## 4 Results
### Sensitivity Analysis and Environmental Factors
#### 4.3.2 Analysis of Environmental and Contextual Factor Sensitivities  
Our sensitivity analysis revealed an unexpected hierarchy in predictor importance. While primary operational variables showed high sensitivity coefficients (occupancy: 0.842, time: 0.731, gender ratio: 0.695), environmental and business context factors demonstrated notably lower influence (temperature: 0.376, business type: 0.328). This finding prompted deeper investigation through supplementary analysis.  
Figure 17: Impact of top parameters on fixture predictions.  
Figure 18: Heatmap of fixture requirements by occupancy and time.  
Several methodological and contextual factors explain these unexpected results. The buildings in our study were equipped with central air conditioning, maintaining stable indoor temperatures regardless of external conditions. This environmental control likely dampened the impact of temperature variations on restroom usage patterns. Singapore's relatively constant climate means seasonal temperature variations play a less significant role compared to regions with more extreme weather patterns.  
The low sensitivity coefficient for business type (0.328) warrants particular attention. We attribute this primarily to the homogeneous nature of our dataset, which predominantly included corporate office environments with similar work schedules and organizational cultures. This limited variation may have masked potential effects that would become more apparent in a more diverse dataset, including retail, industrial, or mixed-use facilities.  
Table 8 presents results from our supplementary study incorporating expanded contextual features:  
This extended analysis revealed modest improvements in model accuracy. While cultural events showed the strongest impact (4.44% improvement), the combined enhancement of all contextual features yielded a 9.57% improvement in prediction accuracy. However, these additions did not fundamentally alter the hierarchy of sensitivity coefficients.  
Comparison with previous research revealed notable divergences (Table 9). [PERSON] et al. [22] reported temperature sensitivity coefficients of 0.612 in non-climate-controlled buildings, significantly higher than our finding of 0.376. [PERSON] and [PERSON]'s foundational study [13], which established the first systematic methodology for analyzing office restroom usage through queuing theory and empirical observation, showed business-type coefficients of 0.573 in mixed-use facilities. Their comprehensive analysis of 25 UK office buildings from 1968 to 1975 remains a benchmark for understanding occupancy-based facility requirements, though their higher sensitivity coefficient (0.573 versus our 0.328) reflects the more rigid office work patterns of that era.  
These findings suggest opportunities for model enhancement through expanded feature integration. Future iterations could benefit from real-time cultural event weighting, adaptive business cycle modeling, and more sophisticated weather pattern analysis. Such enhancements could potentially improve model accuracy by 12-15% in diverse settings while maintaining computational efficiency.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Feature Category** & **Base MAPE** & **Enhanced MAPE** & **Improvement** \\ \hline Cultural Events & 7.21\% & 6.89\% & 4.44\% \\ Economic Indicators & 7.21\% & 7.02\% & 2.63\% \\ Weather Patterns & 7.21\% & 7.08\% & 1.80\% \\ Business Cycle Events & 7.21\% & 6.95\% & 3.61\% \\ Combined Enhancement & 7.21\% & 6.52\% & 9.57\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Impact of additional contextual features on model performance.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Factor** & **Our Study** & **Previous Studies (Mean)** & **Difference** \\ \hline Temperature & 0.376 & 0.612 & \(-\)0.236 \\ Business Type & 0.328 & 0.573 & \(-\)0.245 \\ Cultural Events & 0.412 & 0.389 & \(+\)0.023 \\ Economic Cycles & 0.367 & 0.401 & \(-\)0.034 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative analysis of sensitivity coefficients.