Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity
Travel route preferences can strongly interact with the events that happened in networked traveling, and this coevolving phenomena are essential in providing theoretical foundations for travel route recommendation and predicting collective behaviour in social systems. While most literature puts the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6632956 |
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| _version_ | 1832546966471442432 |
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| author | Juanjuan Chen Liying Huang Chengliang Wang Nijia Zheng |
| author_facet | Juanjuan Chen Liying Huang Chengliang Wang Nijia Zheng |
| author_sort | Juanjuan Chen |
| collection | DOAJ |
| description | Travel route preferences can strongly interact with the events that happened in networked traveling, and this coevolving phenomena are essential in providing theoretical foundations for travel route recommendation and predicting collective behaviour in social systems. While most literature puts the focus on route recommendation of individual scenic spots instead of city travel, we propose a novel approach named City Travel Route Recommendation based on Sequential Events Similarity (CTRR-SES) by applying the coevolving spreading dynamics of the city tour networks and mine the travel spatiotemporal patterns in the networks. First, we present the Event Sequence Similarity Measurement Method based on modelling tourists’ travel sequences. The method can help measure similarities in various city travel routes, which combine different scenic types, time slots, and relative locations. Second, by applying the user preference learning method based on scenic type, we learn from the user’s city travel historical data and compute the personalized travel preference. Finally, we verify our algorithm by collecting data of 54 city travellers of their historical spatiotemporal routes in the ten most popular cities from Mafeng.com. CTRR-SES shows better performance in predicting the user’s new city travel sequence fitting the user’s individual preference. |
| format | Article |
| id | doaj-art-b19614bb388041f3b84dd4a80d02c5c3 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b19614bb388041f3b84dd4a80d02c5c32025-02-03T06:46:30ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66329566632956Discovering Travel Spatiotemporal Pattern Based on Sequential Events SimilarityJuanjuan Chen0Liying Huang1Chengliang Wang2Nijia Zheng3College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, ChinaCollege of Computer Science, Chongqing University, Chongqing 400044, ChinaCollege of Computer Science, Chongqing University, Chongqing 400044, ChinaCollege of Computer and Information Science, Chongqing Normal University, Chongqing 401331, ChinaTravel route preferences can strongly interact with the events that happened in networked traveling, and this coevolving phenomena are essential in providing theoretical foundations for travel route recommendation and predicting collective behaviour in social systems. While most literature puts the focus on route recommendation of individual scenic spots instead of city travel, we propose a novel approach named City Travel Route Recommendation based on Sequential Events Similarity (CTRR-SES) by applying the coevolving spreading dynamics of the city tour networks and mine the travel spatiotemporal patterns in the networks. First, we present the Event Sequence Similarity Measurement Method based on modelling tourists’ travel sequences. The method can help measure similarities in various city travel routes, which combine different scenic types, time slots, and relative locations. Second, by applying the user preference learning method based on scenic type, we learn from the user’s city travel historical data and compute the personalized travel preference. Finally, we verify our algorithm by collecting data of 54 city travellers of their historical spatiotemporal routes in the ten most popular cities from Mafeng.com. CTRR-SES shows better performance in predicting the user’s new city travel sequence fitting the user’s individual preference.http://dx.doi.org/10.1155/2020/6632956 |
| spellingShingle | Juanjuan Chen Liying Huang Chengliang Wang Nijia Zheng Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity Complexity |
| title | Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity |
| title_full | Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity |
| title_fullStr | Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity |
| title_full_unstemmed | Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity |
| title_short | Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity |
| title_sort | discovering travel spatiotemporal pattern based on sequential events similarity |
| url | http://dx.doi.org/10.1155/2020/6632956 |
| work_keys_str_mv | AT juanjuanchen discoveringtravelspatiotemporalpatternbasedonsequentialeventssimilarity AT liyinghuang discoveringtravelspatiotemporalpatternbasedonsequentialeventssimilarity AT chengliangwang discoveringtravelspatiotemporalpatternbasedonsequentialeventssimilarity AT nijiazheng discoveringtravelspatiotemporalpatternbasedonsequentialeventssimilarity |