The growing awareness of climate change worldwide has led the urban tourism market to focus on balancing tourist tailored experiences and CO_(2) emissions.Therefore,designing personalized tourist routes with environ‐...The growing awareness of climate change worldwide has led the urban tourism market to focus on balancing tourist tailored experiences and CO_(2) emissions.Therefore,designing personalized tourist routes with environ‐mental 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.展开更多
基金We acknowledge the financial support from the National Natural Science Foundation of China[Grant number:71701167]the Humani‐ties and Social Science Foundation of Chinese Ministry of Education[Grant number:17YJC630078].
文摘The growing awareness of climate change worldwide has led the urban tourism market to focus on balancing tourist tailored experiences and CO_(2) emissions.Therefore,designing personalized tourist routes with environ‐mental 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.
