## 3 Results  
This section assesses the effectiveness of the components utilized by the proposed algorithm through a detailed case study. Thus, the SDC and RTCVC integration is outlined, highlighting their roles in predicting and managing congestion levels on public urban transport routes. Subsequently, a case study that evaluates the accuracy of the Microsoft Azure Computer Vision service is presented, providing insights into its implications for the algorithm's overall efficiency. The SDC component monitors the congestion levels along the route and weather conditions, while the RTCVC component is responsible for the actual congestion at the bus stops. Considering that ordinary persons cannot install cameras in public places, including bus stations, for capturing images or videos because of privacy rights, data protection laws, surveillance regulations, security and safety concerns, and liability issues, a simulator developed in C# was used, which dynamically processed various images captured by the authors. If the PUTSS algorithm is implemented by a public urban transport company, it will have access to the traffic surveillance cameras that are already placed in any big city that faces traffic congestion or buses' onboard cameras. While the current case study is based on simulated data generated through a C# simulator, the following challenges must be addressed to ensure the successful implementation of PUTSS with real urban transportation data:* Integrating real-time GPS data from buses and traffic monitoring systems into the PUTSS algorithm may involve dealing with inconsistent data formats, communication delays, and network outages;
* In real-world scenarios, the precision of data collected through camera sensors for detecting passenger counts might vary because of environmental factors such as poor lighting, weather conditions, or obstructions;
* The system's performance needs to be tested under varying traffic conditions and with different fleet sizes to ensure that the PUTSS algorithm can handle peak urban traffic efficiently without increasing operational costs. To bridge the gap between simulation and real-world implementation, we must collaborate with a local urban transportation authority to implement PUTSS on a small subset of bus routes. This pilot project would collect real-time data from traffic sensors, buses, and bus stops. However, convincing local authorities to adopt and integrate the system could be challenging because of regulatory hurdles, budget constraints, and the complexity of retrofitting existing infrastructure. Additionally, ensuring the consistent availability and reliability of the necessary data sources could be difficult, as urban environments often present unpredictable variables that may hinder the seamless operation of the system. Based on historical and real-time data, the SDC is applied to predict congestion levels at stations or along a route. This component helps estimate congestion trends using past data (like average crowding at different times or days) combined with current conditions (e.g., real-time traffic data, weather conditions, or nearby events). The application of the SDC is as follows:  
1. Data Collection: The system collects historical data about crowd levels at each station. This includes the following: * Time of day and day of the week. * Special events (e.g., sports games, concerts). * Weather conditions, traffic disruptions, etc. 2. Classification: The algorithm classifies each station's congestion status based on historical patterns. Congestion statuses include the following six stages: light, moderate, heavier-than-usual, heavy, peak hours, and standstill. 3. Average Congestion Factor Calculation: The algorithm calculates an average congestion factor for the entire route or a section using relation (1). The SDC component relies on statistical data to predict the level of station congestion based on historical data and real-time information. The algorithm uses data such as time of day, day of the week, and special events.