Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information

The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urba...
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Autor*in:	Shijun Yu [verfasserIn] Siyuan Zhang [verfasserIn] Shejun Deng [verfasserIn] Tao Ji [verfasserIn] Peng Zhou [verfasserIn] Lang Peng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: Journal of Advanced Transportation - Hindawi-Wiley, 2017, (2021)
Übergeordnetes Werk:	year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1155/2021/1422381

Katalog-ID:	DOAJ019230826

Internformat


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520			\|a The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists.
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allfields	10.1155/2021/1422381 doi (DE-627)DOAJ019230826 (DE-599)DOAJ5635a0f8c1a547bc801978f134d8aafb DE-627 ger DE-627 rakwb eng TA1001-1280 HE1-9990 Shijun Yu verfasserin aut Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists. Transportation engineering Transportation and communications Siyuan Zhang verfasserin aut Shejun Deng verfasserin aut Tao Ji verfasserin aut Peng Zhou verfasserin aut Lang Peng verfasserin aut In Journal of Advanced Transportation Hindawi-Wiley, 2017 (2021) (DE-627)626054354 (DE-600)2553327-7 20423195 nnns year:2021 https://doi.org/10.1155/2021/1422381 kostenfrei https://doaj.org/article/5635a0f8c1a547bc801978f134d8aafb kostenfrei http://dx.doi.org/10.1155/2021/1422381 kostenfrei https://doaj.org/toc/0197-6729 Journal toc kostenfrei https://doaj.org/toc/2042-3195 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
spelling	10.1155/2021/1422381 doi (DE-627)DOAJ019230826 (DE-599)DOAJ5635a0f8c1a547bc801978f134d8aafb DE-627 ger DE-627 rakwb eng TA1001-1280 HE1-9990 Shijun Yu verfasserin aut Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists. Transportation engineering Transportation and communications Siyuan Zhang verfasserin aut Shejun Deng verfasserin aut Tao Ji verfasserin aut Peng Zhou verfasserin aut Lang Peng verfasserin aut In Journal of Advanced Transportation Hindawi-Wiley, 2017 (2021) (DE-627)626054354 (DE-600)2553327-7 20423195 nnns year:2021 https://doi.org/10.1155/2021/1422381 kostenfrei https://doaj.org/article/5635a0f8c1a547bc801978f134d8aafb kostenfrei http://dx.doi.org/10.1155/2021/1422381 kostenfrei https://doaj.org/toc/0197-6729 Journal toc kostenfrei https://doaj.org/toc/2042-3195 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
allfields_unstemmed	10.1155/2021/1422381 doi (DE-627)DOAJ019230826 (DE-599)DOAJ5635a0f8c1a547bc801978f134d8aafb DE-627 ger DE-627 rakwb eng TA1001-1280 HE1-9990 Shijun Yu verfasserin aut Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists. Transportation engineering Transportation and communications Siyuan Zhang verfasserin aut Shejun Deng verfasserin aut Tao Ji verfasserin aut Peng Zhou verfasserin aut Lang Peng verfasserin aut In Journal of Advanced Transportation Hindawi-Wiley, 2017 (2021) (DE-627)626054354 (DE-600)2553327-7 20423195 nnns year:2021 https://doi.org/10.1155/2021/1422381 kostenfrei https://doaj.org/article/5635a0f8c1a547bc801978f134d8aafb kostenfrei http://dx.doi.org/10.1155/2021/1422381 kostenfrei https://doaj.org/toc/0197-6729 Journal toc kostenfrei https://doaj.org/toc/2042-3195 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
allfieldsGer	10.1155/2021/1422381 doi (DE-627)DOAJ019230826 (DE-599)DOAJ5635a0f8c1a547bc801978f134d8aafb DE-627 ger DE-627 rakwb eng TA1001-1280 HE1-9990 Shijun Yu verfasserin aut Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists. Transportation engineering Transportation and communications Siyuan Zhang verfasserin aut Shejun Deng verfasserin aut Tao Ji verfasserin aut Peng Zhou verfasserin aut Lang Peng verfasserin aut In Journal of Advanced Transportation Hindawi-Wiley, 2017 (2021) (DE-627)626054354 (DE-600)2553327-7 20423195 nnns year:2021 https://doi.org/10.1155/2021/1422381 kostenfrei https://doaj.org/article/5635a0f8c1a547bc801978f134d8aafb kostenfrei http://dx.doi.org/10.1155/2021/1422381 kostenfrei https://doaj.org/toc/0197-6729 Journal toc kostenfrei https://doaj.org/toc/2042-3195 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
allfieldsSound	10.1155/2021/1422381 doi (DE-627)DOAJ019230826 (DE-599)DOAJ5635a0f8c1a547bc801978f134d8aafb DE-627 ger DE-627 rakwb eng TA1001-1280 HE1-9990 Shijun Yu verfasserin aut Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists. Transportation engineering Transportation and communications Siyuan Zhang verfasserin aut Shejun Deng verfasserin aut Tao Ji verfasserin aut Peng Zhou verfasserin aut Lang Peng verfasserin aut In Journal of Advanced Transportation Hindawi-Wiley, 2017 (2021) (DE-627)626054354 (DE-600)2553327-7 20423195 nnns year:2021 https://doi.org/10.1155/2021/1422381 kostenfrei https://doaj.org/article/5635a0f8c1a547bc801978f134d8aafb kostenfrei http://dx.doi.org/10.1155/2021/1422381 kostenfrei https://doaj.org/toc/0197-6729 Journal toc kostenfrei https://doaj.org/toc/2042-3195 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
language	English
source	In Journal of Advanced Transportation (2021) year:2021
sourceStr	In Journal of Advanced Transportation (2021) year:2021
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topic_facet	Transportation engineering Transportation and communications
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container_title	Journal of Advanced Transportation
authorswithroles_txt_mv	Shijun Yu @@aut@@ Siyuan Zhang @@aut@@ Shejun Deng @@aut@@ Tao Ji @@aut@@ Peng Zhou @@aut@@ Lang Peng @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	626054354
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language_de	englisch
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author	Shijun Yu
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topic_title	TA1001-1280 HE1-9990 Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information
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title	Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information
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title_sort	modeling tourists’ departure time considering the influence of multisource traffic information
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title_auth	Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information
abstract	The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists.
abstractGer	The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists.
abstract_unstemmed	The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists.
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title_short	Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information
url	https://doi.org/10.1155/2021/1422381 https://doaj.org/article/5635a0f8c1a547bc801978f134d8aafb http://dx.doi.org/10.1155/2021/1422381 https://doaj.org/toc/0197-6729 https://doaj.org/toc/2042-3195
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Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information

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