Scenic route planning for tourists
Abstract Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist t...
Ausführliche Beschreibung
Autor*in: |
Gavalas, Damianos [verfasserIn] Kasapakis, Vlasios [verfasserIn] Konstantopoulos, Charalampos [verfasserIn] Pantziou, Grammati [verfasserIn] Vathis, Nikolaos [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Personal and ubiquitous computing - London : Springer, 1997, 21(2016), 1 vom: 06. Okt., Seite 137-155 |
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Übergeordnetes Werk: |
volume:21 ; year:2016 ; number:1 ; day:06 ; month:10 ; pages:137-155 |
Links: |
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DOI / URN: |
10.1007/s00779-016-0971-3 |
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Katalog-ID: |
SPR00778600X |
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520 | |a Abstract Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist tourists in making feasible plans and visiting the most interesting POIs within their available time. However, existing tourist tour planners only regard available attractions as sites lacking physical dimensions (i.e., POIs are treated as ‘points’). This restricts the modeling of POIs as attractions that may be entered/exited from a certain location (e.g., the main entrance). Although this is adequate for scheduling visits at museums, galleries, small squares or parks with single entry points, it fails to capture practical properties of typical tourist visiting styles in urban destinations. Tourists commonly appreciate strolling through pedestrian zones, market areas or urban areas of architectural, cultural and scenic value rather than only visiting sites of restricted access or taking the fastest route to move among city landmarks. Herein, we introduce Scenic Athens, a context-aware mobile city guide for Athens (Greece) which provides personalized tour planning services to tourists. Far beyond than just providing navigational aid, Scenic Athens derives near-optimal sequencing of POIs along recommended tours, taking into account a multitude of travel restrictions and POI properties, so as to best utilize time available for sightseeing. Unlike similar tools, our application incorporates scenic (walking) routes (in addition to point POIs), thereby supporting more experiential exploration of tourist destinations. This broader perception of tourist attractions substantially increases the complexity of the entailed optimization problem’s modeling. A user evaluation study validated the recommendation value, usability and perceived utility of the proposed application. | ||
650 | 4 | |a Tourist trip design problem |7 (dpeaa)DE-He213 | |
650 | 4 | |a Scenic routes |7 (dpeaa)DE-He213 | |
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700 | 1 | |a Kasapakis, Vlasios |e verfasserin |4 aut | |
700 | 1 | |a Konstantopoulos, Charalampos |e verfasserin |4 aut | |
700 | 1 | |a Pantziou, Grammati |e verfasserin |4 aut | |
700 | 1 | |a Vathis, Nikolaos |e verfasserin |4 aut | |
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10.1007/s00779-016-0971-3 doi (DE-627)SPR00778600X (SPR)s00779-016-0971-3-e DE-627 ger DE-627 rakwb eng 004 620 ASE 004 ASE 53.76 bkl 54.26 bkl Gavalas, Damianos verfasserin aut Scenic route planning for tourists 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist tourists in making feasible plans and visiting the most interesting POIs within their available time. However, existing tourist tour planners only regard available attractions as sites lacking physical dimensions (i.e., POIs are treated as ‘points’). This restricts the modeling of POIs as attractions that may be entered/exited from a certain location (e.g., the main entrance). Although this is adequate for scheduling visits at museums, galleries, small squares or parks with single entry points, it fails to capture practical properties of typical tourist visiting styles in urban destinations. Tourists commonly appreciate strolling through pedestrian zones, market areas or urban areas of architectural, cultural and scenic value rather than only visiting sites of restricted access or taking the fastest route to move among city landmarks. Herein, we introduce Scenic Athens, a context-aware mobile city guide for Athens (Greece) which provides personalized tour planning services to tourists. Far beyond than just providing navigational aid, Scenic Athens derives near-optimal sequencing of POIs along recommended tours, taking into account a multitude of travel restrictions and POI properties, so as to best utilize time available for sightseeing. Unlike similar tools, our application incorporates scenic (walking) routes (in addition to point POIs), thereby supporting more experiential exploration of tourist destinations. This broader perception of tourist attractions substantially increases the complexity of the entailed optimization problem’s modeling. A user evaluation study validated the recommendation value, usability and perceived utility of the proposed application. Tourist trip design problem (dpeaa)DE-He213 Scenic routes (dpeaa)DE-He213 Orienteering problem (dpeaa)DE-He213 Time window (dpeaa)DE-He213 Context awareness (dpeaa)DE-He213 Web service (dpeaa)DE-He213 Mobile application (dpeaa)DE-He213 Android (dpeaa)DE-He213 Performance test (dpeaa)DE-He213 User evaluation (dpeaa)DE-He213 Kasapakis, Vlasios verfasserin aut Konstantopoulos, Charalampos verfasserin aut Pantziou, Grammati verfasserin aut Vathis, Nikolaos verfasserin aut Enthalten in Personal and ubiquitous computing London : Springer, 1997 21(2016), 1 vom: 06. Okt., Seite 137-155 (DE-627)271596686 (DE-600)1480656-3 1617-4917 nnns volume:21 year:2016 number:1 day:06 month:10 pages:137-155 https://dx.doi.org/10.1007/s00779-016-0971-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 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_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2919 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.76 ASE 54.26 ASE AR 21 2016 1 06 10 137-155 |
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10.1007/s00779-016-0971-3 doi (DE-627)SPR00778600X (SPR)s00779-016-0971-3-e DE-627 ger DE-627 rakwb eng 004 620 ASE 004 ASE 53.76 bkl 54.26 bkl Gavalas, Damianos verfasserin aut Scenic route planning for tourists 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist tourists in making feasible plans and visiting the most interesting POIs within their available time. However, existing tourist tour planners only regard available attractions as sites lacking physical dimensions (i.e., POIs are treated as ‘points’). This restricts the modeling of POIs as attractions that may be entered/exited from a certain location (e.g., the main entrance). Although this is adequate for scheduling visits at museums, galleries, small squares or parks with single entry points, it fails to capture practical properties of typical tourist visiting styles in urban destinations. Tourists commonly appreciate strolling through pedestrian zones, market areas or urban areas of architectural, cultural and scenic value rather than only visiting sites of restricted access or taking the fastest route to move among city landmarks. Herein, we introduce Scenic Athens, a context-aware mobile city guide for Athens (Greece) which provides personalized tour planning services to tourists. Far beyond than just providing navigational aid, Scenic Athens derives near-optimal sequencing of POIs along recommended tours, taking into account a multitude of travel restrictions and POI properties, so as to best utilize time available for sightseeing. Unlike similar tools, our application incorporates scenic (walking) routes (in addition to point POIs), thereby supporting more experiential exploration of tourist destinations. This broader perception of tourist attractions substantially increases the complexity of the entailed optimization problem’s modeling. A user evaluation study validated the recommendation value, usability and perceived utility of the proposed application. Tourist trip design problem (dpeaa)DE-He213 Scenic routes (dpeaa)DE-He213 Orienteering problem (dpeaa)DE-He213 Time window (dpeaa)DE-He213 Context awareness (dpeaa)DE-He213 Web service (dpeaa)DE-He213 Mobile application (dpeaa)DE-He213 Android (dpeaa)DE-He213 Performance test (dpeaa)DE-He213 User evaluation (dpeaa)DE-He213 Kasapakis, Vlasios verfasserin aut Konstantopoulos, Charalampos verfasserin aut Pantziou, Grammati verfasserin aut Vathis, Nikolaos verfasserin aut Enthalten in Personal and ubiquitous computing London : Springer, 1997 21(2016), 1 vom: 06. Okt., Seite 137-155 (DE-627)271596686 (DE-600)1480656-3 1617-4917 nnns volume:21 year:2016 number:1 day:06 month:10 pages:137-155 https://dx.doi.org/10.1007/s00779-016-0971-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 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_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2919 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.76 ASE 54.26 ASE AR 21 2016 1 06 10 137-155 |
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10.1007/s00779-016-0971-3 doi (DE-627)SPR00778600X (SPR)s00779-016-0971-3-e DE-627 ger DE-627 rakwb eng 004 620 ASE 004 ASE 53.76 bkl 54.26 bkl Gavalas, Damianos verfasserin aut Scenic route planning for tourists 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist tourists in making feasible plans and visiting the most interesting POIs within their available time. However, existing tourist tour planners only regard available attractions as sites lacking physical dimensions (i.e., POIs are treated as ‘points’). This restricts the modeling of POIs as attractions that may be entered/exited from a certain location (e.g., the main entrance). Although this is adequate for scheduling visits at museums, galleries, small squares or parks with single entry points, it fails to capture practical properties of typical tourist visiting styles in urban destinations. Tourists commonly appreciate strolling through pedestrian zones, market areas or urban areas of architectural, cultural and scenic value rather than only visiting sites of restricted access or taking the fastest route to move among city landmarks. Herein, we introduce Scenic Athens, a context-aware mobile city guide for Athens (Greece) which provides personalized tour planning services to tourists. Far beyond than just providing navigational aid, Scenic Athens derives near-optimal sequencing of POIs along recommended tours, taking into account a multitude of travel restrictions and POI properties, so as to best utilize time available for sightseeing. Unlike similar tools, our application incorporates scenic (walking) routes (in addition to point POIs), thereby supporting more experiential exploration of tourist destinations. This broader perception of tourist attractions substantially increases the complexity of the entailed optimization problem’s modeling. A user evaluation study validated the recommendation value, usability and perceived utility of the proposed application. Tourist trip design problem (dpeaa)DE-He213 Scenic routes (dpeaa)DE-He213 Orienteering problem (dpeaa)DE-He213 Time window (dpeaa)DE-He213 Context awareness (dpeaa)DE-He213 Web service (dpeaa)DE-He213 Mobile application (dpeaa)DE-He213 Android (dpeaa)DE-He213 Performance test (dpeaa)DE-He213 User evaluation (dpeaa)DE-He213 Kasapakis, Vlasios verfasserin aut Konstantopoulos, Charalampos verfasserin aut Pantziou, Grammati verfasserin aut Vathis, Nikolaos verfasserin aut Enthalten in Personal and ubiquitous computing London : Springer, 1997 21(2016), 1 vom: 06. Okt., Seite 137-155 (DE-627)271596686 (DE-600)1480656-3 1617-4917 nnns volume:21 year:2016 number:1 day:06 month:10 pages:137-155 https://dx.doi.org/10.1007/s00779-016-0971-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 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_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2919 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.76 ASE 54.26 ASE AR 21 2016 1 06 10 137-155 |
allfieldsGer |
10.1007/s00779-016-0971-3 doi (DE-627)SPR00778600X (SPR)s00779-016-0971-3-e DE-627 ger DE-627 rakwb eng 004 620 ASE 004 ASE 53.76 bkl 54.26 bkl Gavalas, Damianos verfasserin aut Scenic route planning for tourists 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist tourists in making feasible plans and visiting the most interesting POIs within their available time. However, existing tourist tour planners only regard available attractions as sites lacking physical dimensions (i.e., POIs are treated as ‘points’). This restricts the modeling of POIs as attractions that may be entered/exited from a certain location (e.g., the main entrance). Although this is adequate for scheduling visits at museums, galleries, small squares or parks with single entry points, it fails to capture practical properties of typical tourist visiting styles in urban destinations. Tourists commonly appreciate strolling through pedestrian zones, market areas or urban areas of architectural, cultural and scenic value rather than only visiting sites of restricted access or taking the fastest route to move among city landmarks. Herein, we introduce Scenic Athens, a context-aware mobile city guide for Athens (Greece) which provides personalized tour planning services to tourists. Far beyond than just providing navigational aid, Scenic Athens derives near-optimal sequencing of POIs along recommended tours, taking into account a multitude of travel restrictions and POI properties, so as to best utilize time available for sightseeing. Unlike similar tools, our application incorporates scenic (walking) routes (in addition to point POIs), thereby supporting more experiential exploration of tourist destinations. This broader perception of tourist attractions substantially increases the complexity of the entailed optimization problem’s modeling. A user evaluation study validated the recommendation value, usability and perceived utility of the proposed application. Tourist trip design problem (dpeaa)DE-He213 Scenic routes (dpeaa)DE-He213 Orienteering problem (dpeaa)DE-He213 Time window (dpeaa)DE-He213 Context awareness (dpeaa)DE-He213 Web service (dpeaa)DE-He213 Mobile application (dpeaa)DE-He213 Android (dpeaa)DE-He213 Performance test (dpeaa)DE-He213 User evaluation (dpeaa)DE-He213 Kasapakis, Vlasios verfasserin aut Konstantopoulos, Charalampos verfasserin aut Pantziou, Grammati verfasserin aut Vathis, Nikolaos verfasserin aut Enthalten in Personal and ubiquitous computing London : Springer, 1997 21(2016), 1 vom: 06. Okt., Seite 137-155 (DE-627)271596686 (DE-600)1480656-3 1617-4917 nnns volume:21 year:2016 number:1 day:06 month:10 pages:137-155 https://dx.doi.org/10.1007/s00779-016-0971-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 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_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2919 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.76 ASE 54.26 ASE AR 21 2016 1 06 10 137-155 |
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10.1007/s00779-016-0971-3 doi (DE-627)SPR00778600X (SPR)s00779-016-0971-3-e DE-627 ger DE-627 rakwb eng 004 620 ASE 004 ASE 53.76 bkl 54.26 bkl Gavalas, Damianos verfasserin aut Scenic route planning for tourists 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist tourists in making feasible plans and visiting the most interesting POIs within their available time. However, existing tourist tour planners only regard available attractions as sites lacking physical dimensions (i.e., POIs are treated as ‘points’). This restricts the modeling of POIs as attractions that may be entered/exited from a certain location (e.g., the main entrance). Although this is adequate for scheduling visits at museums, galleries, small squares or parks with single entry points, it fails to capture practical properties of typical tourist visiting styles in urban destinations. Tourists commonly appreciate strolling through pedestrian zones, market areas or urban areas of architectural, cultural and scenic value rather than only visiting sites of restricted access or taking the fastest route to move among city landmarks. Herein, we introduce Scenic Athens, a context-aware mobile city guide for Athens (Greece) which provides personalized tour planning services to tourists. Far beyond than just providing navigational aid, Scenic Athens derives near-optimal sequencing of POIs along recommended tours, taking into account a multitude of travel restrictions and POI properties, so as to best utilize time available for sightseeing. Unlike similar tools, our application incorporates scenic (walking) routes (in addition to point POIs), thereby supporting more experiential exploration of tourist destinations. This broader perception of tourist attractions substantially increases the complexity of the entailed optimization problem’s modeling. A user evaluation study validated the recommendation value, usability and perceived utility of the proposed application. Tourist trip design problem (dpeaa)DE-He213 Scenic routes (dpeaa)DE-He213 Orienteering problem (dpeaa)DE-He213 Time window (dpeaa)DE-He213 Context awareness (dpeaa)DE-He213 Web service (dpeaa)DE-He213 Mobile application (dpeaa)DE-He213 Android (dpeaa)DE-He213 Performance test (dpeaa)DE-He213 User evaluation (dpeaa)DE-He213 Kasapakis, Vlasios verfasserin aut Konstantopoulos, Charalampos verfasserin aut Pantziou, Grammati verfasserin aut Vathis, Nikolaos verfasserin aut Enthalten in Personal and ubiquitous computing London : Springer, 1997 21(2016), 1 vom: 06. Okt., Seite 137-155 (DE-627)271596686 (DE-600)1480656-3 1617-4917 nnns volume:21 year:2016 number:1 day:06 month:10 pages:137-155 https://dx.doi.org/10.1007/s00779-016-0971-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 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_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2919 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.76 ASE 54.26 ASE AR 21 2016 1 06 10 137-155 |
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Enthalten in Personal and ubiquitous computing 21(2016), 1 vom: 06. Okt., Seite 137-155 volume:21 year:2016 number:1 day:06 month:10 pages:137-155 |
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Enthalten in Personal and ubiquitous computing 21(2016), 1 vom: 06. Okt., Seite 137-155 volume:21 year:2016 number:1 day:06 month:10 pages:137-155 |
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Tourist trip design problem Scenic routes Orienteering problem Time window Context awareness Web service Mobile application Android Performance test User evaluation |
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Personal and ubiquitous computing |
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Gavalas, Damianos @@aut@@ Kasapakis, Vlasios @@aut@@ Konstantopoulos, Charalampos @@aut@@ Pantziou, Grammati @@aut@@ Vathis, Nikolaos @@aut@@ |
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2016-10-06T00:00:00Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR00778600X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110195810.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00779-016-0971-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR00778600X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00779-016-0971-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.26</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gavalas, Damianos</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Scenic route planning for tourists</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist tourists in making feasible plans and visiting the most interesting POIs within their available time. However, existing tourist tour planners only regard available attractions as sites lacking physical dimensions (i.e., POIs are treated as ‘points’). This restricts the modeling of POIs as attractions that may be entered/exited from a certain location (e.g., the main entrance). Although this is adequate for scheduling visits at museums, galleries, small squares or parks with single entry points, it fails to capture practical properties of typical tourist visiting styles in urban destinations. Tourists commonly appreciate strolling through pedestrian zones, market areas or urban areas of architectural, cultural and scenic value rather than only visiting sites of restricted access or taking the fastest route to move among city landmarks. Herein, we introduce Scenic Athens, a context-aware mobile city guide for Athens (Greece) which provides personalized tour planning services to tourists. Far beyond than just providing navigational aid, Scenic Athens derives near-optimal sequencing of POIs along recommended tours, taking into account a multitude of travel restrictions and POI properties, so as to best utilize time available for sightseeing. Unlike similar tools, our application incorporates scenic (walking) routes (in addition to point POIs), thereby supporting more experiential exploration of tourist destinations. This broader perception of tourist attractions substantially increases the complexity of the entailed optimization problem’s modeling. 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|
author |
Gavalas, Damianos |
spellingShingle |
Gavalas, Damianos ddc 004 bkl 53.76 bkl 54.26 misc Tourist trip design problem misc Scenic routes misc Orienteering problem misc Time window misc Context awareness misc Web service misc Mobile application misc Android misc Performance test misc User evaluation Scenic route planning for tourists |
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Gavalas, Damianos |
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004 - Data processing & computer science 620 - Engineering & allied operations |
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aut aut aut aut aut |
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1617-4917 |
topic_title |
004 620 ASE 004 ASE 53.76 bkl 54.26 bkl Scenic route planning for tourists Tourist trip design problem (dpeaa)DE-He213 Scenic routes (dpeaa)DE-He213 Orienteering problem (dpeaa)DE-He213 Time window (dpeaa)DE-He213 Context awareness (dpeaa)DE-He213 Web service (dpeaa)DE-He213 Mobile application (dpeaa)DE-He213 Android (dpeaa)DE-He213 Performance test (dpeaa)DE-He213 User evaluation (dpeaa)DE-He213 |
topic |
ddc 004 bkl 53.76 bkl 54.26 misc Tourist trip design problem misc Scenic routes misc Orienteering problem misc Time window misc Context awareness misc Web service misc Mobile application misc Android misc Performance test misc User evaluation |
topic_unstemmed |
ddc 004 bkl 53.76 bkl 54.26 misc Tourist trip design problem misc Scenic routes misc Orienteering problem misc Time window misc Context awareness misc Web service misc Mobile application misc Android misc Performance test misc User evaluation |
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ddc 004 bkl 53.76 bkl 54.26 misc Tourist trip design problem misc Scenic routes misc Orienteering problem misc Time window misc Context awareness misc Web service misc Mobile application misc Android misc Performance test misc User evaluation |
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Elektronische Aufsätze Aufsätze Elektronische Ressource |
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Personal and ubiquitous computing |
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Scenic route planning for tourists |
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Scenic route planning for tourists |
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Gavalas, Damianos Kasapakis, Vlasios Konstantopoulos, Charalampos Pantziou, Grammati Vathis, Nikolaos |
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scenic route planning for tourists |
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abstract |
Abstract Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist tourists in making feasible plans and visiting the most interesting POIs within their available time. However, existing tourist tour planners only regard available attractions as sites lacking physical dimensions (i.e., POIs are treated as ‘points’). This restricts the modeling of POIs as attractions that may be entered/exited from a certain location (e.g., the main entrance). Although this is adequate for scheduling visits at museums, galleries, small squares or parks with single entry points, it fails to capture practical properties of typical tourist visiting styles in urban destinations. Tourists commonly appreciate strolling through pedestrian zones, market areas or urban areas of architectural, cultural and scenic value rather than only visiting sites of restricted access or taking the fastest route to move among city landmarks. Herein, we introduce Scenic Athens, a context-aware mobile city guide for Athens (Greece) which provides personalized tour planning services to tourists. Far beyond than just providing navigational aid, Scenic Athens derives near-optimal sequencing of POIs along recommended tours, taking into account a multitude of travel restrictions and POI properties, so as to best utilize time available for sightseeing. Unlike similar tools, our application incorporates scenic (walking) routes (in addition to point POIs), thereby supporting more experiential exploration of tourist destinations. This broader perception of tourist attractions substantially increases the complexity of the entailed optimization problem’s modeling. A user evaluation study validated the recommendation value, usability and perceived utility of the proposed application. |
abstractGer |
Abstract Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist tourists in making feasible plans and visiting the most interesting POIs within their available time. However, existing tourist tour planners only regard available attractions as sites lacking physical dimensions (i.e., POIs are treated as ‘points’). This restricts the modeling of POIs as attractions that may be entered/exited from a certain location (e.g., the main entrance). Although this is adequate for scheduling visits at museums, galleries, small squares or parks with single entry points, it fails to capture practical properties of typical tourist visiting styles in urban destinations. Tourists commonly appreciate strolling through pedestrian zones, market areas or urban areas of architectural, cultural and scenic value rather than only visiting sites of restricted access or taking the fastest route to move among city landmarks. Herein, we introduce Scenic Athens, a context-aware mobile city guide for Athens (Greece) which provides personalized tour planning services to tourists. Far beyond than just providing navigational aid, Scenic Athens derives near-optimal sequencing of POIs along recommended tours, taking into account a multitude of travel restrictions and POI properties, so as to best utilize time available for sightseeing. Unlike similar tools, our application incorporates scenic (walking) routes (in addition to point POIs), thereby supporting more experiential exploration of tourist destinations. This broader perception of tourist attractions substantially increases the complexity of the entailed optimization problem’s modeling. A user evaluation study validated the recommendation value, usability and perceived utility of the proposed application. |
abstract_unstemmed |
Abstract Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of personalized tour recommendations to assist tourists in making feasible plans and visiting the most interesting POIs within their available time. However, existing tourist tour planners only regard available attractions as sites lacking physical dimensions (i.e., POIs are treated as ‘points’). This restricts the modeling of POIs as attractions that may be entered/exited from a certain location (e.g., the main entrance). Although this is adequate for scheduling visits at museums, galleries, small squares or parks with single entry points, it fails to capture practical properties of typical tourist visiting styles in urban destinations. Tourists commonly appreciate strolling through pedestrian zones, market areas or urban areas of architectural, cultural and scenic value rather than only visiting sites of restricted access or taking the fastest route to move among city landmarks. Herein, we introduce Scenic Athens, a context-aware mobile city guide for Athens (Greece) which provides personalized tour planning services to tourists. Far beyond than just providing navigational aid, Scenic Athens derives near-optimal sequencing of POIs along recommended tours, taking into account a multitude of travel restrictions and POI properties, so as to best utilize time available for sightseeing. Unlike similar tools, our application incorporates scenic (walking) routes (in addition to point POIs), thereby supporting more experiential exploration of tourist destinations. This broader perception of tourist attractions substantially increases the complexity of the entailed optimization problem’s modeling. A user evaluation study validated the recommendation value, usability and perceived utility of the proposed application. |
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|
score |
7.400057 |