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Forecasting Visitor Demand from Open AI-Camera Counts and Weather: Lessons from Japan for Saudi Heritage Sites
arXiv:2611.01033v1 Announce Type: cross Abstract: Small heritage and nature sites need a few days' notice of busy days to plan staff, overflow parking and shuttles, but most have neither sensors nor forecasting expertise. We test what one artificial intelligence (AI) camera counter per site, a public holiday calendar and a free weather forecast can predict. Using 22 months of open AI-camera counts from three contrasting sites in Fukui, Japan (a train station, a coastal cliff and a mountain summit park), we measure how weather changes visits, forecast daily counts up to a week ahead with only the weather forecast available at the time, test busy-day warnings tied to a staffing decision, and ask how much history a new counter needs. The more exposed the site, the more weather matters: 10 mm of rain comes with 3% fewer detections at the station, 9% fewer at the cliff and 37% fewer at the summit park. With the forecasts actually issued, three-day-ahead weighted absolute percentage error (WAPE) at the summit park falls from 38.5% to 31.9% (Diebold-Mariano p<0.001), and the gain holds to five days; the sheltered station gains nothing. Three-day busy-day warnings with forecast weather reach, on past data and under the primary definitions, an area under the receiver operating characteristic curve (AUC) of 0.87-0.93, and weather lifts the overflow car park warning's precision from 0.60 to 0.82. For a new counter, blending a learned model with last week's same-hour counts cuts hourly error after only three days of training, pending a real installation. All inputs are off the shelf, and we outline how the approach could apply to heritage sites in Saudi Arabia.
Abstract:Small heritage and nature sites need a few days' notice of busy days to plan staff, overflow parking and shuttles, but most have neither sensors nor forecasting expertise. We test what one artificial intelligence (AI) camera counter per site, a public holiday calendar and a free weather forecast can predict. Using 22 months of open AI-camera counts from three contrasting sites in Fukui, Japan (a train station, a coastal cliff and a mountain summit park), we measure how weather changes visits, forecast daily counts up to a week ahead with only the weather forecast available at the time, test busy-day warnings tied to a staffing decision, and ask how much history a new counter needs. The more exposed the site, the more weather matters: 10 mm of rain comes with 3% fewer detections at the station, 9% fewer at the cliff and 37% fewer at the summit park. With the forecasts actually issued, three-day-ahead weighted absolute percentage error (WAPE) at the summit park falls from 38.5% to 31.9% (Diebold-Mariano p<0.001), and the gain holds to five days; the sheltered station gains nothing. Three-day busy-day warnings with forecast weather reach, on past data and under the primary definitions, an area under the receiver operating characteristic curve (AUC) of 0.87-0.93, and weather lifts the overflow car park warning's precision from 0.60 to 0.82. For a new counter, blending a learned model with last week's same-hour counts cuts hourly error after only three days of training, pending a real installation. All inputs are off the shelf, and we outline how the approach could apply to heritage sites in Saudi Arabia.
6 pages, 4 figures, 4 tables. Submitted to the 3rd International Conference on Advanced Innovations in Smart Cities (ICAISC 2027), Jeddah, Saudi Arabia. Code and data: this https URL
Computers and Society (cs.CY); Machine Learning (cs.LG)
arXiv:2611.01033 [cs.CY]
(or arXiv:2611.01033v1 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2611.01033 arXiv-issued DOI via DataCite (pending registration)
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