## 4 Conclusions  
In this research study, we investigated the efficacy of the convolutional neural network-based YOLOv7-SORT algorithm for real-time vehicle detection, counting, and tracking. We created a custom dataset named \"ISTraffic\", comprising 36,841 annotated instances across 1125 images, covering four vehicle classes: car, shuttle, bus, and motorcycle. Furthermore, we conducted a comparative analysis by evaluating our proposed strategy against two YOLOv7 model variants--YOLOv7x and YOLOv7-tiny--using both the \"ISTraffic\" dataset and the widely recognized COCO benchmark dataset. Additionally, we assessed model performance under varying environmental conditions using urban traffic surveillance videos. We used two datasets to evaluate vehicle emissions. The base model (YOLOv7-COCO) and the proposed model (YOLOv7-ISTraffic) were employed for comparison. This approach allowed us to assess the effectiveness of each model in estimating emissions accurately. YOLOv7x outperformed the other two variants of YOLOv7 with a [EMAIL_ADDRESS] of 0.87. Its higher precision (0.89) and recall (0.84) demonstrate improved localization and classification. Especially in comparison to the COCO benchmark dataset, the \"ISTraffic\" dataset benefits from high annotation quality, an adequate level of traffic image complexity, and a high number of annotations per image. These factors enhance the object detection capability of the YOLOv7x-SORT algorithm, resulting in a performance boost of 35%. However, it struggles to accurately identify motorcycles under high-speed conditions and has limited ability to monitor vehicles under variable and high traffic speeds. The algorithm's main objective is to predict future car locations using the Kalman filter, but performance declines when vehicle distance from the projected location falls below a threshold. For future work, it is recommended to explore the performance of different tracking algorithms that can handle complex scenarios and occlusions. Additionally, further investigation could focus on fine-tuning strategies and ensemble approaches to enhance overall performance in object detection tasks. The model achieves a maximum accuracy of 100% during low traffic density, but precision rates decline when the total number of vehicles increases. In general, the developed model trained with the ISTraffic dataset is capable of detecting and counting objects in photos or real-time videos. These results are promising and indicate the potential effectiveness of the algorithm in live settings. The ISTraffic dataset addresses key limitations of existing vehicle detection datasets by providing comprehensive and consistent annotations, high image complexity, a wide range of real-world scenarios, and diverse vehicle types and environmental conditions. It ensures all visible objects are labeled and is more accessible and cost-effective, making it a valuable resource for improving vehicle detection models. By leveraging the real-time detection capabilities of the YOLOv7x-SORT algorithm, the vehicle detection system can provide accurate and timely information, allowing for efficient management and optimization of road networks. The comparison between the base model (YOLOv7-COCO) and the proposedmodel (YOLOv7-ISTraffic) reveals that while YOLO-COCO serves as a solid foundation for vehicle detection, the YOLOv7-ISTraffic model significantly enhances detection accuracy and robustness, particularly in complex traffic scenarios. This improvement is evident in the higher precision and recall rates achieved by YOLOv7-ISTraffic, making it more effective for real-time applications. Additionally, YOLOv7-ISTraffic's ability to adapt to varying environmental conditions further underscores its superiority over the base model. Additionally, the data obtained from the live broadcast stream can be utilized for urban planning purposes, helping authorities make informed decisions regarding infrastructure development and traffic management strategies.