Spatial-Temporal Heterogeneity and Built Environment Nonlinearity in Inconsiderate Parking of Dockless Bike-Sharing

Wang, Yacan; Li, Jingjing; Su, Duan; Zhou, Huiyu

doi:10.2139/ssrn.4305474

Cited by 1 publication

(1 citation statement)

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“…As a microscopic manifestation of urban spatial form, the built environment significantly influences travel pa erns [5][6][7]. A multitude of scholars have scrutinized the nexus between the built environment and traffic travel, and studied different models, including metro [8,9], taxi [10][11][12] and dockless bike-sharing [13,14]. As a rising mode of travel, ride-hailing is gradually affecting the change in residents' travel demand.…”

Section: Introductionmentioning

confidence: 99%

The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand

Yin,

Zhao,

Tang

et al. 2024

Preprint

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While numerous studies have explored the correlation between the built environment and ride-hailing demand, few have assessed their nonlinear interplay. Utilizing ride-hailing order data and multi-source built environment data from Nanjing, China, this paper uses the machine learning method, eXtreme Gradient Boosting (XGBoost), combined with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) to investigate the impact of built environment factors on ride-hailing travel demand, including their non-linear and threshold effects. The findings reveal that dining facilities have the most significant impact, with a contribution rate of 30.75%, on predicting ride-hailing travel demand. Additionally, financial, corporate, and medical facilities also exert considerable influence. The built environment factors need to reach a certain threshold or within a certain range to maximize the impact of ride-hailing travel demand. population density, land use mix and distance to the subway station collectively influence ride-hailing demand. The results are helpful for TNCs to allocate network ride-hailing resources reasonably and effectively.

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