# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 4 Evaluation and Limitations
### Counting Results
#### 4.2.3 Traffic on A Highway  
Another road type which is typically recorded is a highway. The classification and counting system analyses one in a province in the Netherlands called Gelderland. The road is recorded in a 45\({}^{\circ}\) angle from the side. The course of the road is a slight left curve and vehicles are entering and exiting the road on both ends. One end is on the bottom left of the image, the other one on the top right. Due to the slight curve vehicles at the top right end are recorded a long time which results in a lot of small vehicles in a restricted area. In the middle of the recorded road it splits itself up to an exit to the left. In total there are two lanes for each direction.  
Figure 13 shows the driving paths generated by the prediction model for the corresponding request intervals, namely 500 ms, 1000 ms and 1500 ms. Noticeable is the distribution of the driving paths in regard to the request intervals. They are more focussed for lower intervals. This is due to the fact that the proposed system detects more vehicles in that restricted are on the top right where the vehicles are also more focussed. If the request interval is lower, the more detections the system performs. Hence, this also results in more updates for the prediction model and therefore more driving paths in that area.  
As mentioned before a majority of driving paths are focussed on a small area on the top right for the recorded highway. This fact also reflects in all the counting results for the corresponding request intervals. The 500 ms results in Table 8 shows the greatest error. There is a total of 163 cars in the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Actual**} \\ \hline  & & **Truck** & **Car** & **Motorbike** & **Lost** & **Sum** \\ \hline \multirow{4}{*}{Counted} & Truck & 19 & 0 & 0 & 0 & 19 \\ \cline{2-7}  & Car & 0 & 9 & 0 & 0 & 22 \\ \cline{2-7}  & Motorbike & 0 & 0 & 0 & 0 & 0 \\ \cline{2-7}  & Lost & 5 & 47 & 5 & 0 & 57 \\ \hline \multicolumn{7}{c}{Sum} & 24 & 56 & 5 & 0 & 85 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Counting results per vehicle type from the confederation bridge with 500 ms request interval.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Actual**} \\ \hline  & & **Truck** & **Car** & **Motorbike** & **Lost** & **Sum** \\ \hline \multirow{4}{*}{Counted} & Truck & 35 & 10 & 0 & 0 & 45 \\ \cline{2-7}  & Car & 0 & 22 & 0 & 0 & 22 \\ \cline{2-7}  & Motorbike & 0 & 0 & 0 & 0 & 0 \\ \cline{2-7}  & Lost & 0 & 61 & 9 & 0 & 70 \\ \hline \multicolumn{7}{c}{Sum} & 35 & 93 & 9 & 0 & 137 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Counting results per vehicle type from the confederation bridge with 250 ms request interval.  
Figure 13: Prediction model visualizing the driving paths on a highway in the Netherlands. Request intervals are 500 ms in (**a**), 1000 ms in (**b**) and 1500 ms in (**c**).  
recorded sequence. However, the counting and classification framework counts another 172 cars in addition. In total this is an error rate of 86.4% for all the vehicles in terms of additional object counts. This is a result of the focussed driving paths. For the regions where no paths are constructed vehicles get counted as often as they are detected because they cannot be associated by the prediction model with other vehicles. Therefore, the lower the request interval, the more detections are performed, hence leading to an additional counting. The same holds true for the 1000 ms interval shown in Table 9. The error rate in terms of additional counting is 18.6%. This decreased number is because of the higher request interval where vehicles are not detected so often. For the interval with 1500 ms the results change. There are no more additional vehicles counted as depicted in Table 10. However, now the error is due to lost vehicles. In total the corresponding error rate is 24.5%. As a reason of the high traffic in the scene, the prediction model needs around 12 s for initialization for every request interval.