# Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
## 5 Implementation and Experimental Results  
We used Simulation of Urban Mobility (SUMO) and Veins (PLEXE) [41], open-source simulation frameworks used in many famous studies, to simulate the vehicle behavior and evaluate the proposed scheme's performance. We simulated a 4 km freeway with \(k=4\) and \(v_{max}\)= 40 m/s (see Figure 1. Members need to accelerate when joining the platoon because the leader keeps the original velocity. Thus, we set 40 m/s (144 km/h) for the vehicles farther away from the leader to quickly catch up to its front platoon members (avoid slow merging).) In addition, we also set vehicle motions to run with the ACC model and CACC model (see Equations (9) to (11)), which can be found in the LuST dataset--including patterns for real traffic in Luxembourg, Europe ([[https://github.com/lcodeca/LuSTScenario](https://github.com/lcodeca/LuSTScenario)]([https://github.com/lcodeca/LuSTScenario](https://github.com/lcodeca/LuSTScenario)), accessed on 24 August 2022). In our simulation, the vehicle appearances followed the following stochastic process. For each lane, a time interval for generating vehicles is set to an uniform random variable. Specifically, the timer interval for each lane is set as follows (in unit of seconds): \(t_{Lanea}=random.randint(40,70)\); \(t_{Lanea}=random.randint(24,50)\); \(t_{Lane1}=random.randint(30,60)\); \(t_{Lane0}=random.randint(20,50)\). Furthermore, the number of vehicles generated during a time interval is also set to a uniform random variable with a range between 1 and 20, i.e., \(random.randint(1,20)\). Note that the parameters of these random variables could be set to fit the desired simulation scenario. The velocity is set according to the standard highways' maximum (110 km/h) and minimum (80 km/h) velocity limits. In the real-world scenario, the inside lane is usually for the high-velocity vehicles to pass low-velocity vehicles. Thus, in our simulation, we set different velocity limits for different lanes. Since the traffic patterns may vary in different countries worldwide (which often vary due to local laws, driving style, and community awareness), the researchers can tweak this velocity limit setting to simulate the traffic models fitting their local environment. The occurrence rate of vehicles in each lane was randomized. The rest of the parameters are shown in Table 3. We also simulated three cases of the distance where a vehicle can receive notifications about the accident: 3500, 2500, and 1500 m ahead. Moreover, we also analyzed the impact of different platoon size  
Figure 3: The workflow for the proposed decentralized Coordinated Platooning Planning (CPP) control. The vehicles maintain a safe inter-vehicle distance (safe distance). settings on the scheme's efficiency performance. In this work, the maximum platoon size was 20 [20,26]. In this simulation, an accident blocks Lane 0. Figures 4 and 5 show the average number of platoon members when using the adaptive configuration of the platoon size in Lane 0 to Lane 3. The number of vehicles in the platoon is limited by the maximum platoon size. When the platoon forms, it will not reach the maximum platoon size every time. This means that there will be many vehicles with different platoon members passing the accident spot before the accident is cleared. When adding up the number of vehicles in these platoons, it corresponds to the number of vehicles observed in Figures 6-9. However, the platoon length of each platoon can be dynamically adjusted when vehicles enter and exit the platoon. We found that the largest platoon size was less than 14 in our simulations. Through simulations, Figures 6-8 show the mean time taken for the vehicles to exit the congestion spot if they receive an accident warning 3500, 2500, and 1500 m ahead of the accident spot. The mean time represents the average duration of an observed vehicle from receiving the warning message until passing the accident point.