Predicting and explaining parking space sharing behaviors using LightGBM and SHAP with individual heterogeneity considered-Reference-Cited by-同舟云学术

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Predicting and explaining parking space sharing behaviors using LightGBM and SHAP with individual heterogeneity considered

Published:2024-09-12 Issue: Volume: Page:1-14
ISSN:1942-7867
Container-title:Transportation Letters
language:en
Short-container-title:Transportation Letters

Author:

Wang Ange¹²,Wang Jiyao¹,Wen Xiao¹,He Dengbo¹²³^ORCID,Tu Ran⁴

Affiliation:

1. Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

2. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China

3. HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Shenzhen, Futian, China

4. School of Transportation, Southeast University, Nanjing, China

Funder

Guangzhou Municipal Science and Technology Project

Guangdong Provincial Key Lab of Integrated Communication, Sensing and Computation for Ubiquitous Internet of Things

Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19427867.2024.2392332

Reference42 articles.

1. Measuring willingness to pay for shared parking

2. A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.

3. Chase R. 2015. Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism. New York: Public Affairs.

4. Predicting lane-changing risk level based on vehicles’ space-series features: A pre-emptive learning approach

5. Bi-level programming model for resource-shared parking lots allocation

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