A conceptual framework: BMC-KPI driven methodology for ITS business model formulation and assessment

Purpose This paper aims to examine methods for creating sustainable business models for AI-integrated intelligent transportation systems (ITS), utilizing a BMC-KPI methodology. This paper addresses a significant gap in current ITS modeling by replacing purely qualitative frameworks with explicit quantitative metrics and operational calculation formulas. The primary objective is to align ITS features with the nine components of the BMC, to emphasize the data analytics layer and to establish a systemic interdependency model to evaluate dynamic trade-offs and monitor the effectiveness of proactive, data-driven mobility models. Design/methodology/approach The proposed approach is a quantified BMC-KPI methodology that aligns ITS features with the nine components of the business model canvas (BMC). This methodology explicitly incorporates AI-driven strategic shifts and operational calculation formulas into the BMC blocks, defining ITS through its data analytics layer. The framework also includes a systemic interdependency model to analyze the dynamic trade-offs among strategic objectives. Finally, a closed-loop assessment process is proposed to guide model updates based on performance results. Findings The BMC-KPI methodology enhances the qualitative analysis of the BMC by facilitating the continuous evaluation of its components through a closed-loop framework. Findings illustrate how AI technology transforms traditional building blocks into a data-centric, proactive mobility framework, shifting value from reactive service to the optimization of urban flow. The methodology introduces a systemic interdependency model to evaluate dynamic trade-offs between strategic goals, such as the tension between technological investment (Cost Structure) and societal outcomes (value proposition). This approach demonstrates how AI-driven proactive management effectively bridges the gap between commercial viability and public value within hybrid funding models. Proposed AI-aligned KPIs and operational formulas for each block facilitate the connection between conceptual design and measurable performance, enabling continuous strategy recalibration. Research limitations/implications A primary limitation remains the lack of empirical validation using real-world performance data from actual ITS deployments. While the proposed methodology establishes a closed-loop diagnostic process, its practical effectiveness in guiding strategic pivots when KPI targets are missed requires rigorous field testing. Furthermore, although the framework now incorporates a Systemic Interdependency Model to capture multi-actor friction, future research should further examine how these strategic linkages function within complex public-private ecosystems where commercial and societal objectives often conflict. Future studies should prioritize gathering longitudinal data to calibrate the AI-aligned KPIs and evaluate the proposed decision-making logic for the continuous recalibration of proactive mobility models. Originality/value The practical contribution is assigning precise mathematical formulas to each BMC block, thereby bridging the gap between conceptual logic and operational performance. This advancement enables stakeholders to transition to actionable, measurable strategies tailored to AI-integrated mobility systems. The study concludes by proposing an iterative assessment cycle that utilizes diagnostic feedback to maintain business model sustainability.