## 5 Experimental Section
### Experimental Results  
We undertake many experimental tests during various time periods for several work days. Specifically, we evaluate the performance of algorithms during representative time blocks of light, moderate, and busy traffic conditions. Time slots of midnight, noon, and morning and evening rush hours are accordingly taken into account. Experimental results are given in Tables 1-12. Tables 1-3, Tables 4-6 and Tables 7-12 show the performance comparisons for light, moderate and rush-hour traffic, respectively. The traffic detection accuracies of the experiments are consistent for the same time slot of work days. In other words, they are unbiased and reliable. The results show that LVF and DTV detection accuracies given by the proposed methods are significantly higher than those by the conventional method. It is noted that DTV detection accuracy of each proposed method is nearly perfect. Moreover, the proposed method 2 generally achieves better LVF detection accuracy than the proposed method 1. Accordingly, it is confirmed that the proposed methods can tackle the problem of lane-crossing FFT side lobes more appropriately. In addition, the proposed method 2 can achieve the best overall performance among the schemes tested. Tables 1-12 show that the detected vehicle number of lane A (lane D) given by the proposed methods tend to be less than the actual vehicle number. Conversely, the detected vehicle number on lane B (lane C) given by the proposed methods tends to be more than the actual vehicle number. This phenomenon may be explained by using road site traffic conditions. A car within lane A (lane D) may be driven close to or may slightly cross the borderline between lanes A and B (lanes D and C) for avoiding motorcycles and bicycles on the sideway beside lane A (lane D). Therefore, the magnitude peak of an edged FFT main lobe which should belong to lane A (lane D) may appear within lane B (lane C). However, since separation blocks are between lanes B and C, lane B (lane C) drivers cannot  
Figure 15: A snapshot of edged FFTs of received vehicle signals and clutter. do what lane A (lane D) drivers do. The gain of the vehicle number within lane B (lane C) and the loss of the vehicle number within lane A (lane D) are almost balanced. Therefore, the proposed methods achieve very high DTV detection accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{(**a**) Proposed Method 2} \\ \hline
**Lane** & **Actual Vehicle Number** & **Detected Vehicle Number** & **LVF Accuracy** & **DTV Accuracy** \\ \hline A & 23 & 21 & 91.3\% & 98.1\% \\ B & 30 & 31 & 96.7\% & 98.1\% \\ \hline C & 27 & 29 & 92.6\% & 100\% \\ D & 23 & 21 & 91.3\% & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c} \hline \hline
**Lane** & **Actual Vehicle Number** & **Detected Vehicle Number** & **LVF Accuracy** & **DTV Accuracy** \\ \hline A & 23 & 27 & 82.6\% & 81.1\% \\ B & 30 & 36 & 80.0\% & 80.0\% \\ \hline C & 27 & 33 & 77.8\% & 80.0\% \\ D & 23 & 27 & 82.6\% & 80.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of LVF (lane vehicle flow) and DTV (directional traffic volume) detection during 12:25–12:35 AM (light traffic) using (**a**) proposed method 2 and (**b**) conventional method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{(**a**) Proposed Method 1} \\ \hline
**Lane** & **Actual Vehicle Number** & **Detected Vehicle Number** & **LVF Accuracy** & **DTV Accuracy** \\ \hline A & 19 & 17 & 89.5\% & 97.4\% \\ B & 20 & 21 & 95.0\% & 97.9\% \\ \hline C & 23 & 25 & 91.3\% & 97.9\% \\ D & 25 & 22 & 88.0\% & 97.9\% \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c} \hline \hline
**Lane** & **Actual Vehicle Number** & **Detected Vehicle Number** & **LVF Accuracy** & **DTV Accuracy** \\ \hline A & 19 & 23 & 78.9\% & 76.9\% \\ B & 20 & 25 & 75.0\% & 76.9\% \\ \hline C & 23 & 26 & 87.0\% & 81.3\% \\ D & 25 & 31 & 76.0\% & 81.3\% \\ \hline \hline \end{tabular}
\end{table}