## 7 Discussion  
In this paper, a graphical analysis was conducted for the proposed threshold method for rollback. This rollback approach is expected to allow for maintaining similar traffic conditions between vehicles and transmitting accurate traffic conditions to other vehicles  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Applied Speed (km/hr)** & **Flow Rate (veh/hr)** & **Applied Speed (km/hr)** & **Flow Rate (veh/hr)** \\ \hline
1 & 60 & 8 & 335 \\
2 & 90 & 9 & 365 \\
3 & 140 & 10 & 395 \\
4 & 170 & 11 & 425 \\
5 & 220 & 12 & 455 \\
6 & 260 & 13 & 485 \\
7 & 300 & 14 & 515 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Applied speed and corresponding flow rate.  
Figure 6: Speed flow diagram.  
beyond the network boundaries of the vehicles. In the proposed scenarios, each in-vehicle simulation models a small portion of the overall network and provides detailed traffic state information. Traffic simulation and data processing are performed in a distributed fashion by multiple vehicles. Each in-vehicle simulation is designed to run in real-time and update its estimates when it is necessary. A local central server receives the traffic states from multiple in-vehicle simulations. Traffic estimates are not guaranteed to be received in time stamp order, since in-vehicles simulations run concurrently. Also, a traffic state can be projected by multiple in-vehicle simulations. A mechanism is needed to coordinate the transmitted data, combine values into a composite value, and save in Space-Time Memory. Optimistic execution inspired by Time Warp can mitigate the synchronization problem allowing each logical process to execute asynchronously. This approach provides increased computing capacity with a time-synchronized approach. Traffic data are constantly updated through rollbacks to reflect any traffic changes which warrant a threshold violation. The proposed methodology is aimed to provide asynchronous execution. integrate distributed traffic simulations and coordinate the estimates generated by multiple vehicles. Also, the rollback process allows for maintaining similar traffic conditions between vehicles and transmitting accurate traffic conditions to other vehicles beyond the network boundaries of the vehicles. With proper feedback the proposed simulation will be able to capture dynamically changing traffic conditions and provide more up-to-date and more robust estimates.  
In a future publication, a comparison to the use of a distributed simulation standard, and a performance analysis of the simulation will be investigated. They will provide more insight regarding the applicability of the model. For the application in real world, the proposed system will have to be tested with real world data. Also, the potential market acceptance and the adoption need to be investigated. It is believed that the proposed method can function in uncongested traffic conditions. However, a more detailed analysis of the system will have to be conducted in urban environments including model validation and performance results. Robust analysis will enable researchers to better understand and apply the proposed system into large-scale transportation systems.  
This work was partially supported by NRF-2020R1A2C1011060 of the Korean government. The data presented in this study are available on request from the corresponding author.  
The author would like to acknowledge [PERSON] and [PERSON]. The authors declare no conflict of interest.