Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model
Aiming at the problems in current research into low-carbon and water scenery tourism, this paper brings forward a low-carbon tour route algorithm of urban scenic water spots based on an improved Divisive Analysis clustering model. Based on the ecological attributes of scenic water spots, the cluster...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/9/1361 |
| _version_ | 1797502541072695296 |
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| author | Xiao Zhou De Zhang Jiangpeng Tian Mingzhan Su |
| author_facet | Xiao Zhou De Zhang Jiangpeng Tian Mingzhan Su |
| author_sort | Xiao Zhou |
| collection | DOAJ |
| description | Aiming at the problems in current research into low-carbon and water scenery tourism, this paper brings forward a low-carbon tour route algorithm of urban scenic water spots based on an improved Divisive Analysis clustering model. Based on the ecological attributes of scenic water spots, the clustering model is set up to create scenic spot clusters. Via the clusters, the low-carbon tour route algorithm of urban scenic water spots based on the optimal energy conservation and emission reduction mode is proposed, and it provides the optimal scenic water spots and low-carbon tour routes for tourists. The model can thus realize the optimization of vehicle exhaust emission in urban travel and reduce exhaust emission damage to urban water bodies and natural environments. In order to verify the advantages of the proposed algorithm, this paper performs an experiment to compare the proposed algorithm with the frequently used route planning methods by tourists. The experimental results show that the proposed algorithm has great advantages in energy conservation, emission reduction and low-carbon travel and can reduce the exhaust emission and the damage to the urban water bodies and the natural environment, realizing low-carbon tourism. The main findings and contributions of the proposed work are as follows. First, an improved clustering algorithm is set up, and the urban scenic water spots are clustered according to attribute data, which could optimize the scenic spot recommendation spatial model. Second, combining with the specific characteristics of scenic water spots, the scenic spot mining and matching algorithm is set up to satisfy tourists’ needs. Third, a method that could reduce emission exhaust by optimizing self-driving tour routes is proposed, which could control and reduce the damage to urban environments and protect water ecosystems. The proposed algorithm could be used as the embedded algorithm of tour recommendation systems or the reference algorithm for planning urban tourism transportation. Especially in peak tourism season, it could be used as an effective method for tourism and traffic management departments to direct traffic flow. |
| first_indexed | 2024-03-10T03:34:34Z |
| format | Article |
| id | doaj.art-c37a471d607a4c9795eaf235ae45636c |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T03:34:34Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-c37a471d607a4c9795eaf235ae45636c2023-11-23T09:34:40ZengMDPI AGWater2073-44412022-04-01149136110.3390/w14091361Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering ModelXiao Zhou0De Zhang1Jiangpeng Tian2Mingzhan Su3Post-Doctoral Innovation Practice Base of Sichuan Province, Leshan Vocational and Technical College, Leshan 614000, ChinaState Key Laboratory of Geo-Information Engineering, Xi’an 710054, ChinaInstitute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, ChinaInstitute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, ChinaAiming at the problems in current research into low-carbon and water scenery tourism, this paper brings forward a low-carbon tour route algorithm of urban scenic water spots based on an improved Divisive Analysis clustering model. Based on the ecological attributes of scenic water spots, the clustering model is set up to create scenic spot clusters. Via the clusters, the low-carbon tour route algorithm of urban scenic water spots based on the optimal energy conservation and emission reduction mode is proposed, and it provides the optimal scenic water spots and low-carbon tour routes for tourists. The model can thus realize the optimization of vehicle exhaust emission in urban travel and reduce exhaust emission damage to urban water bodies and natural environments. In order to verify the advantages of the proposed algorithm, this paper performs an experiment to compare the proposed algorithm with the frequently used route planning methods by tourists. The experimental results show that the proposed algorithm has great advantages in energy conservation, emission reduction and low-carbon travel and can reduce the exhaust emission and the damage to the urban water bodies and the natural environment, realizing low-carbon tourism. The main findings and contributions of the proposed work are as follows. First, an improved clustering algorithm is set up, and the urban scenic water spots are clustered according to attribute data, which could optimize the scenic spot recommendation spatial model. Second, combining with the specific characteristics of scenic water spots, the scenic spot mining and matching algorithm is set up to satisfy tourists’ needs. Third, a method that could reduce emission exhaust by optimizing self-driving tour routes is proposed, which could control and reduce the damage to urban environments and protect water ecosystems. The proposed algorithm could be used as the embedded algorithm of tour recommendation systems or the reference algorithm for planning urban tourism transportation. Especially in peak tourism season, it could be used as an effective method for tourism and traffic management departments to direct traffic flow.https://www.mdpi.com/2073-4441/14/9/1361urban water bodieswater scenery tourismlow-carbon tour routeDIANA algorithmECER mode |
| spellingShingle | Xiao Zhou De Zhang Jiangpeng Tian Mingzhan Su Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model Water urban water bodies water scenery tourism low-carbon tour route DIANA algorithm ECER mode |
| title | Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model |
| title_full | Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model |
| title_fullStr | Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model |
| title_full_unstemmed | Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model |
| title_short | Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model |
| title_sort | low carbon tour route algorithm of urban scenic water spots based on an improved diana clustering model |
| topic | urban water bodies water scenery tourism low-carbon tour route DIANA algorithm ECER mode |
| url | https://www.mdpi.com/2073-4441/14/9/1361 |
| work_keys_str_mv | AT xiaozhou lowcarbontourroutealgorithmofurbanscenicwaterspotsbasedonanimproveddianaclusteringmodel AT dezhang lowcarbontourroutealgorithmofurbanscenicwaterspotsbasedonanimproveddianaclusteringmodel AT jiangpengtian lowcarbontourroutealgorithmofurbanscenicwaterspotsbasedonanimproveddianaclusteringmodel AT mingzhansu lowcarbontourroutealgorithmofurbanscenicwaterspotsbasedonanimproveddianaclusteringmodel |