# Harmonized Autonomous-Human Vehicles via Simulation for Emissions Reduction in Riyadh City
## 2 Related Work  
The research landscape on connected and autonomous vehicles (CAVs) and their integration within smart cities has seen substantial progress. [PERSON] et al. [1] highlighted the effect of CAVs on traffic efficiency at on-ramps, while [PERSON] et al. [2] introduced the LUST scenario, a foundational SUMO dataset for vehicular simulation. [PERSON] et al. [3] examined environmental impacts in mixed traffic flows of CAVs and human-driven vehicles. [PERSON] and [PERSON] [4] applied deep reinforcement learning to reduce emissions, showing the promise of AI in real-time control. [PERSON] et al. [5] provided a broad review of sustainability and energy implications for AVs, supporting the long-term planning of sustainable cities. [PERSON] et al. [6] explored deep learning for urban aerial mobility, offering a relevant methodological parallel for urban simulation studies. [PERSON] and [PERSON] [7] proposed the Sustainable Mobility Guarantee concept, which reframes planning practices to support inclusivity and accessibility. Similarly, [PERSON] et al. [8] discussed innovations in automotive sectors, positioning our focus within broader technological trends. Several studies have utilized SUMO for simulation-based traffic evaluations. [PERSON] et al. [9] developed a co-simulation with AirSim for UTM systems, while [PERSON] et al. [10] built a SUMO-based simulator for emergency vehicle dispatch optimization. [PERSON] et al. [11] and [PERSON] et al. [12] focused on analyzing heterogeneous traffic and driver behavior datasets, respectively. Regarding data sources, [PERSON] [13] introduced TomTomlite for large-scale pattern extraction, while [PERSON] et al. [14] reviewed third-party traffic datasets such as TomTom and HERE Technologies. HERE Technologies is a company that provides a widely used location and mapping platform. HERE offers traffic and mobility datasets similar to TomTom, and their data are commonly used in transportation research, smart city applications, and AV simulations. Recent contributions by [PERSON] et al. [15,16] applied machine learning and collaborative cloud techniques for sustainable mobility and traffic management in Saudi Arabia, particularly in AlKharj. These works are foundational for contextual simulation-based approaches in traffic control, AV integration, and additional emerging urban contexts. Future Internet-oriented studies such as [PERSON] et al. [17] have surveyed AV scenario generation, while [PERSON] et al. [18] proposed fuzzy-based traffic flow control for urban networks. In addition, [PERSON] et al. [19] proposed a crowd-sensing platform for emission monitoring, demonstrating the fusion of IoT and urban mobility analytics.  
In contrast to these works, our contribution lies in simulating AV communication and coordination mechanisms via SUMO using real urban topology and traffic data from AlKharj (Figure 1). We incorporate TraCI-based logic to emulate V2X effects and test coordinated AV behavior using localized TomTom-based scenarios, a combination that has not previously been presented. Moreover, our focus on sustainability-aware simulations using accessible tools aligns strongly with the Future Internet vision.