# Controlling Traffic Congestion in a Residential Area via GLOSA Development
## 3 Results and Discussion  
Our analysis indicates the immediate impacts of simulations obtained using VISSIM and the developed algorithm (CTCRA). It covers rush hours in residential areas and their relationship with travel times for analysis and assessment.  
As a result, we gather sets of result data corresponding to each traffic volume group, travel time, traffic load, red light duration, and number of vehicles, and through data from the general traffic data (GTD), Figure 5 shows the vehicle's structure.  
Figure 4: Algorithm controlling traffic congestion in residential areas (CTCRA).  
Figure 2 shows the results of collecting data for two weeks through inspection of the target area (the section leading to the residential area) and calculating the average traffic density in the section for drivers who want to take the shortcut in addition to determining peak times during work hours.  
Figure 6 shows the simulation results for the variant (v0), which represents the actual case, while the other parts of (v) represent the 10% increase from (v0) for each variant (vehicles). Therefore, when referring to Figure 2 (the actual state), we notice that the important changes are with the increase in the percentage of the variant (v0) when it is 25%, as previously determined during peak hours.  
Figure 5: Vehicles’ structure.  
Figure 6: Average traffic volume (all variants).  
The results presented in Table 2 provide insight into the impact of the simulation using VISSIM and the implemented algorithm on travel times between points A and B. The table highlights the travel time before and after the simulation process. The significant difference in travel time between points A and B indicates that the simulation and algorithmic interventions have a tangible effect on the overall traffic flow.  
The integration of the algorithm responsible for calculating air quality levels in the residential area with a parallel navigation application is a noteworthy approach. Sending notifications to redirect drivers away from the residential area demonstrates a proactive strategy to minimize congestion, and improving air quality is an inevitable result of the reduction in the number of vehicles. The results presented in Table 2 suggest that imposing a speed limit on the residential area shortcut can have a discernible impact on traffic flow.  
The observation that exceeding 40 km/h has no effect on the trade-off indicates that congestion becomes the primary driver of travel time, overshadowing the influence of speed limits. The data indicate that imposing a 30 km/h speed limit can successfully remove traffic. This finding is crucial for balancing traffic reduction with acceptable travel times through the residential area. We recognize the importance of considering commuters' decisions when implementing speed limits. Lowering the speed restriction to an extent that would have little influence on commuters' decisions is acknowledged, ensuring that the shortcut remains a viable option despite the imposed limit. All of these results reflect a thoughtful approach to balancing the needs of residents and commuters. Minimizing additional annoyances for residents during less congested periods showcases a consideration for the diverse temporal patterns of traffic.