# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 10 Case Studies and Real-World Implementations  
Case studies and real-world implementations of urban autonomous vehicle (AV) shuttles provide valuable insights into the challenges and advancements in this field. [PERSON] et al. [313] shared lessons from setting up automated shuttle operations in Brussels, Linkoping, and Turin, emphasizing the need for shuttles to adapt to existing traffic environments without extensive infrastructure modifications. [PERSON] and [PERSON] [314] investigated the use of V2X communication to enhance the efficiency of autonomous shuttles in urban traffic, demonstrating the benefits of innovative traffic light control systems. [PERSON] et al. [315] focused on the cybersecurity challenges of Automated City Shuttles (ACSs), performing risk assessments and penetration tests to identify vulnerabilities. Finally, [PERSON] et al. [316] presented an overall architecture and system design for shuttle unmanned ground vehicles, proposing the integration of a cloud brain system to enhance operational safety and performance. Case studies and real-world implementations of autonomous delivery vehicles (ADVs) provide insights into their potential and challenges. A study by [PERSON] et al. [317] introduced an AI-assisted autonomous delivery robot system designed for small urban communities, emphasizing end-to-end automation and optimization for safe last-mile deliveries. This system was tested in real-world trials on a university campus, showcasing its utility in handling operational uncertainties and client schedules. [PERSON] et al. [318] detailed the design and implementation of an autonomous driving delivery robot equipped with advanced sensor technologies and software frameworks, highlighting its effective path planning and obstacle detection capabilities in various outdoor scenarios. An analysis by [PERSON] et al. [319] explored the impact of autonomous vehicle-assisted deliveries in urban to rural settings, finding that such systems significantly reduce delivery completion times and provide cost-effective solutions, particularly in urban environments. These studies collectively emphasize the importance of robust designs, extensive testing, and the integration of advanced technologies to enhance the efficiency and safety of ADVs. Case studies and real-world implementations of autonomous vehicle (AV) deployment in smart cities provide valuable insights into the potential and challenges of integrating AVs into urban environments. The Vehicle as a Service (VaaS) paradigm proposed by researchers leverages vehicles equipped with sensing, computing, and communication capabilities to build cost-effective service networks in smart cities, highlighting potential use cases and necessary upgrades for traditional vehicular networks [320]. Another study focuses on the design and optimization of solar-powered shared electric AV systems for smart cities, demonstrating significant improvements in operational range and utility through optimized charging station locations and energy management strategies [321]. A pre-deployment testing study of low-speed urban road autonomous driving in Columbus emphasized the importance of extensive lab and controlled environment testing before public road deployment to ensure safety and reliability [322]. The Living Lab for Autonomous Driving in Modena, Italy, showcased applied research in mobility as a service (MaaS) model, contributing to sustainable urban planning [323]. Lastly, a study on AV adoption in Indian smart cities identified high willingness among residents, particularly young adults, to embrace AV technology, suggesting significant potential for reducing accidents and transforming urban mobility [324]. Case studies and real-world implementations of autonomous vehicle (AV) testing and pilots offer valuable insights into the complexities and advancements in this field. The Sohjoa Baltic project ([PERSON] et al.