A Feature-Driven Risk Allocation Modeling Methodology for Transport Infrastructure Public–Private Partnership Projects

Abstract Effective risk allocation is essential for the success of transport infrastructure public–private partnership (TI-PPP) projects. However, inherent features of these projects, including multiple entities, multiple tasks, and dynamicity, complicate the generation of efficient risk allocation solutions. This study develops a feature-driven modeling methodology for dynamic risk allocation involving multiple entities and full task coverage. A dedicated risk allocation protocol is designed, consisting of four modules of risk identification, scenario assumption, model construction, and solution visualization. Using proposed assumptions and multisource data, a multistage dynamic tripartite model is proposed that combines rule definition, algorithm design, and Shapley value method extension to allocate the identified risks. The generated solutions are visualized through a risk allocation matrix. Model applicability is demonstrated via a case study, with multifaceted validations confirming its effectiveness, reliability, and robustness. The results enable entities to intuitively clarify their respective undertaken risks and allocation proportions at any time, while also providing case-based management implications. Theoretically, this study offers a comprehensive paradigm spanning methodology development to validation for risk allocation research. Practically, it delivers operational guidance and policy references for risk allocation practices.