# Harmonized Autonomous-Human Vehicles via Simulation for Emissions Reduction in Riyadh City
## 3 Materials and Methods
### SUMO Simulation (2025)  
We used the Simulation of Urban Mobility (SUMO) framework to model mixed AV-HV traffic conditions along a 20 km corridor of Prince Mohammed bin Salman Road in Riyadh. The simulation network was extracted from OpenStreetMap (OSM) and processed using SUMO's netconvert and NetEdit tools. To replicate real-world fleet diversity, vehicle types in the simulation were distributed based on publicly available statistics from the Saudi Ministry of Transport and regional fleet composition reports. The resulting vehicle mix included 55% sedans, 20% SUVs, 15% pickup trucks, 5% hybrid vehicles, and 5% electric vehicles (EVs), reflecting typical private and commercial vehicle proportions in Saudi Arabia. This distribution was implemented using SUMO's vehicle distribution functions and applied uniformly across all scenarios to ensure comparability. This corridor was chosen due to its strategic role in connecting airport traffic with central Riyadh, diverse traffic patterns, and candidacy for smart mobility upgrades in the near future. The network was extracted from OpenStreetMap (OSM) using the SUMO netconvert tool and visualized in NetEdit. The map shown in Figure 3 illustrates the detailed urban structure of the simulation region, where green areas represent accessible road infrastructure and pink polygons indicate restricted or building zones.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Intersection ID** & **Hour** & **Vehicle Count** & **Control Type** \\ \hline R123 & 08:00 & 540 & Signalized \\ R124 & 17:00 & 680 & Roundabout \\ R125 & 14:00 & 430 & Signalized \\ R126 & 09:00 & 510 & Stop Sign \\ R127 & 18:00 & 720 & Roundabout \\ R128 & 07:00 & 490 & Signalized \\ R129 & 10:00 & 615 & Signalized \\ R130 & 12:00 & 580 & Stop Sign \\ R131 & 15:00 & 660 & Signalized \\ R132 & 16:00 & 700 & Roundabout \\ R133 & 11:00 & 555 & Signalized \\ R134 & 13:00 & 605 & Roundabout \\ R135 & 19:00 & 745 & Signalized \\ R136 & 20:00 & 480 & Stop Sign \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample traffic flow data.  
Figure 3: Visual layout of the simulated road network in SUMO (Prince MBS Corridor).  
The simulation was configured using the osm.sumocfg file with the following components:  
* **Simulation duration:** 54,589 s (approx. 15 h)
* **Vehicle types:** Passenger cars only (HVs and AVs)
* **Routing:** Dynamic trip assignment based on demand profile
* **Traffic scale:** Set to 1.0 using the GUI traffic scaling tool
* **Output:** tripinfos.xml, stopinfos.xml, vehroutes.xml, emissions.xml  
In total, 9215 trips were simulated with a traffic pattern representative of Riyadh's weekday peak (7:00-9:00 a.m., 4:00-6:00 p.m.) and off-peak periods. AV behavior was modeled using SUMO's Krauss car-following model, with the parameters set to reflect typical AV improvements: reduced reaction time (0.5 s), shorter minimum gap (1.5 m), and increased acceleration/deceleration capacities. Vehicle types were randomly assigned using SUMO's distribution functions based on regional vehicle composition statistics: 55% sedans, 20% SUVs, 15% pickups, 5% hybrids, and 5% electric vehicles (EVs). A sample of the simulated dataset using SUMO is shown in Table 3. All experiments were executed using SUMO version 1.17.0 on a system with an Intel i7-12700 CPU, 32 GB RAM, and Ubuntu 22.04. Simulations were conducted via SUMO's command-line interface (CLI) for reproducibility and batch processing.