Version 1
: Received: 10 April 2024 / Approved: 10 April 2024 / Online: 11 April 2024 (14:10:15 CEST)
How to cite:
Yin, J.; Zhao, F.; Tang, W.; Ma, J. The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand. Preprints2024, 2024040763. https://doi.org/10.20944/preprints202404.0763.v1
Yin, J.; Zhao, F.; Tang, W.; Ma, J. The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand. Preprints 2024, 2024040763. https://doi.org/10.20944/preprints202404.0763.v1
Yin, J.; Zhao, F.; Tang, W.; Ma, J. The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand. Preprints2024, 2024040763. https://doi.org/10.20944/preprints202404.0763.v1
APA Style
Yin, J., Zhao, F., Tang, W., & Ma, J. (2024). The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand. Preprints. https://doi.org/10.20944/preprints202404.0763.v1
Chicago/Turabian Style
Yin, J., Wenyun Tang and Jianxiao Ma. 2024 "The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand" Preprints. https://doi.org/10.20944/preprints202404.0763.v1
Abstract
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.
Keywords
ride-hailing; built environment; XGBoost; SHAP; nonlinearity
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
