Personalized day tour design for urban tourists with consideration to CO2 emissions
The growing awareness of climate change worldwide has led the urban tourism market to focus on balancing tourist tailored experiences and CO2 emissions. Therefore, designing personalized tourist routes with environmental pollution consideration is preferable in this context. This study proposes an e...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2022-09-01
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| Series: | Chinese Journal of Population, Resources and Environment |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2325426222000675 |
| _version_ | 1811292863771705344 |
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| author | Lunwen Wu Tao Gu Zhiyu Chen Pan Zeng Zhixue Liao |
| author_facet | Lunwen Wu Tao Gu Zhiyu Chen Pan Zeng Zhixue Liao |
| author_sort | Lunwen Wu |
| collection | DOAJ |
| description | The growing awareness of climate change worldwide has led the urban tourism market to focus on balancing tourist tailored experiences and CO2 emissions. Therefore, designing personalized tourist routes with environmental pollution consideration is preferable in this context. This study proposes an evolution algorithm based on reinforcement learning (FSRL-HA) to design a personalized day tour route that simultaneously considers the utility of tourists and the carbon emission. We conducted a case study in Chengdu, Sichuan, China, to evaluate this algorithm's performance. The results indicate that the proposed algorithm outperforms selected baseline methods. Furthermore, the approach can provide more diverse route choices for different tourists, and an experiment was conducted to explore how tourist preferences affect tourist utilities. |
| first_indexed | 2024-04-13T04:52:12Z |
| format | Article |
| id | doaj.art-17e585c397944b1795a2dcbc31710a61 |
| institution | Directory Open Access Journal |
| issn | 2325-4262 |
| language | English |
| last_indexed | 2024-04-13T04:52:12Z |
| publishDate | 2022-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Chinese Journal of Population, Resources and Environment |
| spelling | doaj.art-17e585c397944b1795a2dcbc31710a612022-12-22T03:01:38ZengKeAi Communications Co., Ltd.Chinese Journal of Population, Resources and Environment2325-42622022-09-01203237244Personalized day tour design for urban tourists with consideration to CO2 emissionsLunwen Wu0Tao Gu1Zhiyu Chen2Pan Zeng3Zhixue Liao4School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, 611130, ChinaSchool of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, 611130, ChinaSchool of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, 611130, ChinaCorresponding author.; School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, 611130, ChinaSchool of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, 611130, ChinaThe growing awareness of climate change worldwide has led the urban tourism market to focus on balancing tourist tailored experiences and CO2 emissions. Therefore, designing personalized tourist routes with environmental pollution consideration is preferable in this context. This study proposes an evolution algorithm based on reinforcement learning (FSRL-HA) to design a personalized day tour route that simultaneously considers the utility of tourists and the carbon emission. We conducted a case study in Chengdu, Sichuan, China, to evaluate this algorithm's performance. The results indicate that the proposed algorithm outperforms selected baseline methods. Furthermore, the approach can provide more diverse route choices for different tourists, and an experiment was conducted to explore how tourist preferences affect tourist utilities.http://www.sciencedirect.com/science/article/pii/S2325426222000675Carbon emission Personalized day tour design Heuristic algorithm Reinforcement learning |
| spellingShingle | Lunwen Wu Tao Gu Zhiyu Chen Pan Zeng Zhixue Liao Personalized day tour design for urban tourists with consideration to CO2 emissions Chinese Journal of Population, Resources and Environment Carbon emission Personalized day tour design Heuristic algorithm Reinforcement learning |
| title | Personalized day tour design for urban tourists with consideration to CO2 emissions |
| title_full | Personalized day tour design for urban tourists with consideration to CO2 emissions |
| title_fullStr | Personalized day tour design for urban tourists with consideration to CO2 emissions |
| title_full_unstemmed | Personalized day tour design for urban tourists with consideration to CO2 emissions |
| title_short | Personalized day tour design for urban tourists with consideration to CO2 emissions |
| title_sort | personalized day tour design for urban tourists with consideration to co2 emissions |
| topic | Carbon emission Personalized day tour design Heuristic algorithm Reinforcement learning |
| url | http://www.sciencedirect.com/science/article/pii/S2325426222000675 |
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