Chapter
Jul 2, 2019
A Parking Utilization Prediction Approach Based on NARNN Model
Authors: Peng-Fei Zhao [email protected], Hong-Zhi Guan [email protected], and Ke Zhang [email protected]Author Affiliations
Publication: CICTP 2019
Abstract
The accurate prediction of parking occupancy of urban parking spaces is one of the most critical aspects of urban parking resources management and public travel. In order to accurately depict the time-varying characteristics and grasp the future trend of parking utilization, in this paper, we address the problem of prediction of parking utilization. Firstly, based on the characteristics of the time series of parking utilization, a nonlinear auto-regressive neural network (NARNN) model was developed, along with its training method. Secondly, the proposed model was trained via the empirical parking data involved both weekdays and weekends. Thirdly, based on the proposed benchmark, the effectiveness of the proposed method was analyzed. Finally, the applications and future work for the prediction of parking occupancy were summarized.
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Published In
CICTP 2019
Pages: 4437 - 4448
Copyright
© 2019 American Society of Civil Engineers.
History
Published online: Jul 2, 2019
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Authors
Affiliations
Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing Univ. of Technology, 100 Pingleyuan, Jiaotong Bldg. 507, Chaoyang District, Beijing 100124, China. E-mail: [email protected]
Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing Univ. of Technology, 100 Pingleyuan, Jiaotong Bldg. 619, Chaoyang District, Beijing 100124, China. E-mail: [email protected]
Beijing Municipal Transportation Operations Coordination Center, A9 LiuliqiaonanLi, Fengtai District, Beijing 100161, P.R. China. E-mail: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.