## 4 Results and Discussions
### Preliminary Analysis  
To assess the proportion of trucks and merging vehicles, a statistical analysis of data for different vehicle types from the seven data collection locations was conducted. Combining Figures 4 and 5, it was found that the proportion of recorded trucks in the morning is higher than during the afternoon peak hours. The truck proportions at both Location 3 and Location 6 exceed 30%. The higher number of vehicles per unit time recorded at Locations 0, 1, and 4 can be attributed to their role as major entry and exit points for Cologne, the fourth-largest city in Germany, resulting in a greater demand for travel. Locations 3 and 5  
Figure 3: The extraction of merging trajectories from the high-precision lanelet2. exhibit a significant proportion of merging vehicles, accounting for more than 30% of the total road users, thereby posing a higher likelihood of disrupting traffic flow. A comparative analysis of merging speed was conducted between \"mainline vehicles discretionary lane changes (DLC)\" and \"on-ramp vehicles mandatory lane changes (MLC)\". The average lane change speed for DLC is 30.0 m/s, while MLC has an average speed of 23.4 m/s. Mainline vehicles typically have more time and autonomy when changing lanes, allowing them to choose the right moment for lane changes when traffic flow is relatively stable. In contrast, merging vehicles face more urgent situations when changing lanes on the ramp; they need to quickly integrate into the main road traffic flow, thus emphasizing speed to match the speed of main lane vehicles to ensure traffic continuity. The average speed for merging vehicles is 23.47 m/s, while for mainline vehicles, it is 30.0 m/s. Prior to the occurrence of congestion, speeds tend to concentrate in the high-speed range; however, following the onset of congestion, there is a significant decrease in lane change speed for mainline vehicles. We introduce a variable _'Merging Distance'_ to investigate the spatial characteristics and designate the center point of the acceleration lane as the coordinate origin O (0, 0), as shown in Figure 2. The distance calculation formula is presented in Equation (1) below. The lateral distance of the vehicle's center point at the merging position from the merging point O is referred to as _Merging Distance_.The merging position is defined as the geometric  
Figure 4: Number of different types of vehicles and proportion of trucks. (The red square and dash line indicate the ratio of the number of trucks). Figure 5: Number of vehicles on the on/off-ramps and the proportion of vehicles on the on-ramp. (The blue squares and dashed lines represent the percentage of vehicles on the ramps at each location). centroid of the merging vehicle when it crosses the lane line. The variable \"_MD_\" represents the merging distance. \[MD=\left\{ \begin{aligned} -laneletLength(1)-lonLaneletPos(i),& i=1\\ lonLaneletPos(i)\,& i=2\\ lanletLength(2)+lonLaneletPos(i),& 1<i\leq 3\end{aligned} \right. \tag{1}\]  
_MD_'_Merging Distance_; _i_--index; \(lanletLength(1)\)--the laneletLength of merging area I; \(lanletLength(2)\)--the laneletLength of merging area II. The laneletLength can be found in Table 3. \(lonLaneletPos(i)\)--the distance from the vehicle's center point to the starting point of the current Lanelet (_i_) in the x-direction. The scatter plot in Figure 6 illustrates the merging points of different types of vehicles across various locations. (1) Merging Section  
A majority of vehicles merge in the Merging Section II. However, more than 30% of vehicles in Location2 and Location5 violate the traffic rules and choose to merge in Merging Section I, and the following excavation for merging behavior will exclude vehicles that drive across the solid line of the acceleration lane. (2) Merging distance-speed trend line  
As the merging position moves forward, the higher the vehicle merging speed.