Quantifying the impact of built environment on traffic congestion: A nonlinear analysis and optimization strategy for sustainable urban planning

Abstract

Traffic congestion is a critical issue that must be addressed for sustainable urban development, as it directly impacts residents’ quality of life and the economic vitality of cities. Understanding the mechanisms through which the built environment (BE) influences traffic performance is essential for optimizing the sustainable development of future cities. To this end, we first identified six categories of BE indicators, including road network design, traffic convenience, regional economic level, accessibility, population density, and land use mix. These indicators were then used to establish a comprehensive evaluation framework for characterizing the built environment. Subsequently, a composite traffic congestion status model was developed using clustering techniques, and a nonlinear impact model of composite traffic congestion status was constructed based on the Gradient Boosting Decision Tree (GBDT) method. Finally, we analyzed the nonlinear impact mechanism of built environment characteristics on traffic congestion using Hefei, China as a case study, and proposed regulatory optimization strategies. By strategically optimizing BE factors, traffic congestion within the study area was alleviated to varying degrees. The findings provide valuable insights for urban planners and policymakers to better understand the influence of the built environment on transportation performance, offering guidance for designing more efficient transportation systems and promoting sustainable urban development.