The Impact of an Obstacle on Competitive Evacuation Through a Bottleneck View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-11

AUTHORS

Peng Lin, Dong li Gao, Guo Yuan Wang, Fan Yu Wu, Jian Ma, You Liang Si, Tong Ran

ABSTRACT

Within the last decade, a number of crowd accidents have attracted the interest of scientists to study the movement of crowd under high levels of competition. An interesting finding in crowd dynamics is that the presence of an obstacle at an appropriate distance in front of an exit can improve flow rate. A numerical simulation based on the discrete element method (DEM) and social force model was adopted in this paper in order to study the impact of obstacles on flow rate during competitive evacuation in two typical Settings with a single 0.8 m-wide exit. Setting I is a 15 m-wide room and Setting II is a 3 m wide corridor formed by the same room with lateral constrains. Numerical simulation in Setting I showed that a 1 m-obstacle at 1-m away from the exit can reduce evacuation time by approximately 49%. However, a contrasting increase of evacuation time by approximately 64% is observed in Setting II for the obstacle at the same location. To verify the findings, a number of experiments were conducted by compelling mice to escape under high competition in two similar settings. The general limitations of such experiments were accepted and described. These results were consistent with numerical simulations. This study demonstrates that whether an obstacle can improve the flow rate passing through an exit is dependent on the surrounding geometry. More... »

PAGES

1-15

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10694-019-00838-4

DOI

http://dx.doi.org/10.1007/s10694-019-00838-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112685184


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Southwest Jiaotong University", 
          "id": "https://www.grid.ac/institutes/grid.263901.f", 
          "name": [
            "Department of Fire Safety Engineering, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, 610031, Chengdu, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lin", 
        "givenName": "Peng", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Southwest Jiaotong University", 
          "id": "https://www.grid.ac/institutes/grid.263901.f", 
          "name": [
            "Department of Fire Safety Engineering, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, 610031, Chengdu, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gao", 
        "givenName": "Dong li", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Southwest Jiaotong University", 
          "id": "https://www.grid.ac/institutes/grid.263901.f", 
          "name": [
            "Department of Fire Safety Engineering, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, 610031, Chengdu, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Guo Yuan", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Southwest Jiaotong University", 
          "id": "https://www.grid.ac/institutes/grid.263901.f", 
          "name": [
            "Department of Fire Safety Engineering, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, 610031, Chengdu, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wu", 
        "givenName": "Fan Yu", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Southwest Jiaotong University", 
          "id": "https://www.grid.ac/institutes/grid.263901.f", 
          "name": [
            "School of Transportation and Logistics, Southwest Jiaotong University, 610031, Chengdu, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ma", 
        "givenName": "Jian", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Southwest Jiaotong University", 
          "id": "https://www.grid.ac/institutes/grid.263901.f", 
          "name": [
            "Department of Fire Safety Engineering, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, 610031, Chengdu, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Si", 
        "givenName": "You Liang", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Southwest Jiaotong University", 
          "id": "https://www.grid.ac/institutes/grid.263901.f", 
          "name": [
            "Department of Fire Safety Engineering, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, 610031, Chengdu, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ran", 
        "givenName": "Tong", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s0301-679x(00)00063-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003024256"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physa.2016.02.017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007500611"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35035023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017951524", 
          "https://doi.org/10.1038/35035023"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35035023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017951524", 
          "https://doi.org/10.1038/35035023"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physa.2011.01.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032969368"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.85.020301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033215843"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.85.020301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033215843"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.firesaf.2006.12.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035673833"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.86.031306", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037822701"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.86.031306", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037822701"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep07324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039323196", 
          "https://doi.org/10.1038/srep07324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.80.036110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047038812"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.80.036110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047038812"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-00318-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052587810", 
          "https://doi.org/10.1007/978-3-319-00318-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-00318-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052587810", 
          "https://doi.org/10.1007/978-3-319-00318-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.trb.2017.01.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053972786"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1674-1056/25/3/034501", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059156221"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.91.022808", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060747156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.91.022808", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060747156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.94.032302", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060750460"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.94.032302", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060750460"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.107.278001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060759234"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.107.278001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060759234"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/s0129183116500911", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062905943"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/trsc.1040.0102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064734139"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physa.2017.01.079", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074208980"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.physa.2017.04.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085210940"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1674-1056/26/10/104501", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092103722"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ifacol.2017.08.333", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092295478"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-03-11", 
    "datePublishedReg": "2019-03-11", 
    "description": "Within the last decade, a number of crowd accidents have attracted the interest of scientists to study the movement of crowd under high levels of competition. An interesting finding in crowd dynamics is that the presence of an obstacle at an appropriate distance in front of an exit can improve flow rate. A numerical simulation based on the discrete element method (DEM) and social force model was adopted in this paper in order to study the impact of obstacles on flow rate during competitive evacuation in two typical Settings with a single 0.8 m-wide exit. Setting I is a 15 m-wide room and Setting II is a 3 m wide corridor formed by the same room with lateral constrains. Numerical simulation in Setting I showed that a 1 m-obstacle at 1-m away from the exit can reduce evacuation time by approximately 49%. However, a contrasting increase of evacuation time by approximately 64% is observed in Setting II for the obstacle at the same location. To verify the findings, a number of experiments were conducted by compelling mice to escape under high competition in two similar settings. The general limitations of such experiments were accepted and described. These results were consistent with numerical simulations. This study demonstrates that whether an obstacle can improve the flow rate passing through an exit is dependent on the surrounding geometry.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10694-019-00838-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1122008", 
        "issn": [
          "0015-2684", 
          "1572-8099"
        ], 
        "name": "Fire Technology", 
        "type": "Periodical"
      }
    ], 
    "name": "The Impact of an Obstacle on Competitive Evacuation Through a Bottleneck", 
    "pagination": "1-15", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "b940b0a1bee6c9746a3e5be058d10cafacbd41e9d14435e26402ffb128404383"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10694-019-00838-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112685184"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10694-019-00838-4", 
      "https://app.dimensions.ai/details/publication/pub.1112685184"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:30", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000357_0000000357/records_99302_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10694-019-00838-4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10694-019-00838-4'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10694-019-00838-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10694-019-00838-4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10694-019-00838-4'


 

This table displays all metadata directly associated to this object as RDF triples.

157 TRIPLES      21 PREDICATES      45 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10694-019-00838-4 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N2951db0f34804dcb9991ad260c32355d
4 schema:citation sg:pub.10.1007/978-3-319-00318-4
5 sg:pub.10.1038/35035023
6 sg:pub.10.1038/srep07324
7 https://doi.org/10.1016/j.firesaf.2006.12.007
8 https://doi.org/10.1016/j.ifacol.2017.08.333
9 https://doi.org/10.1016/j.physa.2011.01.015
10 https://doi.org/10.1016/j.physa.2016.02.017
11 https://doi.org/10.1016/j.physa.2017.01.079
12 https://doi.org/10.1016/j.physa.2017.04.021
13 https://doi.org/10.1016/j.trb.2017.01.008
14 https://doi.org/10.1016/s0301-679x(00)00063-3
15 https://doi.org/10.1088/1674-1056/25/3/034501
16 https://doi.org/10.1088/1674-1056/26/10/104501
17 https://doi.org/10.1103/physreve.80.036110
18 https://doi.org/10.1103/physreve.85.020301
19 https://doi.org/10.1103/physreve.86.031306
20 https://doi.org/10.1103/physreve.91.022808
21 https://doi.org/10.1103/physreve.94.032302
22 https://doi.org/10.1103/physrevlett.107.278001
23 https://doi.org/10.1142/s0129183116500911
24 https://doi.org/10.1287/trsc.1040.0102
25 schema:datePublished 2019-03-11
26 schema:datePublishedReg 2019-03-11
27 schema:description Within the last decade, a number of crowd accidents have attracted the interest of scientists to study the movement of crowd under high levels of competition. An interesting finding in crowd dynamics is that the presence of an obstacle at an appropriate distance in front of an exit can improve flow rate. A numerical simulation based on the discrete element method (DEM) and social force model was adopted in this paper in order to study the impact of obstacles on flow rate during competitive evacuation in two typical Settings with a single 0.8 m-wide exit. Setting I is a 15 m-wide room and Setting II is a 3 m wide corridor formed by the same room with lateral constrains. Numerical simulation in Setting I showed that a 1 m-obstacle at 1-m away from the exit can reduce evacuation time by approximately 49%. However, a contrasting increase of evacuation time by approximately 64% is observed in Setting II for the obstacle at the same location. To verify the findings, a number of experiments were conducted by compelling mice to escape under high competition in two similar settings. The general limitations of such experiments were accepted and described. These results were consistent with numerical simulations. This study demonstrates that whether an obstacle can improve the flow rate passing through an exit is dependent on the surrounding geometry.
28 schema:genre research_article
29 schema:inLanguage en
30 schema:isAccessibleForFree false
31 schema:isPartOf sg:journal.1122008
32 schema:name The Impact of an Obstacle on Competitive Evacuation Through a Bottleneck
33 schema:pagination 1-15
34 schema:productId N0b920a03f63043788b64aceaf8469273
35 N2deb9982df2b49d3843de2ebc04aa3ca
36 N8e01164cb80945088dcfbd22736b9063
37 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112685184
38 https://doi.org/10.1007/s10694-019-00838-4
39 schema:sdDatePublished 2019-04-11T11:30
40 schema:sdLicense https://scigraph.springernature.com/explorer/license/
41 schema:sdPublisher Nf6ab525c0fbb4ee1ad76b507d18b67d4
42 schema:url https://link.springer.com/10.1007%2Fs10694-019-00838-4
43 sgo:license sg:explorer/license/
44 sgo:sdDataset articles
45 rdf:type schema:ScholarlyArticle
46 N08d45852de0f4564b3b5e76174f2d9a9 schema:affiliation https://www.grid.ac/institutes/grid.263901.f
47 schema:familyName Wu
48 schema:givenName Fan Yu
49 rdf:type schema:Person
50 N0b920a03f63043788b64aceaf8469273 schema:name readcube_id
51 schema:value b940b0a1bee6c9746a3e5be058d10cafacbd41e9d14435e26402ffb128404383
52 rdf:type schema:PropertyValue
53 N115bc53592df4ef890ff90cad4c62b34 rdf:first N38523b27f3ba46bfaf52dbd7b33c5a1c
54 rdf:rest Ncb4f1cbf6177427a94d8b65ecb46f77e
55 N294608363cfa428bae7f96e28a2c8ab1 schema:affiliation https://www.grid.ac/institutes/grid.263901.f
56 schema:familyName Ran
57 schema:givenName Tong
58 rdf:type schema:Person
59 N2951db0f34804dcb9991ad260c32355d rdf:first N6b4a604a00d44569964256b614ddd9ca
60 rdf:rest Ne8830a0ff8204ab582cc1b444dd95ea6
61 N2deb9982df2b49d3843de2ebc04aa3ca schema:name doi
62 schema:value 10.1007/s10694-019-00838-4
63 rdf:type schema:PropertyValue
64 N38523b27f3ba46bfaf52dbd7b33c5a1c schema:affiliation https://www.grid.ac/institutes/grid.263901.f
65 schema:familyName Ma
66 schema:givenName Jian
67 rdf:type schema:Person
68 N47d5bdc88bab4e9298e08a312ebba69e rdf:first N294608363cfa428bae7f96e28a2c8ab1
69 rdf:rest rdf:nil
70 N6b4a604a00d44569964256b614ddd9ca schema:affiliation https://www.grid.ac/institutes/grid.263901.f
71 schema:familyName Lin
72 schema:givenName Peng
73 rdf:type schema:Person
74 N7725f0fc051d466ea1d1a379397fdf30 rdf:first N08d45852de0f4564b3b5e76174f2d9a9
75 rdf:rest N115bc53592df4ef890ff90cad4c62b34
76 N8dc5c4b5a72442ce9d0c5ef73994699d rdf:first Nc61faadc6bc7470da17790d9d680442c
77 rdf:rest N7725f0fc051d466ea1d1a379397fdf30
78 N8e01164cb80945088dcfbd22736b9063 schema:name dimensions_id
79 schema:value pub.1112685184
80 rdf:type schema:PropertyValue
81 N9b664325da1847cfa3aca73a5e8f4e7e schema:affiliation https://www.grid.ac/institutes/grid.263901.f
82 schema:familyName Gao
83 schema:givenName Dong li
84 rdf:type schema:Person
85 Nc61faadc6bc7470da17790d9d680442c schema:affiliation https://www.grid.ac/institutes/grid.263901.f
86 schema:familyName Wang
87 schema:givenName Guo Yuan
88 rdf:type schema:Person
89 Ncb4f1cbf6177427a94d8b65ecb46f77e rdf:first Ndab84af527d743c583a17740701c73cb
90 rdf:rest N47d5bdc88bab4e9298e08a312ebba69e
91 Ndab84af527d743c583a17740701c73cb schema:affiliation https://www.grid.ac/institutes/grid.263901.f
92 schema:familyName Si
93 schema:givenName You Liang
94 rdf:type schema:Person
95 Ne8830a0ff8204ab582cc1b444dd95ea6 rdf:first N9b664325da1847cfa3aca73a5e8f4e7e
96 rdf:rest N8dc5c4b5a72442ce9d0c5ef73994699d
97 Nf6ab525c0fbb4ee1ad76b507d18b67d4 schema:name Springer Nature - SN SciGraph project
98 rdf:type schema:Organization
99 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
100 schema:name Information and Computing Sciences
101 rdf:type schema:DefinedTerm
102 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
103 schema:name Artificial Intelligence and Image Processing
104 rdf:type schema:DefinedTerm
105 sg:journal.1122008 schema:issn 0015-2684
106 1572-8099
107 schema:name Fire Technology
108 rdf:type schema:Periodical
109 sg:pub.10.1007/978-3-319-00318-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052587810
110 https://doi.org/10.1007/978-3-319-00318-4
111 rdf:type schema:CreativeWork
112 sg:pub.10.1038/35035023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017951524
113 https://doi.org/10.1038/35035023
114 rdf:type schema:CreativeWork
115 sg:pub.10.1038/srep07324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039323196
116 https://doi.org/10.1038/srep07324
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/j.firesaf.2006.12.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035673833
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1016/j.ifacol.2017.08.333 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092295478
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1016/j.physa.2011.01.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032969368
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1016/j.physa.2016.02.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007500611
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1016/j.physa.2017.01.079 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074208980
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/j.physa.2017.04.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085210940
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1016/j.trb.2017.01.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053972786
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/s0301-679x(00)00063-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003024256
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1088/1674-1056/25/3/034501 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059156221
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1088/1674-1056/26/10/104501 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092103722
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1103/physreve.80.036110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047038812
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1103/physreve.85.020301 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033215843
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1103/physreve.86.031306 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037822701
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1103/physreve.91.022808 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060747156
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1103/physreve.94.032302 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060750460
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1103/physrevlett.107.278001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060759234
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1142/s0129183116500911 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062905943
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1287/trsc.1040.0102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064734139
153 rdf:type schema:CreativeWork
154 https://www.grid.ac/institutes/grid.263901.f schema:alternateName Southwest Jiaotong University
155 schema:name Department of Fire Safety Engineering, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, 610031, Chengdu, People’s Republic of China
156 School of Transportation and Logistics, Southwest Jiaotong University, 610031, Chengdu, People’s Republic of China
157 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...