# Harmonized Autonomous-Human Vehicles via Simulation for Emissions Reduction in Riyadh City
## 6 Discussion  
This study demonstrates the significant potential of autonomous vehicles (AVs) to enhance urban mobility and environmental sustainability within mixed traffic systems. Our simulation results reveal that even moderate integration of AVs (as low as 10%) can lead to measurable improvements in traffic efficiency and substantial reductions in carbon emissions. As AV penetration increases from 0% (baseline) to 50%, average travel times decrease by approximately 25.5%, stop frequencies decline by 57.1%, and average vehicle speeds improve by 27.8%. These improvements reflect smoother and more coordinated traffic flow facilitated by AV-specific adjustments to car-following and lane-changing behaviors. The results of this study also align with and reinforce findings from prior simulation and modeling efforts in the AV domain. For example, the observed reduction in average travel time (up to 25.5%) and CO\({}_{2}\) emissions (up to 14.6%) at 50% AV penetration is consistent with the efficiency gains reported by [PERSON] et al. (2016), who highlighted improved traffic flow and safety benefits of CAVs at on-ramps. Similarly, the emission improvements observed in our simulation support the environmental impact estimates of mixed traffic flows studied by [PERSON] et al. (2017), who emphasized the importance of CAV ratios in achieving sustainability goals. Additionally, our results mirror trends reported by [PERSON] and [PERSON] (2018), who used deep reinforcement learning to optimize vehicle speed and reduce fuel consumption in smart city environments. By validating these findings through localized, data-driven simulations for Riyadh, our work adds practical context to the global discourse on AV integration and supports region-specific mobility policy development. Our study's methodology leverages the Simulation of Urban Mobility (SUMO) platform, supported by extensive real-world datasets from TomTom and Saudi open data repositories. The statistical significance of these outcomes is validated through paired \(t\)-tests, reinforcing the robustness and reliability of the findings. Nevertheless, limitations remain. SUMO's inherent inability to explicitly simulate wireless communication protocols means that the study relies on behavioral approximations; while these approximations effectively replicate key aspects of coordinated AV behavior, incorporating a full-stack V2X simulation (e.g., OMNeT++ or Eclipse MOSAIC) in future studies could provide deeper insights into the dynamics of real-time AV communication, including potential issues such as network latency and packet loss. Furthermore, this study considered the potential scalability and practical deployment of the HARMONY-AV framework. Given the strategic importance of Prince Mohammed bin Salman Road in Riyadh, these findings provide actionable insights for urban planners and policymakers aiming to integrate AV technology into existing infrastructures. The \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **10\% AV vs. HV** & **30\% AV vs. HV** & **50\% AV vs. HV** \\ \hline Travel Time (s) & 0.017 & 0.004 & 0.001 \\ Stops per Trip & 0.022 & 0.008 & 0.002 \\ Average Speed (km/h) & 0.015 & 0.006 & 0.001 \\ CO\({}_{2}\) Emissions (g/trip) & 0.018 & 0.007 & 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Paired \(t\)-test \(p\)-values comparing AV scenarios to baseline (100% HV). realistic scenarios we tested can suggest a roadmap for gradual AV integration, emphasizing progressive benefits for mobility and emissions reduction. In addition to its local relevance, the outcomes of this study offer valuable insights for other rapidly developing Gulf cities such as Dubai and Doha, which share similar urban growth patterns, car-centric infrastructure, and ambitions for smart mobility transformation. The observed improvements in traffic flow and emission reductions through coordinated AV-HV integration are highly likely to be generalizable in these contexts.