Neural-Network-Based Dynamic Distribution Model of Parking Space Under Sharing and Non-Sharing Modes

Ziyao Zhao, Yi Zhang, Yi Zhang, Kaifeng Ji and He Qi
Additional contact information
Ziyao Zhao: Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China
Yi Zhang: Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China
Yi Zhang: Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China
Kaifeng Ji: Industrial Engineering department, Tsinghua University, Beijing 100084, China
He Qi: China Construction Science and Technology Co. Ltd., Shenzhen 510152, China

Sustainability, 2020, vol. 12, issue 12, 1-27

Abstract: In recent years, with the rapid development of China’s automobile industry, the number of vehicles in China has been increasing steadily. Vehicles represent a convenient mode of travel, but the growth rate of the number of urban motor vehicles far exceeds the construction rate of parking facilities. The continuous improvement of parking allocation methods has always been key for ensuring sustainable city management. Thus, developing an efficient and dynamic parking distribution algorithm will be an important breakthrough to alleviate the urban parking shortage problem. However, the existing parking distribution models do not adequately consider the influence of real-time changes in parking demand and supply on parking space assignment. Therefore, this study proposed a method for dynamic parking allocation using parking demand predictions and a predictive control method. A neural-network-based dynamic parking distribution model was developed considering seven influencing factors: driving duration, walking distance, parking fee, traffic congestion, possibility of finding a parking space in the target parking lot and adjacent parking lot, and parking satisfaction degree. Considering whether the parking spaces in the targeted parking lots are shared or not, two allocation modes—sharing mode and non-sharing mode—were proposed and embedded into the model. At the experimental stage, a simulation case and a real-time case were performed to evaluate the developed models. The experimental results show that the dynamic parking distribution model based on neural networks can not only allocate parking spaces in real time but also improve the utilisation rate of different types of parking spaces. The performance score of the dynamic parking distribution model for a time interval of 2–20 min was maintained above 80%. In addition, the distribution performance of the sharing mode was better than that of the non-sharing mode and contributed to a better overall effectiveness. This model can effectively improve the utilisation rate of resources and the uniformity of distribution and can reduce the failure rate of parking; thus, it significantly contributes to more smart and sustainable urban parking management.

Keywords: dynamic distribution model; neural network; parking demand prediction; predictive control; utility function; sharing distribution mode (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/12/4864/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/12/4864/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:12:p:4864-:d:371585

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().