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

Jinming Yan · Jingyi Feng · Jianjun Wang · Yiwen Hu · Xuexin Yan

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Urban Transport and Accessibility Transportation Planning and Optimization Urban Design and Spatial Analysis

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.

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References

2010Travel and the Built Environment 2011Neighborhood Environment and Physical Activity Among Youth 2006Personal, Family, Social, and Environmental Correlates of Active Commuting to School 2018Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo 2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level 2010School site and the potential to walk to school: The impact of street connectivity and traffic exposure in school neighborhoods 2015Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul 2019How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds

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