Spatial Image-Based Walkability Evaluation Using Regression Model

Author:

Hwang Jiyeon1ORCID,Nam Kwangwoo1ORCID,Lee Changwoo1

Affiliation:

1. Department of Computer Information Engineering, Kunsan National University, Gunsan 54150, Republic of Korea

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.

Funder

Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport

Publisher

MDPI AG

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