"=>"Mathieu", "last_name"=>"Moreau", "scopus_author_id"=>"54412751500"}, {"first_name"=>"Jérôme", "last_name"=>"Fehrenbach", "scopus_author_id"=>"16241323100"}, {"first_name"=>"Olivier", "last_name"=>"Chabiron", "scopus_author_id"=>"55220197700"}, {"first_name"=>"Samuel", "last_name"=>"Lemercier", "scopus_author_id"=>"23467356000"}, {"first_name"=>"Julien", "last_name"=>"Pettré", "scopus_author_id"=>"6506525739"}, {"first_name"=>"Cécile", "last_name"=>"Appert-Rolland", "scopus_author_id"=>"6603943120"}, {"first_name"=>"Pierre", "last_name"=>"Degond", "scopus_author_id"=>"7005668908"}, {"first_name"=>"Guy", "last_name"=>"Theraulaz", "scopus_author_id"=>"7004591018"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"364830915", "sgr"=>"84861126231", "issn"=>"1553734X", "arxiv"=>"1203.5267", "pmid"=>"22457615", "scopus"=>"2-s2.0-84861126231", "doi"=>"10.1371/journal.pcbi.1002442", "isbn"=>"1553-7358"}, "id"=>"d9c57abd-7235-39d0-9328-dad8165e5e1a", "abstract"=>"In human crowds as well as in many animal societies, local interactions among individuals often give rise to self-organized collective organizations that offer functional benefits to the group. For instance, flows of pedestrians moving in opposite directions spontaneously segregate into lanes of uniform walking directions. This phenomenon is often referred to as a smart collective pattern, as it increases the traffic efficiency with no need of external control. However, the functional benefits of this emergent organization have never been experimentally measured, and the underlying behavioral mechanisms are poorly understood. In this work, we have studied this phenomenon under controlled laboratory conditions. We found that the traffic segregation exhibits structural instabilities characterized by the alternation of organized and disorganized states, where the lifetime of well-organized clusters of pedestrians follow a stretched exponential relaxation process. Further analysis show that the inter-pedestrian variability of comfortable walking speeds is a key variable at the origin of the observed traffic perturbations. We show that the collective benefit of the emerging pattern is maximized when all pedestrians walk at the average speed of the group. In practice, however, local interactions between slow- and fast-walking pedestrians trigger global breakdowns of organization, which reduce the collective and the individual payoff provided by the traffic segregation. This work is a step ahead toward the understanding of traffic self-organization in crowds, which turns out to be modulated by complex behavioral mechanisms that do not always maximize the group's benefits. The quantitative understanding of crowd behaviors opens the way for designing bottom-up management strategies bound to promote the emergence of efficient collective behaviors in crowds.", "link"=>"http://www.mendeley.com/research/traffic-instabilities-selforganized-pedestrian-crowds", "reader_count"=>96, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>13, "Researcher"=>16, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>5, "Student > Master"=>11, "Other"=>2, "Student > Bachelor"=>6, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>5, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>13, "Researcher"=>16, "Student > Doctoral Student"=>6, "Student > Ph. D. 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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/342647", "https://ndownloader.figshare.com/files/342730", "https://ndownloader.figshare.com/files/342807", "https://ndownloader.figshare.com/files/342859", "https://ndownloader.figshare.com/files/343088", "https://ndownloader.figshare.com/files/343184"], "description"=>"<div><p>In human crowds as well as in many animal societies, local interactions among individuals often give rise to self-organized collective organizations that offer functional benefits to the group. For instance, flows of pedestrians moving in opposite directions spontaneously segregate into lanes of uniform walking directions. This phenomenon is often referred to as a smart collective pattern, as it increases the traffic efficiency with no need of external control. However, the functional benefits of this emergent organization have never been experimentally measured, and the underlying behavioral mechanisms are poorly understood. In this work, we have studied this phenomenon under controlled laboratory conditions. We found that the traffic segregation exhibits structural instabilities characterized by the alternation of organized and disorganized states, where the lifetime of well-organized clusters of pedestrians follow a stretched exponential relaxation process. Further analysis show that the inter-pedestrian variability of comfortable walking speeds is a key variable at the origin of the observed traffic perturbations. We show that the collective benefit of the emerging pattern is maximized when all pedestrians walk at the average speed of the group. In practice, however, local interactions between slow- and fast-walking pedestrians trigger global breakdowns of organization, which reduce the collective and the individual payoff provided by the traffic segregation. This work is a step ahead toward the understanding of traffic self-organization in crowds, which turns out to be modulated by complex behavioral mechanisms that do not always maximize the group's benefits. The quantitative understanding of crowd behaviors opens the way for designing bottom-up management strategies bound to promote the emergence of efficient collective behaviors in crowds.</p> </div>", "links"=>[], "tags"=>["instabilities", "self-organized", "pedestrian", "crowds"], "article_id"=>127736, "categories"=>["Biological Sciences"], "users"=>["Mehdi Moussaïd", "Elsa G. Guillot", "Mathieu Moreau", "Jérôme Fehrenbach", "Olivier Chabiron", "Samuel Lemercier", "Julien Pettré", "Cécile Appert-Rolland", "Pierre Degond", "Guy Theraulaz"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002442.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002442.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002442.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002442.s004", "https://dx.doi.org/10.1371/journal.pcbi.1002442.s005", "https://dx.doi.org/10.1371/journal.pcbi.1002442.s006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Traffic_Instabilities_in_Self_Organized_Pedestrian_Crowds/127736", "title"=>"Traffic Instabilities in Self-Organized Pedestrian Crowds", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-03-22 02:08:56"}
• {"files"=>["https://ndownloader.figshare.com/files/663476"], "description"=>"<p>Illustration of the unstable dynamics observed under experimental conditions for one replication with N = 60 pedestrians.</p>", "links"=>[], "tags"=>["unstable", "observed", "conditions", "replication"], "article_id"=>333941, "categories"=>["Biological Sciences"], "users"=>["Mehdi Moussaïd", "Elsa G. Guillot", "Mathieu Moreau", "Jérôme Fehrenbach", "Olivier Chabiron", "Samuel Lemercier", "Julien Pettré", "Cécile Appert-Rolland", "Pierre Degond", "Guy Theraulaz"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002442.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_the_unstable_dynamics_observed_under_experimental_conditions_for_one_replication_with_N_8202_8202_60_pedestrians_/333941", "title"=>"Illustration of the unstable dynamics observed under experimental conditions for one replication with N = 60 pedestrians.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-22 01:05:41"}
• {"files"=>["https://ndownloader.figshare.com/files/663585"], "description"=>"<p>(A) Two pedestrians <i>i</i> and <i>j</i> belong to the same cluster if one follows the other. (B) The pedestrian <i>j</i> follows pedestrian <i>i</i>, if <i>j</i> moves closer than a distance from the position of pedestrian <i>i</i> at time <i>t</i>, during a time period of seconds. Here,  = 1 s and  = 0.6 m are two clustering parameters.</p>", "links"=>[], "tags"=>["clustering"], "article_id"=>334066, "categories"=>["Biological Sciences"], "users"=>["Mehdi Moussaïd", "Elsa G. Guillot", "Mathieu Moreau", "Jérôme Fehrenbach", "Olivier Chabiron", "Samuel Lemercier", "Julien Pettré", "Cécile Appert-Rolland", "Pierre Degond", "Guy Theraulaz"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002442.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_the_clustering_method_/334066", "title"=>"Illustration of the clustering method.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-22 01:07:46"}
• {"files"=>["https://ndownloader.figshare.com/files/663709"], "description"=>"<p>The clustering method is described in the <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002442#s4\" target=\"_blank\">Materials and Methods</a> section and illustrated <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002442#pcbi-1002442-g002\" target=\"_blank\">Fig. 2</a>. During the first ten seconds, the initial transition from disorder to order is visible. Then, the number of clusters oscillates between well-organized (five clusters or less), and disorganized states (ten clusters or more). (B) The corresponding segregation dynamics for the same three replications.</p>", "links"=>[], "tags"=>["clusters", "replications", "50", "60"], "article_id"=>334191, "categories"=>["Biological Sciences"], "users"=>["Mehdi Moussaïd", "Elsa G. Guillot", "Mathieu Moreau", "Jérôme Fehrenbach", "Olivier Chabiron", "Samuel Lemercier", "Julien Pettré", "Cécile Appert-Rolland", "Pierre Degond", "Guy Theraulaz"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002442.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Illustration_of_the_evolution_of_the_number_of_clusters_for_three_replications_with_N_8202_8202_30_50_and_60_pedestrians_/334191", "title"=>"(A) Illustration of the evolution of the number of clusters for three replications with N = 30, 50 and 60 pedestrians.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-22 01:09:51"}
• {"files"=>["https://ndownloader.figshare.com/files/663796"], "description"=>"<p>(A) The probability for a cluster to remain unchanged after a time period of <i>t</i> seconds. (B) <i>log(p)</i> versus <i>t</i> does not yield a straight line, showing that <i>p(t)</i> decays slower than an exponential. (C) <i>log(p)</i> versus <i>log(t)</i> is a curve, showing that <i>p(t)</i> decays faster than a power-law. (D) A straight line is found for <i>log(p)</i> versus <i>t^k</i> with <i>k = 0.4</i>, demonstrating that the lifetime of pedestrian clusters follows a stretched exponential relaxation law: , where the relaxation exponent <i>k</i> depends on the number of pedestrians N. The insets indicate simulation results, where the same distribution law is found. Empirical data and computer simulations yield the same relaxation exponents <i>k</i> = 0.6, 0.5, and 0.5 for N = 30, 50 and 60 respectively.</p>", "links"=>[], "tags"=>["clusters"], "article_id"=>334278, "categories"=>["Biological Sciences"], "users"=>["Mehdi Moussaïd", "Elsa G. Guillot", "Mathieu Moreau", "Jérôme Fehrenbach", "Olivier Chabiron", "Samuel Lemercier", "Julien Pettré", "Cécile Appert-Rolland", "Pierre Degond", "Guy Theraulaz"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002442.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Empirical_distribution_of_the_clusters_lifetime_/334278", "title"=>"Empirical distribution of the clusters lifetime.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-22 01:11:18"}
• {"files"=>["https://ndownloader.figshare.com/files/663923"], "description"=>"<p>(A) Local density maps for three representative replications with N = 30, 50 and 60 pedestrians. The emergence and the propagation of density peaks (red) and density gaps (blue) are visible. (B) Local radial speed for the same three replications, showing the lateral movements of pedestrians. The largest values occur mostly around density gaps. (C) Average local density as a function of local radial speed, for all replications with N = 30, 50 and 60 pedestrians. The largest values of occur where the local density level is low, that is, around density gaps. This correlation is less visible for N = 30, probably due to the lower global density level.</p>", "links"=>[], "tags"=>["radial"], "article_id"=>334405, "categories"=>["Biological Sciences"], "users"=>["Mehdi Moussaïd", "Elsa G. Guillot", "Mathieu Moreau", "Jérôme Fehrenbach", "Olivier Chabiron", "Samuel Lemercier", "Julien Pettré", "Cécile Appert-Rolland", "Pierre Degond", "Guy Theraulaz"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002442.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_between_local_radial_speed_and_density_gaps_/334405", "title"=>"Correlation between local radial speed and density gaps.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-22 01:13:25"}
• {"files"=>["https://ndownloader.figshare.com/files/664015"], "description"=>"<p>(A) The average walking speed of all participants as they were walking alone in the experimental corridor. The grey area indicates the standard deviation of the mean. The dashed lines are the limits of the measurement zone, where the pedestrians are assumed to have reached their comfortable walking speed. (B) The comfortable walking speeds are normally distributed with mean  = 1.2 m/s and standard deviation  = 0.16 (a Kolmogorov-Smirnov test yields a p-value of 0.73).</p>", "links"=>[], "tags"=>["behaviour"], "article_id"=>334498, "categories"=>["Biological Sciences"], "users"=>["Mehdi Moussaïd", "Elsa G. Guillot", "Mathieu Moreau", "Jérôme Fehrenbach", "Olivier Chabiron", "Samuel Lemercier", "Julien Pettré", "Cécile Appert-Rolland", "Pierre Degond", "Guy Theraulaz"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002442.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characterization_of_the_walking_behaviour_during_the_control_test_/334498", "title"=>"Characterization of the walking behaviour during the control test.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-22 01:14:58"}
• {"files"=>["https://ndownloader.figshare.com/files/664142"], "description"=>"<p>(A) As speed variability increases from  = 0 to  = 0.3, the model predicts an increasingly unstable segregation dynamics. These instabilities go along with the emergence of increasingly sharp density gaps (B), which leads to stronger and more frequent lateral movements (C). The time and place where lateral movements occur in (C) fit with the propagation of density waves in (B) and explain the unstable dynamics observed in (A).</p>", "links"=>[], "tags"=>["observed"], "article_id"=>334621, "categories"=>["Biological Sciences"], "users"=>["Mehdi Moussaïd", "Elsa G. Guillot", "Mathieu Moreau", "Jérôme Fehrenbach", "Olivier Chabiron", "Samuel Lemercier", "Julien Pettré", "Cécile Appert-Rolland", "Pierre Degond", "Guy Theraulaz"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002442.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_the_dynamics_observed_during_computer_simulations_/334621", "title"=>"Illustration of the dynamics observed during computer simulations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-22 01:17:01"}
• {"files"=>["https://ndownloader.figshare.com/files/664229"], "description"=>"<p>(A) Cluster lifetime as a function of the standard deviation of the comfortable walking speed distribution, as predicted by numerical simulations. The decreasing curves demonstrate the relationship between inter-individual variability and traffic instabilities. The width of the curves indicates the 95% confidence bounds of the lifetime estimation. (B) The collective payoff provided by the lane organization, as a function of . (C) The individual payoff of pedestrians averaged over all simulations for N = 30, N = 50, and N = 60, grouped according to their desired walking speed. The black areas indicate the absence of value.</p>", "links"=>[], "tags"=>["Computational biology"], "article_id"=>334712, "categories"=>["Biological Sciences"], "users"=>["Mehdi Moussaïd", "Elsa G. Guillot", "Mathieu Moreau", "Jérôme Fehrenbach", "Olivier Chabiron", "Samuel Lemercier", "Julien Pettré", "Cécile Appert-Rolland", "Pierre Degond", "Guy Theraulaz"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002442.g008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Collective_dynamics_predicted_in_simulations_/334712", "title"=>"Collective dynamics predicted in simulations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-03-22 01:18:32"}

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• {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2021", "month"=>"2"}

Relative Metric

{"start_date"=>"2012-01-01T00:00:00Z", "end_date"=>"2012-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[322, 550, 671, 773, 864, 955, 1048, 1135, 1223, 1308, 1387, 1465, 1534, 1602, 1673, 1744, 1813, 1885, 1955, 2026, 2093, 2160, 2228, 2290, 2349]}, {"subject_area"=>"/Biology and life sciences/Behavior", "average_usage"=>[354, 559, 676, 770, 854, 949, 1040, 1128, 1220, 1295, 1364, 1439, 1511, 1580, 1636, 1693, 1753, 1808, 1882, 1944, 2019, 2070, 2120, 2176, 2247]}, {"subject_area"=>"/Biology and life sciences/Neuroscience", "average_usage"=>[325, 542, 658, 757, 836, 925, 1015, 1095, 1177, 1263, 1338, 1411, 1477, 1537, 1599, 1663, 1726, 1795, 1857, 1921, 1979, 2041, 2099, 2150, 2205]}, {"subject_area"=>"/Computer and information sciences/Systems science", "average_usage"=>[331, 552, 654, 739, 811, 881, 963, 1040, 1088, 1156, 1222, 1308, 1369, 1458, 1542, 1601, 1693, 1735, 1785, 1871, 1939, 2008, 2067, 2103, 2169]}, {"subject_area"=>"/Ecology and environmental sciences", "average_usage"=>[348, 541, 638, 730, 827, 907, 993, 1074, 1152, 1232, 1310, 1379, 1442, 1507, 1576, 1648, 1715, 1786, 1854, 1926, 1991, 2059, 2125, 2181, 2249]}, {"subject_area"=>"/Social sciences", "average_usage"=>[354, 585, 710, 810, 904, 998, 1090, 1181, 1268, 1361, 1435, 1525, 1594, 1657, 1731, 1813, 1878, 1948, 2016, 2094, 2158, 2219, 2294, 2347, 2410]}, {"subject_area"=>"/Social sciences/Sociology", "average_usage"=>[379, 645, 772, 877, 988, 1089, 1191, 1313, 1410, 1521, 1621, 1696, 1785, 1857, 1932, 2023, 2100, 2186, 2256, 2345, 2429, 2505, 2595, 2646, 2741]}]}

F1000Prime