Urban- and Block-Scale Road Network Quality from the Perspective of Walking Needs: Research Progress and Future Directions

Road transport is indispensable to urban functioning. As people-centered development and community-oriented planning have gained prominence, pedestrian environments and road network quality have received increasing attention. Existing reviews, however, generally examine either city-scale networks or street-level environments in isolation, leaving a gap in cross-scale synthesis centered on walking needs. Using Web of Science (WOS) as the data source, this review screened 31,650 records and retained 508 publications; CiteSpace 6.4 was used to map the knowledge structure and major research themes. Drawing on the hierarchy-of-needs framework, walking needs are classified into basic needs for feasibility and safety and higher-order needs for convenience, comfort, and experience, with differences interpreted in relation to walking characteristics. City-scale research is reviewed in terms of road network layout, density, hierarchy, and utility, whereas block-scale research is examined through pedestrian routes and intersections, street frontages, and street furniture. The literature indicates that macro-scale attributes such as connectivity, density, and hierarchical structure affect the feasibility and safety of walking at the city scale. At the block scale, attention has expanded from continuity and safety to the convenience of daily travel, environmental comfort, and heterogeneity in perception. Walking, however, is an individual behavior jointly supported by macro-scale road networks and micro-scale street environments. Current research provides limited integration between these scales, often treats each travel mode or road-user group in isolation, and relies heavily on static data that cannot adequately explain temporal changes in environmental conditions or provide timely feedback on network modifications. Future research should therefore develop cross-scale, multimodal, and dynamic analytical frameworks supported by diverse data sources.