# An Efficient Parallelization of Microscopic Traffic Simulation
## 4 Discussion  
### Main Results  
The main results are that ParSim, through its parallelization, is approximately 3000 times faster than a similar SUMO simulation on a single-core CPU. Note that the SUMO simulation tends to show artificial gridlock at large intersections and high traffic flows due to the complex algorithms that resolve conflicts between interacting vehicles. In contrast, ParSim does not have conflicts within the intersection, as vehicles move to the next edge as soon as enough room becomes available. This means that ParSim should not show artificial gridlocks. On the other hand, the simplified intersection handling by ParSim may not only neglect turning delays but also, in the absence of conflicts within junctions, tend to overestimate the throughput in intersections. The 1 million trips of the 24 h Bologna scenario could be finished in just 41 s, while it took 26 min for the completion of the larger 24 h, 18-million-trip Bay Area scenario. The  
Figure 10: The regression diagrams present the comparison of the simulated traffic volumes between ParSim and SUMO over different demand thresholds and time periods: the 25% travel demand in the off-peak (**a**) and peak hours (**b**) and the 50% travel demand in the off-peak (**c**) and peak hours (**d**). Red dashed lines represent the linear regression \(y(x)\), see labels in each graph. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**Runtime [s]**} & \multicolumn{2}{c}{**Memory Footprint [GB]**} \\ \cline{2-5}  & **ParSim** & **LPSim** & **ParSim** & **LPSim \({}^{*}\)** \\ \hline
24 h, 18 M trips & 1534.68 & - & 5.07 & \(\approx\)160 ** \\ Rush hour, 3 M trips & 21.73 & 144 *** & 0.826 & \(\approx\)40 \\ \hline \hline \end{tabular}  
* These are essentially upper limits, because estimate is based on the number of GPUs in use. ** Corresponding to eight NVIDIA A100 GPUs with 40 GB of memory each. *** LPSim running on a single NVIDIA A100 GPU with 40 GB of memory; no communication between GPUs take place.  
\end{table} 
Table 8: Simulation speed and memory usage comparison between ParSim and LPSim. simulator seems to be seven times faster than LPSim running on a similar scenario, while also occupying less memory on the GPU. However, it should be acknowledged that LPSim has a more detailed lane-change model that allows mandatory lane maneuvers and ParSim did not perform traffic-light operations for this specific scenario. So, the higher speed of ParSim could be partially explained by the simpler simulation processes. In any case, execution times can only be reliably compared by simulating exactly the same network and the same trip patterns. The simulation results of ParSim were validated by comparing the average waiting times and average speeds with the output of the established SUMO microsimulator. The average travel speeds of ParSim vehicles were approximately 10% higher compared to the average speed determined by SUMO, presumably due to the simpler intersection model of ParSim. In addition, ParSim has the possibility of visual verification and debugging through the GUI capabilities of HybridPy. The visualization turned out to be crucial for code writing and debugging. It can also be helpful for network planning tasks. In addition, the hourly edge flows of ParSim and SUMO have been plotted on a map for comparison. Flows were proportional, but with a distribution around the unit line. The advantages of the presented simulator are based on multiple design decisions. 1. The network and vehicle model have been implemented so that the occupied memory on the GPU is minimized. We have shown that even large-scale simulations, like the one for the San Francisco Bay Area with 8 million inhabitants can fit on a medium-grade GPU with 8 GB of RAM. The fact that all processes take place on a single GPU constitutes a speed advantage over a solution with multiple GPUs, where communications take place; communication can delay processes or bandwidth limitations between the GPUs can become a bottleneck [18]. 2.