The Non-Linear Influence of Built Environment on the School Commuting Metro Ridership: The Case in Wuhan, China

Abstract

Although many studies have investigated the non-linear relationship between the built environment and rail patronage, it remains unclear whether this influence is equally applicable to primary and secondary school students due to their physiological characteristics and cognitive limitations. This study applies the GBDT model to Wuhan student metro swipe data in order to investigate the relative importance and non-linear association of the built environment on the school-commuting metro ridership. The results show that the variable with the greatest predictive power is the number of living service facilities followed by the number of intersections, and the degree of land-use mixture. All of the built environment variables had non-linear associations with the school-commuting ridership, and the greatest attraction to the school-commuting metro ridership occurred when the number of living service facilities was 500, the number of intersections was 36, and the degree of land-use mixture was 0.8. These findings can help planners to prioritize land-use optimization and the effective range of land-use indicators when developing child-friendly rail transport policies.

Nonlinear Hierarchical Effects of Housing Prices and Built Environment Based on Multiscale Life Circle—A Case Study of Chengdu

“…Its goal is to simulate real values through the minimum loss function, iterate multiple times, and reduce the prediction error. Compared to ordinary regression models, GBDT has two advantages [18]: (1) it does not demand normality of data and can accommodate variables with missing values; (2) it solves the problem of multicollinearity within the data serves to avert the intercorrelation among the built environment indicators.…”

“…From the perspective of the urban built environment, with the massive generation of urban crowdsourcing data, research based on the built environment within the life circle has gradually become enriched. Its content has expanded from traditional planning data such as land use type [17,18] to the functional properties represented by points of interest (POI) [19,20], traffic network data [21,22], pedestrian vitality data based on location-based services (LBSs) [23,24], blue-green space areas [25], etc. With the increased popularity of various social media application, "checking in" at scenic spots and places of consumption has become a trend among young people, which has yielded a new type of open source data with a strong tendency toward consumption behavior, such as from Weibo, Xiaohongshu, Meituan, etc.…”

“…The rapid development of artificial intelligence (AI) has led to the application of neural networks (NNs) in the field of spatial information mining, which has led to a new method suitable for analyzing, mining, and extracting crowdsourcing data, i.e., machine learning (ML). Machine learning has matured in geoscience research, and the gradient boosting decision tree (GBDT) algorithm has shown excellent applicability for the information mining of urban crowdsourcing data and nonlinear relationships in the living environment [18,50]. On the one hand, its parameter variability can overcome the difficulty in fitting the hedonic model under multimodality [22,51].…”