Journal of Global Tourism Research | 2026
Authors: Matsunaga T.; Hori R.; Urata M.; Endo M.
DOI: 10.37020/jgtr.11.1_15
Journal: Journal of Global Tourism Research
Year: 2026
Publisher: Union Press
Document Type: Article
Open Access: All Open Access; Gold Open Access
Cited by: 0
The increasing availability of human mobility data—enabled by AI-based cameras or mobile GPS technologies—has made it easier to monitor pedestrian activity in tourist destinations. However, practical applications that directly support business operations remain limited. This study develops a pedestrian count prediction model as a concrete approach to data utilization that goes beyond simple descriptive monitoring. Using pedestrian count data collected by AI cameras installed in a shopping district in Takayama City, Gifu Prefecture, we constructed a prediction AI model that incorporates weather conditions and local event information. The resulting forecasts were provided to a stationery shop and a restaurant in the tourist area in the form of an hourly prediction calendar by a communication chat tool and email. Businesses reported that the availability of quantitative forecasts not only enabled more appropriate staffing, which had previously relied heavily on intuition, but reduced the burden of staff scheduling, contributing to reductions in unnecessary labor costs. These findings demonstrate that pedestrian count prediction and our user interface can effectively support operational efficiency and managerial decision-making for businesses in tourist destinations. © 2026 International Society for Tourism Research.
data utilization; decision-making support; machine learning; pedestrian count prediction; tourism area management