## 4 Discussion
### Handling Uncertainty in the PUTSS Algorithm  
The PUTSS algorithm addresses uncertainty by incorporating real-time data from multiple sources, such as Google Maps and Microsoft Azure Computer Vision, to adjust bus schedules dynamically. External factors like weather (rain or snow) are integrated through real-time traffic updates, allowing the system to make informed decisions about bus departure times and additional fleet requirements.  
While traditional statistical approaches to predicting congestion can be practical in stable conditions, they often fail to account for unpredictable external factors such as sudden weather changes, public events, or accidents. These factors can significantly impact congestion levels, making it difficult for classical methods to provide accurate real-time predictions. In contrast, AI-based algorithms, such as the one used in the PUTSS model, offer greater flexibility by incorporating real-time data and dynamically adjusting to changing conditions. This makes them more suited for handling complex and fluid urban transportation systems. Moreover, by combining historical data (\(SDC\)) and real-time computer vision (\(RTCVC\)), the proposed model addresses these challenges more effectively, ensuring that public transportation services remain responsive.  
Fuzzy logic is widely recognized as a robust framework for managing uncertainty in path planning, particularly in scenarios with ambiguous constraints like adverse weather conditions. The PUTSS algorithm uses a more deterministic approach. By relying on real-time data and statistical models, the algorithm directly responds to observable conditions rather than fuzzy memberships, which could be a more appropriate approach in situations with precise, quantifiable data.  
However, it is acknowledged that fuzzy-based approaches might offer additional flexibility in scenarios where uncertainty is high or data are more imprecise. Future research could compare the performance of the PUTSS algorithm against fuzzy logic systems, particularly in extreme weather conditions, to evaluate which framework better handles dynamic and uncertain environments in urban transport systems.  
By refining these aspects, the PUTSS algorithm could achieve higher operational use in practice, improving urban mobility and public transit management.  
Future work should incorporate additional data sources, such as weather forecasts, event schedules, and social media trends, to enhance the predictive accuracy of the \(SDCF\) and the responsiveness of the \(RTCVCF\). This multi-faceted data integration could help better anticipate and manage sudden surges in passenger numbers.  
Secondly, future research should investigate methods for dynamically adjusting the empirically set parameters, such as the passenger number margin. ML techniques could optimize these parameters based on real-time data and changing conditions.  
Although Google Maps Service provides real-time traffic and congestion data, it relies on another model's estimations rather than raw data directly collected from buses. This approach introduces potential inaccuracies, as Google Maps' data are derived from a combination of user data and various prediction models, which may not always reflect the exact traffic conditions relevant to a specific public transport system.  
In an ideal scenario, buses could be equipped with sensors to deliver raw, real-time data about traffic and passenger congestion levels. Such sensors would offer more accurate and reliable insights, allowing for precise scheduling and congestion management adjustments. However, the cost and complexity of implementing this infrastructure might not be feasible for many public transportation systems.