Modeling the Spatial Dynamics of Traffic Congestion Through Street-Level Visual Features: Evidence from Street View Images in Chicago

Traffic congestion continues to challenge urban development, yet most research emphasizes large-scale factors such as road layouts and land use, overlooking localized visual aspects encountered by drivers. This study employs geographically weighted random forest, a non-linear and spatially explicit method, to explore how localized visual features—such as vehicle density, building structures, greenery, and road conditions—impact traffic congestion in Chicago. By integrating transport network dynamics with visual streetscape characteristics, the geographically weighted random forest approach captures spatial heterogeneity and complex interactions more effectively than traditional models. Results demonstrate that incorporating these multi-scale features improves model fit, revealing that greenery mitigates congestion, while dense urban structures and vehicle clusters exacerbate delays. These results highlight the potential of integrating visual characteristics of streetscapes into urban strategies to address congestion more effectively.