Investigating the Nonlinear Effect of Land Use and Built Environment on Public Transportation Choice Using a Machine Learning Approach

Zhenbao Wang, Shuyue Liu, Haitao Lian and Xinyi Chen ()
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Zhenbao Wang: School of Architecture and Art, Hebei University of Engineering, Handan 056038, China
Shuyue Liu: School of Architecture and Art, Hebei University of Engineering, Handan 056038, China
Haitao Lian: School of Architecture and Art, Hebei University of Engineering, Handan 056038, China
Xinyi Chen: School of Architecture, Tianjin University, Tianjin 300072, China

Land, 2024, vol. 13, issue 8, 1-16

Abstract: Understanding the relationship between the demand for public transportation and land use is critical to promoting public-transportation-oriented urban development. Taking Beijing as an example, we took the Public Transportation Index (PTI) during the working day’s early peak hours as the dependent variable. And 15 land use and built environment variables were selected as the independent variables according to the “7D” built environment dimensions. According to the Modifiable Areal Unit Problem (MAUP), the size and shape of the spatial units will affect the aggregation results of the dependent variable and the independent variables. To find the ideal spatial unit division method, we assess how well the nonlinear model fits several spatial units. Extreme Gradient Boosting (XGBoost) was utilized to investigate the nonlinear effects of the built environment on PTI and threshold effects based on the ideal spatial unit. The results show that (1) the best spatial unit division method is based on traffic analysis zones (TAZs); (2) the top four explanatory variables affecting PTI are, in order: mean travel distance, residential density, subway station density, and public services density; (3) there are nonlinear relationships and significant threshold effects between the land use variables and PTI. The priority regeneration TAZs were identified according to the intersection analysis of the low PTI TAZs set and the PTI-sensitive TAZs set based on different land use variables. Prioritized urban regeneration TAZs require targeted strategies, and the results of the study may provide a scientific basis for proposing strategies to renew land use to increase PTI.

Keywords: land use; built environment; Public Transportation Index; Extreme Gradient Boosting (XGBoost); Modifiable Areal Unit Problem (MAUP); explainable machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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