How Does the Built Environment Affect Mechanical Parking Space Planning: A Case Study in Xi’an City
Source abstract
Abstract
Mechanical parking lots and spaces are known as the “energy saver” of urban space because of their small footprint, high efficiency, and environmental protection. However, the location and number of mechanical parking lots and space planning have become an important part of effectively exerting the function of mechanical parking lots. In order to explore the planning problem of mechanical parking lots, this study used the gradient boosting decision tree–Shapley additive explanations (GBDT-SHAPs) to measure the non-linear impact of the urban built environment on the mechanical parking spaces ratio and extract the optimal threshold of key variables. The results show that land use mix and distance to Bell Tower (CBD) are two key variables affecting mechanical parking space planning, and both have a non-linear relationship with the built environment. The threshold values are 0.83 and 7 km. The results will provide urban and transport planners with strategies for planning mechanical parking lots and spaces.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Parking Systems Research · Urban Transport and Accessibility · Urban and Freight Transport Logistics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 200092, China
School of Architecture, Chang’an University, Xi’an 710064, China
Engineering Research Center of Collaborative Planning of Low-Carbon Urban Space and Transportation, Universities of Shaanxi Province, Xi’an 710061, China
Funding
Education Department of Shaanxi Provincial Government
References (42)
Show source-provided references
1. Examining the relationship between built environment and urban parking demand from the perspective of travelers10.1016/j.jclepro.2022.135766 ↗
2. Cruising for parking around a circle10.1016/j.trb.2017.07.009 ↗
3. A survey on smart parking systems in urban cities10.1002/cpe.6511 ↗
4. Smart Parking Architecture based on Multi Agent System
5. Rocco, G., Pipino, C., and Pagano, C. (2023). An Overview of Urban Mobility: Revolutionizing with Innovative Smart Parking Systems. Sustainability, 15.10.3390/su151713174 ↗
6. Zong, F., Zeng, M., and Yu, P. (2023). Transportation, Springer.
7. Zhang, Y.N., Huang, Q.B., and Cui, Y.Z. (2009, January 26–27). Modeling and Simulation of Virtual Prototype of the Lift-Sliding Mechanical Parking Equipment Based on Solid Works. Proceedings of the International Conference on Modeling, Simulation and Optimization, Beijing, China.
8. Probability distribution and recommended values of passenger car external dimensions in mechanical parking garage design10.1080/13467581.2021.1971995 ↗
9. Analysis of parking cruising behaviour and parking location choice10.1080/03081060.2020.1805545 ↗
10. Xu, B., Yu, B., and Tao, Z. (2013, January 16–17). Strength design of a mechanical parking system. Proceedings of the International Conference on Mechanical Structures and Smart Materials (ICMSSM 2013), Xiamen, China.
11. Parking Trajectory Planning Using Multiresolution State Roadmaps10.1109/TIV.2017.2769882 ↗
12. Amari, A., Moussaid, L., and Tallal, S. (2023). New Parking Lot Selection Approach Based on the Multi-Criteria Decision Making (MCDM) Methods: Health Criteria. Sustainability, 15.10.3390/su15020938 ↗
13. Travel and the Built Environment10.1080/01944361003766766 ↗
14. Wang, Z., Song, J., Zhang, Y., Li, S., Jia, J., and Song, C. (2022). Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments. Sustainability, 14.10.3390/su142316314 ↗
15. Examining the relationship between built environment and metro ridership at station-to-station level10.1016/j.trd.2020.102332 ↗
16. Yin, C.Y., Shao, C.F., and Wang, X.Q. (2018). Built Environment and Parking Availability: Impacts on Car Ownership and Use. Sustainability, 10.10.3390/su10072285 ↗
17. Modeling heterogeneous parking choice behavior on university campuses10.1080/03081060.2018.1407518 ↗
18. Parking facilities and the built environment: Impacts on travel behaviour10.1016/j.tra.2016.10.025 ↗
19. Analyzing parking sentiment and its relationship to parking supply and the built environment using online reviews10.1007/s42421-021-00036-1 ↗
20. Determining Parking Demand and Locating Parking Areas Using Geographic Analytics Methods10.1061/(ASCE)UP.1943-5444.0000650 ↗
21. How does parking interplay with the built environment and affect automobile commuting in high-density cities? A case study in China10.1177/0042098016667040 ↗
22. Zeng, C., Zhou, X., and Yu, L. (2023). Parking Generating Rate Prediction Method Based on Grey Correlation Analysis and SSA-GRNN. Sustainability, 15.10.3390/su151713016 ↗
23. Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm10.1155/2022/2529912 ↗
24. Lee, Y.W. (2015). Establishment of an on-street parking demand estimation model through a survey on parking during day and night. Asia Life Sci., 471–484.10.14257/astl.2015.100.10 ↗
25. Optimization design and evaluation of parking route based on automatic assignment mechanism of parking lot10.1177/1687814017712416 ↗
26. Prediction model for drivers’ tendency to perpetrate a double parking violation on urban trips10.1016/j.tranpol.2023.08.001 ↗
27. A user equilibrium, traffic assignment model of network route and parking lot choice, with search circuits and cruising flows10.1016/j.trc.2014.07.014 ↗
28. Wang, T., Li, S., and Li, W. (2023). A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information. Sustainability, 15.10.3390/su15097096 ↗
29. Nonlinear Model of Impact of Built Environment on Urban Parking Demand
30. Parking policy: The effects of residential minimum parking requirements in Seattle10.1016/j.landusepol.2019.104053 ↗
31. Liu, Q., Zhu, M.J., and Xiao, Z.P. (2019). Workplace Parking Provision and Built Environments: Improving Context-Specific Parking Standards Towards Sustainable Transport. Sustainability, 11.10.3390/su11041142 ↗
32. Ma, X.W., and Yin, Y. (2022). Short-Term Prediction of Bike-Sharing Demand Using Multi-Source Data: A Spatial-Temporal Graph Attentional LSTM Approach. Appl. Sci., 12.10.3390/app12031161 ↗
33. Liu, K.L., Chen, J., and Li, R. (2022). Nonlinear Effects of Community Built Environment on Car Usage Behavior: A Machine Learning Approach. Sustainability, 14.10.3390/su14116722 ↗
34. Liu, C.M., Wang, Y., and Li, W. (2024). An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks. Appl. Sci., 14.10.3390/app14052108 ↗
35. Nonlinear effect of accessibility on car ownership in Beijing: Pedestrian-scale neighborhood planning10.1016/j.trd.2020.102445 ↗
36. The nonlinear effects of multi-scale built environments on CO2 emissions from commuting10.1016/j.trd.2023.103736 ↗
37. Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches10.1109/ACCESS.2020.3025589 ↗
38. How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE10.1111/grow.12587 ↗
39. Visualizing the effects of predictor variables in black box supervised learning models10.1111/rssb.12377 ↗
40. Lee, C., and Lee, S. (2022). Exploring the Contributions by Transportation Features to Urban Economy: An Experiment of a Scalable Tree-Boosting Algorithm with Big Data. Land, 11.10.3390/land11040577 ↗
41. Traffic volume and road network structure: Revealing transportation-related factors on PM2.5 concentrations10.1016/j.trd.2023.103935 ↗
42. Ding, C., Wang, D., Ma, X., and Li, H. (2016). Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees. Sustainability, 8.10.3390/su8111100 ↗