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Connected and Autonomous Vehicles (CAVs) are reshaping urban systems, demanding substantial computational support. While existing research emphasizes the significance of establishing physical and virtual infrastructure to facilitate CAV integration, a comprehensive framework for designing CAV-related infrastructure principles remains largely absent. This paper introduces a holistic framework that addresses gaps in current literature by presenting principles for the design of CAV-related infrastructure. We identify diverse urban infrastructure types crucial for CAVs, each characterized by intricate considerations. Deriving from existing literature, we introduce five principles to guide investments in physical infrastructure, complemented by four principles specific to virtual infrastructure. These principles are expected to evolve with CAV development and associated technology advancements. Furthermore, we exemplify the application of these principles through a case study in Oxford, UK. In doing so, we assess urban conditions, identify representative streets, and craft CAV-related urban infrastructure tailored to distinct street characteristics. This framework stands as a valuable reference for cities worldwide as they prepare for the increasing adoption of CAVs. [ABSTRACT FROM AUTHOR]

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide. [ABSTRACT FROM AUTHOR]