Understanding factors associated with individuals’ non-mandatory activities using machine learning and SHAP interpretation: A case study of Guangzhou, China

Non-mandatory activities (e.g., shopping and leisure) are irregular in space and time, resulting in complex interactions between individuals and urban spaces. Understanding the associated factors of non-mandatory activities is vital for effective urban transport planning and management. This study uses travel survey data from Guangzhou, China, and a random forest (RF) model to investigate non-linear relationships between non-mandatory activities and their associated factors from the perspectives of time, location, built environment, activity dependency, and individual socioeconomic status, on both weekdays and weekends. The contribution of each factor to different non-mandatory activities is examined by a post hoc interpretable method, Shapley Additive exPlanations (SHAP). The results show that activity start time and activity dependency factors have a more significant impact on non-mandatory activities on weekdays, while duration has a greater influence on weekends. Built environment factors like wholesale and retail points of interest (POIs) play a significant role in shopping activities on both weekdays and weekends, while tourism POIs have a greater impact on leisure activities on weekends. Additionally, our analysis reveals the nonlinear dependencies and threshold effects of the top three factors for each category of non-mandatory activities and highlights their disparities between weekdays and weekends.