Predicting and explaining parking space sharing behaviors using LightGBM and SHAP with individual heterogeneity considered

Abstract

Shared parking plays a crucial role in alleviating parking pressure, but the heterogeneity of potential suppliers’ intentions was often ignored. This study addresses this gap by adopting an interpretable Machine Learning (ML) framework to investigate parking space sharing intentions, considering individual differences. A survey with 383 respondents from mainland China was conducted, and a Latent Class Model (LCM) identified three distinct groups of potential suppliers. The Light Gradient Boosting Machine (LightGBM), outperforming other ML models, was used to quantify factors influencing sharing behaviors. The SHapley Additive exPlanation (SHAP) approach revealed that influential factors vary across different latent classes. These findings provide insights for shared parking operators to encourage potential suppliers’ participation in shared parking.