Multimodal Data-Driven Hourly Dynamic Assessment of Walkability on Urban Streets and Exploration of Regulatory Mechanisms for Diurnal Changes: A Case Study of Wuhan City

Xingyao Wang, Ziyi Peng and Xue Yang ()
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Xingyao Wang: School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Ziyi Peng: School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Xue Yang: School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China

Land, 2025, vol. 14, issue 8, 1-30

Abstract: The use of multimodal data can effectively compensate for the lack of temporal resolution in streetscape imagery-based studies and achieve hourly refinement in the study of street walkability dynamics. Exploring the 24 h dynamic pattern of urban street walkability and its diurnal variation characteristics is a crucial step in understanding and responding to the accelerated urban metabolism. Aiming at the shortcomings of existing studies, which are mostly limited to static assessment or only at coarse time scales, this study integrates multimodal data such as streetscape images, remote sensing images of nighttime lights, and text-described crowd activity information and introduces a novel approach to enhance the simulation of pedestrian perception through a visual–textual multimodal deep learning model. A baseline model for dynamic assessment of walkability with street as a spatial unit and hour as a time granularity is generated. In order to deeply explore the dynamic regulation mechanism of street walkability under the influence of diurnal shift, the 24 h dynamic score of walkability is calculated, and the quantification system of walkability diurnal change characteristics is further proposed. The results of spatio-temporal cluster analysis and quantitative calculations show that the intensity of economic activities and pedestrian experience significantly shape the diurnal pattern of walkability, e.g., urban high-energy areas (e.g., along the riverside) show unique nocturnal activity characteristics and abnormal recovery speeds during the dawn transition. This study fills the gap in the study of hourly street dynamics at the micro-scale, and its multimodal assessment framework and dynamic quantitative index system provide important references for future urban spatial dynamics planning.

Keywords: walkability; multimodal data fusion; fine temporal granularity; diurnal change characteristics; deep learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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