## 6 Results
### Experimental Setting  
1. The establishment of the simulation experiment environment. Before the experiment started, we used Python3.8 to build a simulation environment for vehicle communication scenarios. First of all, we observed the changes in the traffic flows at ten highway intersections in reality at 7:00 a.m.-9:00 a.m. and 3:00 p.m.-5:00 p.m. through video tapes, and obtained the speed characteristics and the interval distributions of vehicles entering the intersection. According to the observation results, the vehicle speeds obeyed normal distributions with a mean of 23 m/s and a variance of 6; the interval between two vehicles on the same lane entering the intersection obeyed a normal distribution with a mean of 3.7 s and a variance of 1.2. We then simulated a two-way six-lane highway with a length of 2000 m and generated vehicles with different speeds and directions at the beginning of the lanes based on the survey results. In addition, each vehicle had a different data generation rate. In this way, after a period of simulation, the road was filled with a certain number of vehicles. At this time, the possible collision of the vehicle must be considered. In the simulation environment of this paper, when two vehicles might have collided, the rear vehicle may have taken measures to change lanes and brake to avoid the collision. The speed of the vehicle also had a probability to change. Another important point is that the RSU was located in the middle of the road in the experiment. In addition, in the simulation program, the position of the vehicle was updated every 0.1 s. In previous vehicular communication simulations, there were insufficient numbers of vehicles [16; 17; 19; 20], or insufficient mobility of vehicles (vehicles either did not move [16] or moved at a constant speed [21]). In this paper, the driving conditions of the vehicle in the real environment were fully imitated in the simulation environment to reflect the real performance of the vehicle communication method. Experimental parameter settings. To verify the applicability of CTDHR under the high-speed movement of the vehicle, the speed range of the vehicle was 18 to 28 m/s. Moreover, the number of vehicles in the experimental scene obeyed the normal distribution of \(\mathcal{N}(130,5)\). Although the communication radius was extended to 2000 m in IEEE802.11 bd, the road was usually curved, and there were obstacles between two vehicles to block the transmission of signals, so the communication radius of DSRC was set to 400 m in this paper. \(l_{ik}\) obeys the normal distribution of \(\mathcal{N}(17,8)\) in milliseconds, but its value is still limited to \([\lceil min,\lceil max\rceil]\). Since it is difficult to obtain the accurate data transmission rate of DSRC in the current research, according to the spectrum resources and sub-carrier spacing of 802.11 bd [31], the data transmission rate of DSRC was estimated to be 200 Mbps using the Nyquist criterion and Shannon formula. Refer to Table 3 for other parameter settings. 3. To facilitate the comparison of the CTDHR and TDCR clustering algorithms, this paper uses cluster stability and the number of clusters to measure the quality of cluster results. The stability of a cluster is measured by the cluster hold time, which refers to the time from when the cluster is established to when the distance between any CM and CH in the cluster exceeds the DSRC communication radius. In addition, the number of clusters in the system should be kept in an appropriate range because a smaller number of clusters can reduce the number of hops on the VANET link, thereby reducing the delay of packet transmission. However, if the number of clusters is too small, the interval between two clusters will be too large to establish a communication link, or congestion may easily occur during communication. 4. Both CTDHR and TDCR were tested in the same simulation environment. 5. We implemented CTDHR and TDCR using Python 3.8.