Measuring Objective Walkability from Pedestrian-Level Visual Perception Using Machine Learning and GSV in Khulna, Bangladesh

Abstract

Walkability entails measuring the degree of walking activity, a non-motorized mode of active transportation crucial in fast-developing urban settings and combating sedentary lifestyles. While there has been extensive objective research focusing on factors related to the physical environment that influence walkability, there has been a comparatively limited exploration into objectively evaluating a pedestrian’s visual perception. This study in Khulna, Bangladesh, aimed to develop a novel method for objectively measuring walkability based on pedestrian-level visual perception using machine learning. In this research, ResNet, a computer vision model, analyzed 127 panoramic Google Street View images taken at 200-meter intervals from seven major roads. The model, trained with the “deeplabv3plusResnet18CamVid” algorithm, quantified five selected visual features. The results, including walkability rankings, correlation analysis, and spatial mapping, highlighted that greenery and visual enclosures significantly influenced the walkability index. However, the impact of other visual features was less distinctive due to an overall poor streetscape condition. This study bridged the gap between human perception and scientific intelligence, allowing for the evaluation of previously “unmeasurable” streetscape designs. It provides valuable insights for more human-centered planning and transportation strategies, addressing the challenges of modern urbanization and sedentary behavior.