Data-Driven Pattern Analysis of Attributes Influencing Transportation Construction Program Development

Abstract

Translate Article

There is a growing consensus that transportation construction projects have an influence beyond the provision of reliable infrastructure and significantly impact socioeconomic advancements. This is reflected in recent global infrastructure development initiatives, which encompass diverse objectives related not only to technical aspects but also nontechnical ones. However, it is challenging to synthesize diverse and sometimes competing objectives in program development. Well-established frameworks or formulas supporting the synthesis and delivery of objectives do not always exist. Ex post empirical analysis of program development outcomes, rather than planning documents, is scarce. In this study, the authors applied an explainable machine learning method, specifically, a zero-inflated negative binomial model with two classification parts, supported by a genetic algorithm, the extreme gradient boosting algorithm, and SHapley Addictive explanation values, on the project inventory supported by discretionary transportation programs of Canadian federal governments over an eight-year period. The project records were mapped to the three categories of community attributes corresponding to the three objectives of the Canadian infrastructure initiative: resilient infrastructure; economic growth; and equity promotion. The results reveal a dichotomous reality: Although transportation equity could be a significant factor in rural areas, the communities where most Canadians live host major transportation construction projects because of factors related to transportation assets and local economics. Bridge improvement is a focal theme, and a vibrant local economy is generally favored. Percentages of senior citizens and children might adjust the level of development. It is anticipated that the findings will provide policy decision-makers, transportation asset owners, and community leaders with a realistic understanding of the realities of discretionary transportation program development, thereby informing evidence-based decision-making in the future.