Temporal Demand Modeling is a quantitative analytical framework that belongs to the class of tourism demand forecasting methods, specifically distinguished by its focus on capturing and predicting the systematic temporal variations in tourist arrivals or consumption patterns across different time scales (e.g., seasonal, monthly, or daily cycles) using statistical or machine learning techniques.
57
Publications
2.5K
Citations
178
Authors
96
Institutions
Publications per year
2017–2026
28
Parents
Publications
57
1–5 of 57
Authors
178
- SS
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1–5 of 178
Institutions
96
Hong Kong Polytechnic University
Hong Kong, Hong Kong
3 h-index
3 publications
503 citations
Zhejiang University
Hangzhou, China
3 h-index
6 publications
139 citations
Tsinghua University
Beijing, China
2 h-index
8 publications
346 citations
Guangxi University
Nanning, China
2 h-index
2 publications
113 citations
University of Surrey
Guildford, United Kingdom
2 h-index
4 publications
291 citations
Academy of Mathematics and Systems Science
Beijing, China
2 h-index
3 publications
104 citations
Chinese Academy of Sciences
Beijing, China
2 h-index
3 publications
104 citations
University of Leeds
Leeds, United Kingdom
2 h-index
7 publications
138 citations
1–8 of 96
Venues
- Annals of Tourism Research
3 publications · 179 citations
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- Tourism Management
3 publications · 336 citations
3
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- Transportation Research Part C Emerging Technologies
2 publications · 67 citations
2
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- International Journal of Tourism Research
2 publications · 51 citations
2
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- Transportation Research Record Journal of the Transportation Research Board
TRR · 2 publications · 71 citations
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