{"data":{"id":"10.6084/m9.figshare.c.3491559.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.3491559.v1","prefix":"10.6084","suffix":"m9.figshare.c.3491559.v1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Solé-Ribalta, Albert","nameType":"Personal","givenName":"Albert","familyName":"Solé-Ribalta","affiliation":[],"nameIdentifiers":[]},{"name":"Gómez, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Gómez","affiliation":[],"nameIdentifiers":[]},{"name":"Arenas, Alex","nameType":"Personal","givenName":"Alex","familyName":"Arenas","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Supplementary material from \"A model to identify urban traffic congestion hotspots in complex networks\""}],"publisher":"Figshare","container":{},"publicationYear":2016,"subjects":[{"subject":"20304 Thermodynamics and Statistical Physics","subjectScheme":"FOR"},{"subject":"FOS: Mechanical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80201 Analysis of Algorithms and Complexity","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2016-10-04","dateType":"Created"},{"date":"2016-10-04","dateType":"Updated"},{"date":"2016","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1098/rsos.160098","relatedIdentifierType":"DOI"},{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.c.3491559","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC-BY","rightsUri":"https://creativecommons.org/licenses/by/4.0"}],"descriptions":[{"description":"The rapid growth of population in urban areas is jeopardizing the mobility and air quality worldwide. One of the most notable problems arising is that of traffic congestion. With the advent of technologies able to sense real-time data about cities, and its public distribution for analysis, we are in place to forecast scenarios valuable for improvement and control. Here, we propose an idealized model, based on the critical phenomena arising in complex networks, that allows to analytically predict congestion hotspots in urban environments. Results on real cities' road networks, considering, in some experiments, real-traffic data, show that the proposed model is capable of identifying susceptible junctions that might become hotspots if mobility demand increases.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHJlc291cmNlIHhtbG5zOnhzaT0iaHR0cDovL3d3dy53My5vcmcvMjAwMS9YTUxTY2hlbWEtaW5zdGFuY2UiIHhtbG5zPSJodHRwOi8vZGF0YWNpdGUub3JnL3NjaGVtYS9rZXJuZWwtNCIgeHNpOnNjaGVtYUxvY2F0aW9uPSJodHRwOi8vZGF0YWNpdGUub3JnL3NjaGVtYS9rZXJuZWwtNCBodHRwOi8vc2NoZW1hLmRhdGFjaXRlLm9yZy9tZXRhL2tlcm5lbC00L21ldGFkYXRhLnhzZCI+CiAgPGlkZW50aWZpZXIgaWRlbnRpZmllclR5cGU9IkRPSSI+MTAuNjA4NC9NOS5GSUdTSEFSRS5DLjM0OTE1NTkuVjE8L2lkZW50aWZpZXI+CiAgPGNyZWF0b3JzPgogICAgPGNyZWF0b3I+CiAgICAgIDxjcmVhdG9yTmFtZSBuYW1lVHlwZT0iUGVyc29uYWwiPlNvbMOpLVJpYmFsdGEsIEFsYmVydDwvY3JlYXRvck5hbWU+CiAgICAgIDxnaXZlbk5hbWU+QWxiZXJ0PC9naXZlbk5hbWU+CiAgICAgIDxmYW1pbHlOYW1lPlNvbMOpLVJpYmFsdGE8L2ZhbWlseU5hbWU+CiAgICA8L2NyZWF0b3I+CiAgICA8Y3JlYXRvcj4KICAgICAgPGNyZWF0b3JOYW1lIG5hbWVUeXBlPSJQZXJzb25hbCI+R8OzbWV6LCBTZXJnaW88L2NyZWF0b3JOYW1lPgogICAgICA8Z2l2ZW5OYW1lPlNlcmdpbzwvZ2l2ZW5OYW1lPgogICAgICA8ZmFtaWx5TmFtZT5Hw7NtZXo8L2ZhbWlseU5hbWU+CiAgICA8L2NyZWF0b3I+CiAgICA8Y3JlYXRvcj4KICAgICAgPGNyZWF0b3JOYW1lIG5hbWVUeXBlPSJQZXJzb25hbCI+QXJlbmFzLCBBbGV4PC9jcmVhdG9yTmFtZT4KICAgICAgPGdpdmVuTmFtZT5BbGV4PC9naXZlbk5hbWU+CiAgICAgIDxmYW1pbHlOYW1lPkFyZW5hczwvZmFtaWx5TmFtZT4KICAgIDwvY3JlYXRvcj4KICA8L2NyZWF0b3JzPgogIDx0aXRsZXM+CiAgICA8dGl0bGU+U3VwcGxlbWVudGFyeSBtYXRlcmlhbCBmcm9tICJBIG1vZGVsIHRvIGlkZW50aWZ5IHVyYmFuIHRyYWZmaWMgY29uZ2VzdGlvbiBob3RzcG90cyBpbiBjb21wbGV4IG5ldHdvcmtzIjwvdGl0bGU+CiAgPC90aXRsZXM+CiAgPHB1Ymxpc2hlcj5GaWdzaGFyZTwvcHVibGlzaGVyPgogIDxwdWJsaWNhdGlvblllYXI+MjAxNjwvcHVibGljYXRpb25ZZWFyPgogIDxyZXNvdXJjZVR5cGUgcmVzb3VyY2VUeXBlR2VuZXJhbD0iQ29sbGVjdGlvbiI+Q29sbGVjdGlvbjwvcmVzb3VyY2VUeXBlPgogIDxzdWJqZWN0cz4KICAgIDxzdWJqZWN0IHN1YmplY3RTY2hlbWU9IkZPUiI+MjAzMDQgVGhlcm1vZHluYW1pY3MgYW5kIFN0YXRpc3RpY2FsIFBoeXNpY3M8L3N1YmplY3Q+CiAgICA8c3ViamVjdCBzdWJqZWN0U2NoZW1lPSJGaWVsZHMgb2YgU2NpZW5jZSBhbmQgVGVjaG5vbG9neSAoRk9TKSIgc2NoZW1lVVJJPSJodHRwOi8vd3d3Lm9lY2Qub3JnL3NjaWVuY2UvaW5uby8zODIzNTE0Ny5wZGYiPkZPUzogTWVjaGFuaWNhbCBlbmdpbmVlcmluZzwvc3ViamVjdD4KICAgIDxzdWJqZWN0IHN1YmplY3RTY2hlbWU9IkZPUiI+ODAyMDEgQW5hbHlzaXMgb2YgQWxnb3JpdGhtcyBhbmQgQ29tcGxleGl0eTwvc3ViamVjdD4KICAgIDxzdWJqZWN0IHN1YmplY3RTY2hlbWU9IkZpZWxkcyBvZiBTY2llbmNlIGFuZCBUZWNobm9sb2d5IChGT1MpIiBzY2hlbWVVUkk9Imh0dHA6Ly93d3cub2VjZC5vcmcvc2NpZW5jZS9pbm5vLzM4MjM1MTQ3LnBkZiI+Rk9TOiBDb21wdXRlciBhbmQgaW5mb3JtYXRpb24gc2NpZW5jZXM8L3N1YmplY3Q+CiAgPC9zdWJqZWN0cz4KICA8ZGF0ZXM+CiAgICA8ZGF0ZSBkYXRlVHlwZT0iQ3JlYXRlZCI+MjAxNi0xMC0wNDwvZGF0ZT4KICAgIDxkYXRlIGRhdGVUeXBlPSJVcGRhdGVkIj4yMDE2LTEwLTA0PC9kYXRlPgogICAgPGRhdGUgZGF0ZVR5cGU9Iklzc3VlZCI+MjAxNjwvZGF0ZT4KICA8L2RhdGVzPgogIDxyZWxhdGVkSWRlbnRpZmllcnM+CiAgICA8cmVsYXRlZElkZW50aWZpZXIgcmVsYXRlZElkZW50aWZpZXJUeXBlPSJET0kiIHJlbGF0aW9uVHlwZT0iSXNTdXBwbGVtZW50VG8iPjEwLjEwOTgvcnNvcy4xNjAwOTg8L3JlbGF0ZWRJZGVudGlmaWVyPgogICAgPHJlbGF0ZWRJZGVudGlmaWVyIHJlbGF0ZWRJZGVudGlmaWVyVHlwZT0iRE9JIiByZWxhdGlvblR5cGU9IklzSWRlbnRpY2FsVG8iPjEwLjYwODQvbTkuZmlnc2hhcmUuYy4zNDkxNTU5PC9yZWxhdGVkSWRlbnRpZmllcj4KICA8L3JlbGF0ZWRJZGVudGlmaWVycz4KICA8c2l6ZXMvPgogIDxmb3JtYXRzLz4KICA8dmVyc2lvbi8+CiAgPHJpZ2h0c0xpc3Q+CiAgICA8cmlnaHRzIHJpZ2h0c1VSST0iaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LzQuMCI+Q0MtQlk8L3JpZ2h0cz4KICA8L3JpZ2h0c0xpc3Q+CiAgPGRlc2NyaXB0aW9ucz4KICAgIDxkZXNjcmlwdGlvbiBkZXNjcmlwdGlvblR5cGU9IkFic3RyYWN0Ij5UaGUgcmFwaWQgZ3Jvd3RoIG9mIHBvcHVsYXRpb24gaW4gdXJiYW4gYXJlYXMgaXMgamVvcGFyZGl6aW5nIHRoZSBtb2JpbGl0eSBhbmQgYWlyIHF1YWxpdHkgd29ybGR3aWRlLiBPbmUgb2YgdGhlIG1vc3Qgbm90YWJsZSBwcm9ibGVtcyBhcmlzaW5nIGlzIHRoYXQgb2YgdHJhZmZpYyBjb25nZXN0aW9uLiBXaXRoIHRoZSBhZHZlbnQgb2YgdGVjaG5vbG9naWVzIGFibGUgdG8gc2Vuc2UgcmVhbC10aW1lIGRhdGEgYWJvdXQgY2l0aWVzLCBhbmQgaXRzIHB1YmxpYyBkaXN0cmlidXRpb24gZm9yIGFuYWx5c2lzLCB3ZSBhcmUgaW4gcGxhY2UgdG8gZm9yZWNhc3Qgc2NlbmFyaW9zIHZhbHVhYmxlIGZvciBpbXByb3ZlbWVudCBhbmQgY29udHJvbC4gSGVyZSwgd2UgcHJvcG9zZSBhbiBpZGVhbGl6ZWQgbW9kZWwsIGJhc2VkIG9uIHRoZSBjcml0aWNhbCBwaGVub21lbmEgYXJpc2luZyBpbiBjb21wbGV4IG5ldHdvcmtzLCB0aGF0IGFsbG93cyB0byBhbmFseXRpY2FsbHkgcHJlZGljdCBjb25nZXN0aW9uIGhvdHNwb3RzIGluIHVyYmFuIGVudmlyb25tZW50cy4gUmVzdWx0cyBvbiByZWFsIGNpdGllcycgcm9hZCBuZXR3b3JrcywgY29uc2lkZXJpbmcsIGluIHNvbWUgZXhwZXJpbWVudHMsIHJlYWwtdHJhZmZpYyBkYXRhLCBzaG93IHRoYXQgdGhlIHByb3Bvc2VkIG1vZGVsIGlzIGNhcGFibGUgb2YgaWRlbnRpZnlpbmcgc3VzY2VwdGlibGUganVuY3Rpb25zIHRoYXQgbWlnaHQgYmVjb21lIGhvdHNwb3RzIGlmIG1vYmlsaXR5IGRlbWFuZCBpbmNyZWFzZXMuPC9kZXNjcmlwdGlvbj4KICA8L2Rlc2NyaXB0aW9ucz4KPC9yZXNvdXJjZT4K","url":"https://figshare.com/collections/Supplementary_material_from_A_model_to_identify_urban_traffic_congestion_hotspots_in_complex_networks_/3491559/1","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":1,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2016-10-04T06:23:01.000Z","registered":"2016-10-04T06:23:02.000Z","published":"2016","updated":"2024-12-15T07: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