Identification and Mechanism of Residents’ Regional Non-Commuting Flow Patterns Based on the Gradient Boosting Decision Tree Model: A Case Study of the Shanghai Metropolitan Area

Yang Cao, Linxing Wang (), Hao Wu, Shuqi Yan and Shuwen Shen
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Yang Cao: College of Construction Engineering, Jiangsu Open University, Nanjing 210019, China
Linxing Wang: College of Construction Engineering, Jiangsu Open University, Nanjing 210019, China
Hao Wu: Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Shuqi Yan: Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Shuwen Shen: Department of Financial, Nanjing Institute of Technology, Nanjing 211167, China

Land, 2023, vol. 12, issue 9, 1-21

Abstract: With the improvement in residents’ living standards, non-commuting has gradually become an important daily transportation behaviour for residents. The intensity of non-commuting flow can reflect the level of urban functional services and external attractiveness and can intuitively characterise the interconnection pattern and relationship among various cities within the metropolitan area. Related research is also a key topic in the fields of urban planning and transportation geography from a humanistic perspective. Taking the Shanghai Metropolitan Area as an example, this study explored the characteristics of the non-commuting flow of residents in the region and between cities and its nonlinear influencing factors with the help of the mobile phone signalling data and the gradient lifting decision tree model. Three conclusions were identified: first, non-commuting flow within each city in the metropolitan area was concentrated in the central urban area, while non-commuting flow between cities was concentrated in the central urban area of the urban border and strong core cities. Second, the built environment had a nonlinear impact on residents’ non-commuting flow. Different types of large-scale service facilities had different impact mechanisms on non-commuting flow, and public service facilities and transportation infrastructure jointly affected residents’ non-commuting flow. Third, transportation facilities had a more significant impact on the non-commuting flow between cities. Large tourism, cultural, and medical service facilities had a more significant impact on non-commuting flow within cities, with upper or lower thresholds according to the type of facility. The planning strategy needs to conduct targeted planning, regulation, and facility configuration based on the area’s actual needs. In addition, this study identified the characteristics of non-commuter flow differentiation in street towns and the nonlinear impact of the built environment.

Keywords: non-commuting flow; nonlinear effects; gradient elevation decision tree model; Shanghai metropolitan area (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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