Spatial Image-Based Walkability Evaluation Using Regression Model

Abstract

Governments worldwide have invested considerable money and time into creating pedestrian-oriented urban environments. However, generalizing arbitrary standards for walking environments is challenging. Therefore, this study presents a method for predicting walkability scores of evaluations using five regression models, including Multiple linear, Ridge, LASSO regression, SVR, and XGBoost. The models were trained using semantic segmentation, walkability evaluations based on crowdsourcing, and image scores obtained using the TrueSkill algorithm, and their performances were compared. Feature selection was employed to improve the accuracies of the models, which were retrained using the importance of extracted features. Among the five regression models, XGBoost, a tree-based regression model, exhibited the lowest error rate, high accuracy, and greatest performance improvement after retraining. This study is expected to generalize the walking environments preferred by various people and demonstrate that objective walkability evaluations are possible through a computer system rather than through subjective human judgment.