## 4 Experimental Results and Analysis
### Road Data Extraction
#### 4.2.1 Traffic Density Detection  
Delving deeper into the field of traffic density detection, our attention is focused on carefully quantifying the presence of vehicles in observed street scenes. This particular regulation has resonated widely and has a major impact not only on the current state of the road but also on the complex web of CO\({}_{2}\) emissions closely linked to vehicle activity. The accurate identification of cars with rear orientations relative to the camera's perspective is important to this quest [23]. This prudent criterion is the result of a thorough understanding of vehicle dynamics and their complex influence on our immediate surroundings. Vehicles traveling in the same direction as us are extremely important because they can shape our trajectory and impact the overall traffic flow. This selection principle is guided by two rationales. Firstly, cars traveling in the same direction as ours are inherently linked, with their motions firmly intertwined with ours. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Date (DD/MM/YY)** & **Source** & **Destination** & **Distance (Km)** & **Time (Min)** & **Number of Images** & **PEMS Data** \\ \hline \multirow{2}{*}{Eco-route} & 16/06/2023 & ESIGELEC & Bosgouet & 27 & 28 & 6785 & 1334 \\  & 16/06/2023 & Bosgouet & ESIGELEC & 26 & 24 & 5087 & 1125 \\ \hline \multirow{2}{*}{Fastest route} & 16/06/2023 & ESIGELEC & Bosgouet & 24 & 27 & 6109 & 1278 \\  & 16/06/2023 & Bosgouet & ESIGELEC & 29 & 23 & 4955 & 1752 \\ \hline \multirow{2}{*}{Eco-route} & 24/07/2023 & ESIGELEC & Yvetot & 44 & 46 & 11,224 & 3211 \\  & 24/07/2023 & Yvetet & ESIGELEC & 46 & 43 & 11,379 & 2545 \\ \hline \multirow{2}{*}{Fastest route} & 24/07/2023 & ESIGELEC & Yvetot & 43 & 44 & 11,139 & 3132 \\  & 24/07/2023 & Yvetet & ESIGELEC & 42 & 39 & 10,144 & 2195 \\ \hline \multirow{2}{*}{Eco-route} & 25/07/2023 & ESIGELEC & Saint-Saens & 42 & 55 & 12,664 & 3819 \\  & 25/07/2023 & Saint-Saens & ESIGELEC & 43 & 52 & 12,162 & 3322 \\ \hline \multirow{2}{*}{Fastest route} & 25/07/2023 & ESIGELEC & Saint-Saens & 39 & 48 & 12,001 & 3052 \\  & 25/07/2023 & Saint-Saens & ESIGELEC & 39 & 46 & 11,586 & 2984 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Information collected during experiments on various directions. As a result, their existence has a substantial impact on overall traffic dynamics, necessitating a thorough examination. Secondly, this distinction ensures that our analysis is grounded in a practical perspective. Vehicles with opposing frontal orientations, although essential to the overall traffic ecology, have comparatively less influence on our local operational domain. In this pursuit, we opt to use a pre-trained YOLOv5 model [24], a well-regarded solution recognized for its proficiency. This model, having already demonstrated commendable performance, has been trained on the comprehensive Vehicle Orientation Dataset [25]. However, our deliberate emphasis remains on a subset of these categories, specifically centered around cars, trucks, and buses. Figure 5 illustrates the successful detection capabilities of our model. we have established two categories termed 'coming vehicle', and 'outgoing vehicle'. This classification groups vehicles into a single class, distinguished by their orientation relative to their trajectory. Specifically, vehicles presenting a back view are grouped as 'outgoing vehicles', indicating their departure from the observer's viewpoint. Conversely, vehicles exhibiting a frontal view indicate their approach and are classified as 'Coming vehicles'. Our primary focus is squarely on 'outgoing vehicles', aligning seamlessly with our objectives because they have the potential to impact our traffic density. This discerning approach allows us to delve into vehicular dynamics with enhanced precision, dissecting intricate traffic patterns and their consequential environmental impacts. Figure 5: Highlighting vehicle detection with orientation: showcasing examples from our dataset.