The analysis and solution for intercity travel behaviors during holidays in the post-epidemic era based on big data
The COVID-19 had a huge impact on the transportation industry. In the post-epidemic stage, intercity transportation will face great challenges as places are unsealed, tourism and other service industries begin to recover, and residents' travel demand gradually increases. An in-depth study of re...
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description | The COVID-19 had a huge impact on the transportation industry. In the post-epidemic stage, intercity transportation will face great challenges as places are unsealed, tourism and other service industries begin to recover, and residents' travel demand gradually increases. An in-depth study of residents' intercity travel behavior during holidays in the post-epidemic era will help restore public trust in public transportation and improve the quality of public transportation services. Based on traditional research on ways of travelling, the study adopted the Complex Network Analysis Theory. The city clusters of Shandong Peninsula were taken as the research region. The research studied the impact of the differences in regional attributes of the cities in Shandong Peninsula on residents' intercity travel in the post-epidemic times. A dynamic evolution model of how residents choose to travel was built to simulate the changes to their ways of traveling in the post-epidemic era under two conditions, which are: traveling under the government's supervision of intercity travel and traveling under the government's optimization of intercity travel conditions. The conclusions drawn from the analyses of Complex Network Theory and Evolutionary Game Theory are as follows. First, in the holiday intercity travel in the post-epidemic times, the neighboring cities of Shandong Peninsula are closely connected, thus traveling between neighboring cities dominates intercity travel. Second, the travel network concentration of residents on long-term holidays is lower than that on short-term holidays, and the migration intensity of residents is higher than that on short-term holidays, while the willingness of residents' migration on short-term holidays is higher than that on long-term holidays. The willingness to migrate on holidays is generally lower than that before the epidemic. Third, in a normal intercity travel network, the travel between two cities with medium and long distances is mainly by public transport. However, the dominance of public transport will be affected under the impact of the epidemic. In short-distance travel between two cities, private transport is in an advantageous position, and under the impact of the epidemic, this advantage will become more significant. The government can improve the position of public transport in short-distance travel by making optimizations. |
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In the post-epidemic stage, intercity transportation will face great challenges as places are unsealed, tourism and other service industries begin to recover, and residents' travel demand gradually increases. An in-depth study of residents' intercity travel behavior during holidays in the post-epidemic era will help restore public trust in public transportation and improve the quality of public transportation services. Based on traditional research on ways of travelling, the study adopted the Complex Network Analysis Theory. The city clusters of Shandong Peninsula were taken as the research region. The research studied the impact of the differences in regional attributes of the cities in Shandong Peninsula on residents' intercity travel in the post-epidemic times. A dynamic evolution model of how residents choose to travel was built to simulate the changes to their ways of traveling in the post-epidemic era under two conditions, which are: traveling under the government's supervision of intercity travel and traveling under the government's optimization of intercity travel conditions. The conclusions drawn from the analyses of Complex Network Theory and Evolutionary Game Theory are as follows. First, in the holiday intercity travel in the post-epidemic times, the neighboring cities of Shandong Peninsula are closely connected, thus traveling between neighboring cities dominates intercity travel. Second, the travel network concentration of residents on long-term holidays is lower than that on short-term holidays, and the migration intensity of residents is higher than that on short-term holidays, while the willingness of residents' migration on short-term holidays is higher than that on long-term holidays. The willingness to migrate on holidays is generally lower than that before the epidemic. Third, in a normal intercity travel network, the travel between two cities with medium and long distances is mainly by public transport. However, the dominance of public transport will be affected under the impact of the epidemic. In short-distance travel between two cities, private transport is in an advantageous position, and under the impact of the epidemic, this advantage will become more significant. The government can improve the position of public transport in short-distance travel by making optimizations.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0288510</identifier><identifier>PMID: 37467244</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Behavior ; Big Data ; Biology and Life Sciences ; Cities ; Computer and Information Sciences ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - prevention & control ; Decision making ; Earth Sciences ; Engineering and Technology ; Epidemics ; Epidemics - prevention & control ; Game theory ; Health aspects ; Holidays ; Holidays & special occasions ; Humans ; Interurban travel ; Network analysis ; Optimization ; Physical Sciences ; Public transportation ; Service industries ; Social Sciences ; Tourism ; Transportation industry ; Transportation networks ; Transportation services ; Travel ; Travel demand ; Travel industry</subject><ispartof>PloS one, 2023-07, Vol.18 (7), p.e0288510-e0288510</ispartof><rights>Copyright: © 2023 Zhang, Gao. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Zhang, Gao. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Zhang, Gao 2023 Zhang, Gao</rights><rights>2023 Zhang, Gao. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c576t-caccf8011408e12055e44de40055949fc2dc60e7c2512c14bc04172dc8091bba3</cites><orcidid>0009-0005-6646-390X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10355389/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10355389/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37467244$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Cheng, Jing</contributor><creatorcontrib>Zhang, Xike</creatorcontrib><creatorcontrib>Gao, Jiaqi</creatorcontrib><title>The analysis and solution for intercity travel behaviors during holidays in the post-epidemic era based on big data</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The COVID-19 had a huge impact on the transportation industry. In the post-epidemic stage, intercity transportation will face great challenges as places are unsealed, tourism and other service industries begin to recover, and residents' travel demand gradually increases. An in-depth study of residents' intercity travel behavior during holidays in the post-epidemic era will help restore public trust in public transportation and improve the quality of public transportation services. Based on traditional research on ways of travelling, the study adopted the Complex Network Analysis Theory. The city clusters of Shandong Peninsula were taken as the research region. The research studied the impact of the differences in regional attributes of the cities in Shandong Peninsula on residents' intercity travel in the post-epidemic times. A dynamic evolution model of how residents choose to travel was built to simulate the changes to their ways of traveling in the post-epidemic era under two conditions, which are: traveling under the government's supervision of intercity travel and traveling under the government's optimization of intercity travel conditions. The conclusions drawn from the analyses of Complex Network Theory and Evolutionary Game Theory are as follows. First, in the holiday intercity travel in the post-epidemic times, the neighboring cities of Shandong Peninsula are closely connected, thus traveling between neighboring cities dominates intercity travel. Second, the travel network concentration of residents on long-term holidays is lower than that on short-term holidays, and the migration intensity of residents is higher than that on short-term holidays, while the willingness of residents' migration on short-term holidays is higher than that on long-term holidays. The willingness to migrate on holidays is generally lower than that before the epidemic. Third, in a normal intercity travel network, the travel between two cities with medium and long distances is mainly by public transport. However, the dominance of public transport will be affected under the impact of the epidemic. In short-distance travel between two cities, private transport is in an advantageous position, and under the impact of the epidemic, this advantage will become more significant. The government can improve the position of public transport in short-distance travel by making optimizations.</description><subject>Analysis</subject><subject>Behavior</subject><subject>Big Data</subject><subject>Biology and Life Sciences</subject><subject>Cities</subject><subject>Computer and Information Sciences</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 - prevention & control</subject><subject>Decision making</subject><subject>Earth Sciences</subject><subject>Engineering and Technology</subject><subject>Epidemics</subject><subject>Epidemics - prevention & control</subject><subject>Game theory</subject><subject>Health aspects</subject><subject>Holidays</subject><subject>Holidays & special occasions</subject><subject>Humans</subject><subject>Interurban travel</subject><subject>Network analysis</subject><subject>Optimization</subject><subject>Physical Sciences</subject><subject>Public transportation</subject><subject>Service industries</subject><subject>Social Sciences</subject><subject>Tourism</subject><subject>Transportation industry</subject><subject>Transportation networks</subject><subject>Transportation services</subject><subject>Travel</subject><subject>Travel demand</subject><subject>Travel industry</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkl-L1DAUxYso7rr6DUQDguhDxyRNmvZJlsU_CwsLuvoa0vS2zZJpxiQdnG9vxu0sU9kHyUNC8rvnJicny14SvCKFIB9u3eRHZVcbN8IK06riBD_KTkld0LykuHh8tD7JnoVwizEvqrJ8mp0UgpWCMnaahZsBkEo6u2BCWrQoODtF40bUOY_MGMFrE3coerUFixoY1NY4H1A7eTP2aHDWtGoXEopi0tq4EHPYmBbWRiPwCjUqQIuSYGN61KqonmdPOmUDvJjns-zH5083F1_zq-svlxfnV7nmooy5Vlp3FSaE4QoIxZwDYy2w9Axes7rTtNUlBqEpJ1QT1mjMiEibFa5J06jiLHt9p7uxLsjZryBpVdSC4rLGifg4E1OzhlbDmJ5p5cabtfI76ZSRy5PRDLJ3W0lwwZOZdVJ4Nyt492uCEOXaBA3WqhHctG_GMGUlL0RC3_yDPnylmeqVBWnGzqXGei8qzwUXguOKsEStHqDS-Gt7SkRn0v6i4P2iIDERfsdeTSHIy-_f_p-9_rlk3x6xAygbh0OCwhJkd6D2LgQP3b3LBMt9oA9uyH2g5RzoVPbq-Ifuiw4JLv4Ag_zxLg</recordid><startdate>20230719</startdate><enddate>20230719</enddate><creator>Zhang, Xike</creator><creator>Gao, Jiaqi</creator><general>Public Library of Science</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>COVID</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0009-0005-6646-390X</orcidid></search><sort><creationdate>20230719</creationdate><title>The analysis and solution for intercity travel behaviors during holidays in the post-epidemic era based on big data</title><author>Zhang, Xike ; Gao, Jiaqi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c576t-caccf8011408e12055e44de40055949fc2dc60e7c2512c14bc04172dc8091bba3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Analysis</topic><topic>Behavior</topic><topic>Big Data</topic><topic>Biology and Life Sciences</topic><topic>Cities</topic><topic>Computer and Information Sciences</topic><topic>COVID-19</topic><topic>COVID-19 - epidemiology</topic><topic>COVID-19 - prevention & control</topic><topic>Decision making</topic><topic>Earth Sciences</topic><topic>Engineering and Technology</topic><topic>Epidemics</topic><topic>Epidemics - prevention & control</topic><topic>Game theory</topic><topic>Health aspects</topic><topic>Holidays</topic><topic>Holidays & special occasions</topic><topic>Humans</topic><topic>Interurban travel</topic><topic>Network analysis</topic><topic>Optimization</topic><topic>Physical Sciences</topic><topic>Public transportation</topic><topic>Service industries</topic><topic>Social Sciences</topic><topic>Tourism</topic><topic>Transportation industry</topic><topic>Transportation networks</topic><topic>Transportation services</topic><topic>Travel</topic><topic>Travel demand</topic><topic>Travel industry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Xike</creatorcontrib><creatorcontrib>Gao, Jiaqi</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Xike</au><au>Gao, Jiaqi</au><au>Cheng, Jing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The analysis and solution for intercity travel behaviors during holidays in the post-epidemic era based on big data</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-07-19</date><risdate>2023</risdate><volume>18</volume><issue>7</issue><spage>e0288510</spage><epage>e0288510</epage><pages>e0288510-e0288510</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The COVID-19 had a huge impact on the transportation industry. In the post-epidemic stage, intercity transportation will face great challenges as places are unsealed, tourism and other service industries begin to recover, and residents' travel demand gradually increases. An in-depth study of residents' intercity travel behavior during holidays in the post-epidemic era will help restore public trust in public transportation and improve the quality of public transportation services. Based on traditional research on ways of travelling, the study adopted the Complex Network Analysis Theory. The city clusters of Shandong Peninsula were taken as the research region. The research studied the impact of the differences in regional attributes of the cities in Shandong Peninsula on residents' intercity travel in the post-epidemic times. A dynamic evolution model of how residents choose to travel was built to simulate the changes to their ways of traveling in the post-epidemic era under two conditions, which are: traveling under the government's supervision of intercity travel and traveling under the government's optimization of intercity travel conditions. The conclusions drawn from the analyses of Complex Network Theory and Evolutionary Game Theory are as follows. First, in the holiday intercity travel in the post-epidemic times, the neighboring cities of Shandong Peninsula are closely connected, thus traveling between neighboring cities dominates intercity travel. Second, the travel network concentration of residents on long-term holidays is lower than that on short-term holidays, and the migration intensity of residents is higher than that on short-term holidays, while the willingness of residents' migration on short-term holidays is higher than that on long-term holidays. The willingness to migrate on holidays is generally lower than that before the epidemic. Third, in a normal intercity travel network, the travel between two cities with medium and long distances is mainly by public transport. However, the dominance of public transport will be affected under the impact of the epidemic. In short-distance travel between two cities, private transport is in an advantageous position, and under the impact of the epidemic, this advantage will become more significant. The government can improve the position of public transport in short-distance travel by making optimizations.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>37467244</pmid><doi>10.1371/journal.pone.0288510</doi><tpages>e0288510</tpages><orcidid>https://orcid.org/0009-0005-6646-390X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Behavior Big Data Biology and Life Sciences Cities Computer and Information Sciences COVID-19 COVID-19 - epidemiology COVID-19 - prevention & control Decision making Earth Sciences Engineering and Technology Epidemics Epidemics - prevention & control Game theory Health aspects Holidays Holidays & special occasions Humans Interurban travel Network analysis Optimization Physical Sciences Public transportation Service industries Social Sciences Tourism Transportation industry Transportation networks Transportation services Travel Travel demand Travel industry |
title | The analysis and solution for intercity travel behaviors during holidays in the post-epidemic era based on big data |
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