HKUST PPOL Newsletter Fall 2023

19 Research Showcase Zhu, P., Li, J., Wang, K., & Huang, J. (2023). Exploring Spatial Heterogeneity in the Impact of Built Environment on Taxi Ridership Using Multiscale Geographically Weighted Regression. Transportation, 1-35. This paper applies multiscale geographically weighted regression (MGWR) to investigate the associations between taxi ridership and spatial contexts to address the effects of spatial heterogeneity in the built environment on taxi passengers’ travel behaviours. The MGWR considerably improves modeling fit compared to the global OLS model by capturing the spatially varying processes at different scales. The results demonstrate the existence of strong spatial nonstationarity in the various built environment factors affecting the spatial distribution of taxi pick-ups and drop-offs. This study reveals the complex relationships between the built environment and the distribution of taxi ridership at different spatial scales and provides valuable insights for transport planning, taxi resource allocation, and urban governance. Wang, K., Chen, Z., Cheng, L., Zhu, P., Shi, J., & Bian, Z. (2023). Integrating Spatial Statistics a nd Machine Learning to Identify Relationships between E-Commerce and Distribution Facilities in Texas, US. Transportation Research Part A: Policy and Practice, 173, 103696. This paper proposes a novel analytical framework that integrates spatial statistics and machine learning techniques to identify relationships between e-commerce and distribution facilities. The framework includes centro-graphic analysis, global and local spatial association measurements, and a recently popularized interpretable machine learning approach – gradient boosting decision trees (GBDT) – to analyze warehousing location choices. The GBDT results show that industrial activities and transportation network accessibility are key factors influencing warehousing location choices. It is also found that the relationship between warehouses and e-commerce establishments is weaker in Houston, a major maritime gateway for goods entering and leaving, compared to Dallas-Fort Worth and Austin. Implications for local freight transportation planners and decision-makers are discussed.