# Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
## 6 Conclusions  
This work introduces an efficient multi-platooning traffic coordination method to resolve temporary bottlenecks on multi-lane freeways. The proposed decentralized heuristic approach allows multiple vehicles to form platoons and perform lane-changing actions to exit the congestion areas by well-organized orders, thus improving the road capacity and reducing the exiting delay. The experimental results show that the heuristic scheme can reduce delay for the vehicles to exit the congestion area, particularly by up to 22% in typical cases of traffic density. We also perform extensive evaluations on various platoon size configurations to determine the best setting. We found that there is a trade-off between the expected length of the platoon and the feasibility of the coordination schedule. The average number of vehicles in a platoon for typical traffic density is approximately 10 to 14 in order to commit a good balance for the scheduling efficiency, control complexity, and safety. Several other findings are as follows. First, the platoon strategy can reduce accidents caused by human error and enable precise vehicle trajectory control by moving in well-organized orders. This work also highlights the importance of issuing warning notifications early. When the warning is sent farther away from the congestion spot, the vehicles will have more opportunities to execute lane-changing actions and build suitable shapes of platoons. This is because forming the platoons from the vehicles on different lanes requires time. Finally, we found that the platooning model can significantly help to optimize traffic capacity and leave the accident spot smoothly, as long as all of the vehicles voluntarily obey the constraints of the maneuver protocol. The model is susceptible to quick changes in road conditions, e.g., unexpected behaviors of rebel drivers, that can lead to many lane-changing actions, which is a costly procedure. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Mean Distance** & **Mean Time** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 4: The mean distance and mean time of the vehicles if all platoon members receive warning early. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1500 m Ahead**} & \multicolumn{2}{c}{**2500 m Ahead**} & \multicolumn{2}{c}{**3500 m Ahead**} \\ \cline{2-5}  & **Mean** & **Overall** & **Mean** & **Overall** & **Mean** & **Overall** \\ \hline
**Lane 1** & 17.7 vehicle & 9.3\% & 5.4 vehicle & 2.6\% & 1.5 vehicle & 0.79\% \\
**Lane 2** & 39.4 vehicle & 16.7\% & 21.6 vehicle & 8.6\% & 4 vehicle & 1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The mean of remaining vehicles that did not execute lane-changing in Lane 1 and Lane 2. In this work, we considered the vehicle type to be homogeneous. However, the presence of heavy vehicles is a critical factor to the traffic coordination. For example, heavy vehicles may not be able to keep up with the velocity of the platoon of vehicles because of their large weight (long trucks often have slow acceleration and velocity). As a result, it may take a longer time to schedule these heavy vehicles into the platoons than with small vehicles. A straightforward solution is to consider each heavy vehicle as an independent platoon. We believe that the platoon-based coordination models for mixed traffic can be an interesting topic for future work. Another research direction can be to support the fuel consumption factor in traffic coordination to optimize fuel efficiency [42] and investigate the effect of heavy traffic. Conceptualization, [PERSON]-[PERSON] and [PERSON]; methodology, [PERSON]-H.H., [PERSON]-[PERSON], [PERSON] and [PERSON]; software, [PERSON]-[PERSON] and [PERSON]-[PERSON]; validation, [PERSON]-H.H. and [PERSON]-L.N. ; formal analysis, [PERSON]-Y.L. and [PERSON]-[PERSON]; investigation, [PERSON], [PERSON], and [PERSON]-[PERSON]; resources, R.-H.H. ; data curation, [PERSON]-Y.L.