Non-Linear Impacts of Built Environments with Parking Facility Provision on Commuting Mode Choices
Bibliographic Data
| ID | 13112978 |
|---|---|
| Authors | Weijia Li (0000-0003-4189-2140, Chang'an University, corresponding author), Xingyu Ma (0000-0001-5268-5169, Chang'an University), Xinge Ji (Chang'an University), Yan Zheng (0009-0004-7882-2247, Chang'an University), Qiang Li (0000-0003-2737-0768, Northwest University), Binfeng Tuo (Northwest University) |
| Year | 2026 |
| Volume | 10 |
| Issue | 1 |
| Pages | 17-17 |
| Publication date | 2026-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Urban Science (JOURNAL) |
| Journal identifiers | ISSN: 2413-8851 • E-ISSN: 2413-8851 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/urbansci10010017 |
| OpenAlex | W7117974220 |
| Language | EN |
| References cited | 52 |
Despite the critical role of parking supply in urban transportation, the nonlinear relationship between parking facilities and commute mode choice remains poorly understood. This study systematically examines the nonlinear influences of the built environment, with a focus on parking facilities, on commuting mode choice using 2019 survey data from Xi’an. A Gradient Boosting Decision Tree (GBDT) model combined with Accumulated Local Effects (ALE) analysis was applied to capture complex relationships. The parking-related variables encompass factors such as parking fees, distance to the nearest parking lot, number of parking spaces, and parking density. Key findings indicate that car ownership, gender, land use mix-work, and distance to CBD-work, distance to CBD-home, and number of parking spaces-home at home are significant predictors. Notably, the number of parking spaces proved more influential than parking density. A positive correlation was observed between parking supply at workplaces and car usage, with a sharp increase in the probability of car ownership when supply exceeds 2800 spaces/km2. Similarly, a threshold of 7500 spaces/km2 around residences significantly promotes car dependence. The results underscore the importance of incorporating nonlinear parking supply effects into travel demand forecasting and provide insights for developing targeted parking management policies
Boosting (machine learning · Decision tree · Gradient boosting · Key (lock · Land use · Mode (computer interface · Mode choice · Parking guidance and information · Trip generation · Elevator Systems and Control · Smart Parking Systems Research · Urban Transport and Accessibility
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Does residential parking supply affect household car ownership? The case of New York City
Influences of LRT on travel behaviour
Travel and the Built Environment
| Citation velocity | historical |
|---|---|
| Highly cited | No |