## 4 Data Description and Processing
### Data Reduction and Extraction
It can be noted that, when acceleration varies greatly in unit time, the absolute value of jerk increases, which means that \(\left|\overline{J}\right|_{mean}\) is negatively correlated with \(\left|\overline{a}\right|_{mean}\). Therefore, jerk can represent the change in acceleration and can be applied to identify the driving state as a supplement. Mean acceleration is represented as \(\left|\overline{a}\right|_{mean}\), standard deviation of acceleration as \(\left|\overline{a}\right|_{std}\), mean jerk as \(\left|\overline{J}\right|_{mean}\), and standard deviation of jerk as \(\left|J\right|_{std}\). The second step is to extract the data from all of the intersections for the entire selection. The data collected in the long trip only requires all the accurately recorded data of the driver, from entering the intersection until exiting the intersection. Therefore, all other data from the road sections should be ignored. Using VBOX Tools and Google Earth, all of the 2-dimensional coordinates of the intersections on the test routes were located. A dilemma zone is located in signalized intersections where drivers driving at the legal speed limit can neither stop nor clear the intersection successfully, which may cause an impact on traffic safety. Therefore, in this paper, the range of the intersection influence area included the 200 m before a truck would pass through the intersection. The standard for extracting the data was all of the vehicle's kinematic parameters within an area 200 m away from the intersection, which is shown in Figure 6.  
Relying on the distance formula and the latitudinal and longitudinal data, all data within the range of 200 m from intersections were extracted using Python. Figure 7 shows the process of a test truck driver passing through the intersections encountered in one trip. Driving data for all intersections are shown in the figure, with  
Figure 6: Extraction range of intersection data. each colored line segment indicating an intersection. The mean acceleration and jerk data were processed by time window. As seen in Figure 7, at intersection 5 (which is represented by a green line), the acceleration fluctuates and the change in jerk is also very steep, indicating that the speed change of the truck was very dramatic when it passed through intersection 5. However, at intersection 6 (light green line), although the acceleration fluctuated, the jerk basically remained stable in a small range, 0-0.05. What does that illustrate? The process of the driver passing through the intersection was basically a process of slowly and constantly accelerating. Since the heavy truck described in this article was a concrete mixer, its weight could reach 40-50 tons during the transportation process. According to the speed limit, the maximum speed of the heavy-duty truck could not exceed 80 km/h. According to power performance and braking performance, the acceleration could not exceed 0.4 g (3.92 m/s\({}^{2}\)). The final data sample excluded the abnormal data and abnormal interference to the test truck. Sample data are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time** & \(|\overline{a}|_{mean}\) **(m/s\({}^{2}\))** & \(|\overline{a}|_{std}\) **(m/s\({}^{2}\))** & \(|\overline{J}|_{mean}\) & \(|J|_{std}\) & **No. of** **Crossing** \\ \hline
101,700 & 0.9973 & 0.5398 & 0.0358 & 0.2652 & 1 \\
101,702 & 0.9485 & 0.3153 & 0.0336 & 0.1678 & 1 \\
101,704 & 0.9448 & 0.2817 & 0.0028 & 0.1044 & 1 \\
101,706 & 1.0852 & 0.3414 & 0.0011 & 0.1003 & 1 \\
101,708 & 0.8823 & 0.4861 & 0.0229 & 0.1104 & 1 \\
101,710 & 0.7189 & 0.2573 & 0.0005 & 0.0203 & 1 \\
101,712 & 0.7794 & 0.2207 & 0.0101 & 0.0625 & 1 \\... &... &... &... &... &... \\
102,110 & 1.6754 & 0.7505 & 0.0205 & 0.1783 & 5 \\
102,112 & 2.2657 & 0.5997 & 0.0286 & 0.1719 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sample data processed by time window. Figure 7: Acceleration and jerk at intersections in time windows.