## 1 Introduction  
Sanitary facilities are more than an afterthought in building design; they significantly affect occupant comfort, health, and resource use [1]. Historically, building codes--like New Zealand's Building Code (Clause G1 Personal Hygiene) [2], the United Kingdom's Building Regulations--Approved Document G [3], and the International Plumbing Code in the United States [4]--have guided fixture requirements. However, these guidelines often rely on static assumptions that fail to capture flexible work arrangements or varying daily usage. Thus, many facilities end up overbuilt or underprepared for peak demands. Traditional approaches based on queueing theory [5, 6] further illustrate the complexity. While mathematically sound, they tend to overlook the wide range of human behaviors involved in restrooms. This highlights the need for new approaches that adapt to modern building use, evolving demographics, and shifting occupancy patterns. While existing approaches using queueing theory and prescriptive codes provide basic guidelines, they fail to account for dynamic usage patterns, cultural factors, and evolvingworkplace demographics. There is a critical need for a data-driven, adaptive approach that can capture these complex interactions and provide more accurate predictions for sanitary facility requirements. Current approaches to sanitary facility planning reveal significant gaps between theoretical models and real-world requirements. While existing methods based on queueing theory and prescriptive codes provide foundational guidelines, they exhibit several critical limitations. Traditional queueing models assume steady-state conditions and uniform arrival patterns, failing to capture the dynamic nature of modern workplace occupancy. Prescriptive codes, typically based on historical data and simplified occupancy assumptions, cannot adapt to emerging workplace trends such as flexible hours, remote work policies, and varying cultural practices. Furthermore, existing approaches demonstrate three key shortcomings:  
First, current methodologies lack the ability to account for temporal variations in facility usage. Peak demand periods, seasonal fluctuations, and event-driven usage spikes remain poorly addressed by static models. For instance, traditional calculations based on total occupancy fail to consider how usage patterns vary throughout the day or how special events might impact demand. Second, contemporary workplace demographics and cultural factors receive insufficient consideration in existing models. The increasing diversity of office populations, varying cultural norms regarding facility usage, and gender-specific requirements are not adequately captured by current approaches. This oversight leads to potential mismatches between provided facilities and actual user needs. Third, the emergence of flexible working arrangements and hybrid office models has introduced new complexities in occupancy patterns that traditional methods were not designed to handle. The rigid assumptions underlying current models become increasingly inappropriate as workplaces evolve toward more dynamic and adaptable environments. There is, therefore, a critical need for a data-driven, adaptive approach that can perform the following:  
* Capture and respond to dynamic usage patterns in real-time;
* Account for cultural factors and demographic variations;
* Adapt to evolving workplace trends and occupancy patterns;
* Provide more accurate predictions for fixture requirements based on actual usage data;
* Optimize resource allocation while maintaining user satisfaction. This research gap presents an opportunity to develop a more sophisticated, AI-driven approach that can integrate multiple data sources, learn from usage patterns, and provide more accurate and adaptable recommendations for sanitary facility planning. The proposed LSTM-based framework addresses these limitations by offering a dynamic, data-driven solution that can evolve with changing workplace needs while maintaining high standards of service quality.