# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 4 Evaluation and Limitations
### Counting Results
#### 4.2.1 Traffic Approaching a Toll Station  
One of the road parts analysed by the counting and classification application is a toll station in Russia. As expected the road characteristics are similar to most such stations around the world. There are three driving lanes at the bottom of the frame which splits up into eight lanes. Each of the eight lanes ends at a barrier where the cashier or a cash machine is located. The view angle of the camera is about 30\({}^{\circ}\) from the top to the ground. Furthermore, it is located at the right side of the lanes recording the traffic from the back, hence all vehicles are driving away from the camera. Additionally,  
Figure 10: Demonstration of the tracking and counting phase. Based on the predictions from the prediction model (**a**), the tracking component can track objects successfully (**b**). Additionally, passed by objects are counted with help of the tracking results.  
the video camera records an area up to about 400 m to have a greater angle to record. However, this means that all vehicles are of little size.  
For this scenario the evaluation is done with three different request intervals, namely 500 ms, 1000 ms and 1500 ms. For each of these request intervals, Figure 11 shows the corresponding prediction models with the estimated driving paths. In principle these driving paths are nearly the same for all the request intervals due to the wide area of recorded roads.  
However, the counting results differ greatly. Table 3 shows the outcome of the proposed application. The prediction model needs around 30 s to built itself up, the tracking and counting begins immediately afterwards. The results show that from the 111 vehicles only 7.2% are not counted successfully. After increasing the request interval to 1000 ms, the counting gets worse as depicted in Table 4. For this setting the prediction model needs about 1 min and 9 s for initialization. The amount of lost vehicles increases, the error rate is 21.9%. This is due to the detection issues of small vehicles. Objects driving away from the camera covering great distances with this setup, hence becoming smaller in size quicker. A continuous tracking is getting more difficult with increasing request interval. Same holds true for Table 5. Here the error rate is 43.2% with a request interval of 1500 ms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Actual**} \\ \hline  & & **Truck** & **Car** & **Motorbike** & **Lost** & **Sum** \\ \hline \multirow{3}{*}{Counted} & Truck & 0 & 0 & 0 & 0 & 0 \\ \cline{2-7}  & Car & 1 & 63 & 0 & 0 & 64 \\ \cline{2-7}  & Motorbike & 0 & 0 & 0 & 0 & 0 \\ \cline{2-7}  & Lost & 0 & 18 & 0 & 0 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Counting results per vehicle type from a toll station with 1000 ms request interval.  
Figure 11: Prediction model visualizing the driving paths at a toll station. (**a-c**) have request intervals set at 500 ms, 1000 ms and 1500 ms, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Actual**} \\ \hline  & & **Truck** & **Car** & **Motorbike** & **Lost** & **Sum** \\ \hline \multirow{3}{*}{Counted} & Truck & 0 & 0 & 0 & 0 & 0 \\ \cline{2-7}  & Car & 1 & 102 & 0 & 0 & 103 \\ \cline{2-7}  & Motorbike & 0 & 0 & 0 & 0 & 0 \\ \cline{2-7}  & Lost & 0 & 8 & 0 & 0 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Counting results per vehicle type from a toll station with 500 ms request interval.