Accurate vehicle detection from traffic videos is essential for efficient traffic management, enabling real-time monitoring, congestion analysis, and traffic flow optimization. This research work introduces a novel framework, Multi-layer Contiguous Virtual Layer (MCVL), employing heuristic techniques to enhance detection accuracy and operational efficiency in urban mobility solutions. To evaluate MCVL's effectiveness in vehicle detection, this paper conducts a comparative analysis of three distinct methods: optical flow, blob tracking, and YOLO sort, for detecting vehicle movements in traffic videos. While existing research predominantly relies on deep learning for motion boundary detection, these approaches often entail extensive training on large datasets, leading to high computational costs and increased complexity. To mitigate these challenges, this paper proposes three alternative algorithms that leverage existing methods, avoiding the need for intensive training phases while maintaining robust performance in real-world traffic scenarios.