Algorithm for scenario benefit route planning based on user’s requests
Most of the existing research for point of interest route planning only consider the static properties of POI,however,the congestion of the hot spots and users’ discontent may greatly reduce the travel quality.In order to increase the tourists’ satisfaction,the dynamic attributes of POI was consider...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2018-05-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018089/ |
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| author | Nan WANG Honglei ZHOU Jinbao LI Lingli LI |
| author_facet | Nan WANG Honglei ZHOU Jinbao LI Lingli LI |
| author_sort | Nan WANG |
| collection | DOAJ |
| description | Most of the existing research for point of interest route planning only consider the static properties of POI,however,the congestion of the hot spots and users’ discontent may greatly reduce the travel quality.In order to increase the tourists’ satisfaction,the dynamic attributes of POI was considered and a route planning algorithm based on user’s requests was proposed.Firstly,Markov-GM(1,1) forecasting algorithm was designed to predict the number of people in each scenic spot.Markov-GM(1,1) could make the average predication error 12.2% lower than the GM(1,1) algorithm by introducing the predication residual.And then,the forward refinement (FR) algorithm was designed which could avoid visiting the unnecessary place and satisfy user’s requests as well.The average solving time of forward refinement algorithm was 9.4% lower than TMT algorithm under the same amount of user’s requests.Finally,based on the factors such as spot popularity,KL divergence of time,visiting order and distance et al,the scenic route profit planning algorithm which could make the number of Rank 1-5 spots 34.8% higher than Time_Based algorithm and 47.3% higher than Rand_GA algorithm. |
| format | Article |
| id | doaj-art-006ed670b0e4486186c96714843e2646 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2018-05-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-006ed670b0e4486186c96714843e26462025-01-14T07:14:51ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2018-05-013918919859718477Algorithm for scenario benefit route planning based on user’s requestsNan WANGHonglei ZHOUJinbao LILingli LIMost of the existing research for point of interest route planning only consider the static properties of POI,however,the congestion of the hot spots and users’ discontent may greatly reduce the travel quality.In order to increase the tourists’ satisfaction,the dynamic attributes of POI was considered and a route planning algorithm based on user’s requests was proposed.Firstly,Markov-GM(1,1) forecasting algorithm was designed to predict the number of people in each scenic spot.Markov-GM(1,1) could make the average predication error 12.2% lower than the GM(1,1) algorithm by introducing the predication residual.And then,the forward refinement (FR) algorithm was designed which could avoid visiting the unnecessary place and satisfy user’s requests as well.The average solving time of forward refinement algorithm was 9.4% lower than TMT algorithm under the same amount of user’s requests.Finally,based on the factors such as spot popularity,KL divergence of time,visiting order and distance et al,the scenic route profit planning algorithm which could make the number of Rank 1-5 spots 34.8% higher than Time_Based algorithm and 47.3% higher than Rand_GA algorithm.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018089/point of interestscenario benefitKL divergenceroute planning |
| spellingShingle | Nan WANG Honglei ZHOU Jinbao LI Lingli LI Algorithm for scenario benefit route planning based on user’s requests Tongxin xuebao point of interest scenario benefit KL divergence route planning |
| title | Algorithm for scenario benefit route planning based on user’s requests |
| title_full | Algorithm for scenario benefit route planning based on user’s requests |
| title_fullStr | Algorithm for scenario benefit route planning based on user’s requests |
| title_full_unstemmed | Algorithm for scenario benefit route planning based on user’s requests |
| title_short | Algorithm for scenario benefit route planning based on user’s requests |
| title_sort | algorithm for scenario benefit route planning based on user s requests |
| topic | point of interest scenario benefit KL divergence route planning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018089/ |
| work_keys_str_mv | AT nanwang algorithmforscenariobenefitrouteplanningbasedonusersrequests AT hongleizhou algorithmforscenariobenefitrouteplanningbasedonusersrequests AT jinbaoli algorithmforscenariobenefitrouteplanningbasedonusersrequests AT linglili algorithmforscenariobenefitrouteplanningbasedonusersrequests |