An ODD-Stratified Multi-Criteria Framework for Assessing Urban Territorial Readiness for Connected and Automated Vehicles: Methodology and Proof-of-Concept Demonstration

Abstract

Existing readiness indices for Connected and Automated Vehicles (CAVs) produce a single composite score per city. This conflates use cases with very different territorial requirements and, being compensatory, allows strong performance on one criterion to mask a deficiency that is, in practice, a hard prerequisite. This paper proposes and demonstrates an assessment framework with four elements. First, the verdict is stratified by Operational Design Domain (ODD) profile, so that one territory yields one verdict per use case. Second, each profile is assessed only on its eligible service area, the roads on which the corresponding service could actually operate. Third, a non-compensatory veto layer is overlaid on a compensatory segment-level score, which separates the readiness score from the fit share, the proportion of network length that satisfies every critical threshold. Fourth, the diagnosis is extended to specific corridors through a readiness-aware routing formulation. The framework is demonstrated on the Versailles, Satory, and Velizy perimeter of about 92 square kilometes, using a real OpenStreetMap network of 4834 nodes and 733 kilometres, combined with an explicitly illustrative synthetic indicator calibration. Restricting each profile to its service area changes one verdict outright. The campus shuttle and urban robotaxi profiles obtain near-identical scores, both close to 0.51, but fit shares differing seven-fold, a distinction that a single score cannot express. Readiness-aware routing reroutes up to 42 percent of origin and destination pairs onto better-equipped itineraries for about 1 percent additional travel length. Monte Carlo analysis and cross-method tests using TOPSIS and PROMETHEE-II leave all verdicts unchanged. We position the work as a methodological demonstration rather than a validated deployment tool: the indicator values are synthetic, and empirical calibration, expert weight elicitation, and field validation remain prerequisites for operational use.