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Title	Evaluating The Quality Of High-Frequency Pedestrian Commuting Streets: A Data-Driven Approach In Shenzhen
ID_Doc	24680
Authors	Guo X.; Hu Y.; Zhang Y.; Yi S.; Tu W.
Year	2025
Published	Smart Cities, 8, 3
DOI	http://dx.doi.org/10.3390/smartcities8030083
Abstract	Highlights: What are the main findings? A mismatch between usage and quality: only 2.15% of high-frequency pedestrian commuting streets in Shenzhen perform well across all three evaluation dimensions, indicating a substantial gap between street quality and usage frequency. Regional disparities in street distribution and quality: Approximately 70% of these streets are concentrated in the northern areas, where the overall quality is relatively poor. In contrast, the southern region has slightly better street quality but still requires improvements. What is the implication of the main finding? These findings highlight the need for targeted urban planning and resource allocation to optimize street environments. By addressing the mismatch between usage frequency and street quality, and by improving the overall quality of high-frequency pedestrian commuting streets, urban experiences can be significantly enhanced. This study provides valuable insights for policymakers and urban planners to prioritize interventions in areas with the highest pedestrian activity and to reduce regional disparities in street quality. Streets, as critical public space nexuses, require synergistic quality–utilization alignment—where quality without use signifies institutional inefficiency, and use without quality denotes operational ineffectiveness. Focusing on high-frequency pedestrian commuting streets (HFPCSs) that not only crucially mediate metropolitan mobility patterns but also shape citizens’ daily urban experiences and satisfaction, this study proposes a data-driven diagnostic framework for street quality–utilization assessment, integrating multi-source urban big data through a case study of Shenzhen. By integrating multi-source urban big data, we identify HFPCSs using LBS data and develop a multi-dimensional evaluation system that incorporates 1.07 million Points of Interest (POIs) for assessing convenience, utilizes DeepLabv3+ for the semantic segmentation of street view imagery to evaluate comfort, and leverages 15,374 km of road network data for accessibility analysis. The results expose dual mismatches: merely 2.15% of HFPCSs achieve balanced comfort–convenience–accessibility benchmarks, while over 70% of these are clustered in northern districts, exhibiting systematically inferior quality metrics across dimensions. Diagnostic analysis reveals specific planning and spatial configurations contributing to these disparities, informing targeted retrofitting strategies for priority street typologies. This approach establishes a replicable model for megacity street renewal, deploying supply–demand diagnostics to synchronize infrastructure upgrades with pedestrian flow realities. By bridging data insights with human-centric urban improvements, this framework demonstrates how smart city technologies can concretely address the quality–utilization paradox—advancing sustainable urbanism through evidence-based street transformations. © 2025 by the authors.
Author Keywords	big data; high-frequency pedestrian commuting streets; human-centric urban design; street quality assessment

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