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, the 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.

• Record URL:
  • https://doi.org/10.1016/j.tbs.2024.100894
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2214367X24001571
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/2214367X
• Supplemental Notes:
  • © 2024 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zou, Dan
  • Li, Qiuping
  • Zhou, Yang
  • Liang, Shen
  • Zhou, Suhong
• Publication Date: 2025-1

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; Maps; References; Tables;
• Pagination: 100894
• Serial:
  • Travel Behaviour and Society
  • Volume: 38
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2214-367X
  • Serial URL: http://www.sciencedirect.com/science/journal/2214367X

Subject/Index Terms

• TRT Terms: Built environment; Leisure time; Shopping trips; Travel behavior
• Geographic Terms: Guangzhou (China)
• Subject Areas: Operations and Traffic Management; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01932660
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 3 2024 5:07PM