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Making CAV Deployments Compatible with Complete Streets Objectives for Safe and Efficient Operation
Urban streets are increasingly complex environments where diverse transportation modes like cars, bicycles, pedestrians, micromobility, and emerging automated vehicles, interact within constrained spaces. Traditional design practices, guided by qualitative policies such as Complete Streets, often lack the quantitative foundation needed to evaluate trade-offs between efficiency, equity, and safety. This research introduces a data-driven framework to address these gaps by integrating macroscopic and microscopic modeling approaches for multimodal street design. The study develops two major tools: a Network Fundamental Diagram (NFD)-based optimization framework and a multi-agent simulation platform. The optimization framework applies a two-step process: first, identifying flow-maximizing network attributes at the aggregate level, and second, allocating these attributes to specific links using a Genetic Algorithm (GA) under multiple ethical objectives, including utilitarian, sufficiency, accessibility gap, and maximin principles. The simulation platform models realistic interactions among vehicles, cyclists, pedestrians, and emerging modes, incorporating stochastic demand and behavioral rules to evaluate safety and efficiency.
Making CAV Deployments Compatible with Complete Streets Objectives