# An Efficient Parallelization of Microscopic Traffic Simulation
## 3 Results
### Test Scenarios  
The implemented simulator has been tested with two realistic large-scale microsimulation scenarios: a smaller scenario for the metropolitan area of Bologna, Italy, and a larger scenario for the entire San Francisco Bay Area. The main characteristics of the scenarios are summarized in Table 4.  
The Bologna SUMO scenario contains a detailed street network of the city of Bologna, covering approximately 50 km\({}^{2}\). It includes bikeways and footpaths but excludes the rail network. A simplified road network of 3703 km\({}^{2}\) extends into the metropolitan hinterland of Bologna; the entire simulated area has a population of about one million. The road network has been imported from Openstreetmap [31] and manually refined. The travel demand has been essentially generated from the OD matrices. For the core part of the city,  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Items** & **Bologna** & **S.F. Bay Area** \\ \hline Total edges & 58,882 & 181,932 \\ Total length [km] & 3737 & 45,060 \\ Number of trips & 1 M & 18 M \\ Number of TTL & 312 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristics of the scenarios.  
Figure 6: Graphical User Interface of HybridPy with data browser (left panel) and interactive network visualization (in blue), foodpath (in green) and reserved roads (in purple) and vehicles (in yellow). Below the network are the buttons to operate the ParSim. Note that the vehicle queues waiting at a traffic light and two bicycles running on the red bikepath.  
the OD matrices have been disaggregated to create a synthetic population with daily travel plans, including car, bicycle, motorcycle, and bus modes. The hinterland demand matrices were used to create trips for external and through traffic [32,33]. Public transport services have been created from GTFS data, obtained from the local bus operator TPER.  
For a fair comparison with the parallelized simulation, the following modifications have been made to the original SUMO scenario: (1) Within the city area, the original SUMO scenario simulates the door-to-door trip of each person of the synthetic population, e.g., a person walking from their house to the parking area, taking their car, etc; as pedestrians are currently not simulated in ParSim, all walks have been removed and only vehicle movements have been retained. (2) The original scenario uses the SUMO sublane lane-change model, where, for example, a car and a bike can stay side by side in the same lane. As such detail is not modeled with the parallelized version, the sublane model has been replaced by a simplified lane-change model (LANECHANGE2013), where only one vehicle can stay at one place on the same lane. (3) The routes have been pre-calculated for the comparison, such that SUMO and ParSim use the exact same routes; no re-routing during the simulation run is performed.  
The second scenario is the mesoscopic BEAM CORE model of the entire San Francisco Bay Area. The original scenario consists of only 10% of the active population [14]. The BEAM CORE network and the travel plans have been imported into hybridPY. Like the Bologna model, only vehicle trips have been extracted and pedestrians are not simulated. The demand has been upscaled by a factor of 10 by replicating existing routes and randomly varying their departure time by \(\pm 15\) min.  
Concerning the computer hardware used for the tests, all experiments were performed on a laptop with a GeForce RTX 4060 GPU from NVIDIA, purchased in Italy, that contains 3072 CUDA cores and 8 GB of RAM. The CUDA version used was 12.8. The CPU running the SUMO version 1.23.1 simulation was a 12 th-Gen Intel(R) i7-12700. Speed measurements are performed in headless mode, allowing ParSim to run at full GPU speed without rendering overhead. In addition, the time for uploading the data arrays into the GPU memory is excluded from the simulation runtime measurements.