Real Time Short-term Forecasting Method of Remaining Parking Space in Urban Parking Guidance Systems

  • Xiaobo Zhu The Key Laboratory of Road and Traffic Engineering (Ministry of Education), School of Transportation Engineering, Tongji University； Intelligent Transportation System Research Center, Southeast University
  • Jianhua Guo Intelligent Transportation System Research Center, Southeast University
  • Wei Huang Intelligent Transportation System Research Center, Southeast University
  • Fengquan Yu Intelligent Transportation System Research Center, Southeast University
  • Byungkyu Brian Park Department of Civil and Environmental Engineering, University of Virginia
DOI: https://doi.org/10.7307/ptt.v30i2.2388
Keywords: parking guidance system, remaining parking space, time series method, short-term forecasting method, neural network,

Abstract

Short-term forecasting of the remaining parking space is important for urban parking guidance systems (PGS). The previous methods like polynomial equations and neural network methods are difficult to be applied in practice because of low accuracy or lengthy initial training time which is unfavourable if real-time training is carried out on adapting to changing traffic conditions. To forecast the remaining parking space in real-time with higher accuracy and improve the performances of PGS, this study develops an online forecasting model based on a time series method. By analysing the characteristics of data collected in Nanjing, China, an autoregressive integrated moving average (ARIMA) model has been established and a real-time forecasting procedure developed. The performance of this proposed model has been further analysed and compared with the performances of a neural network method and the Markov chain method. The results indicate that the mean error of the proposed model is about 2 vehicles per 15 minutes, which can meet the requirements for general PGS. Furthermore, this method outperforms the neural network model and the Markov chain method both in individual and collective error analysis. In summary, the proposed online forecasting method appears to be promising for forecasting the remaining parking space in supporting the PGS.

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Xiaobo Zhu, The Key Laboratory of Road and Traffic Engineering (Ministry of Education), School of Transportation Engineering, Tongji University； Intelligent Transportation System Research Center, Southeast University

Xiaobo Zhu is a PHD student in School of Transportation Engineering, Tongji University (Shanghai, China). He received the M.S. degree from Southeast University. His research interests are in the areas of traffic flow theory of urban road network, intelligent transportation system and traffic control and management.

Jianhua Guo, Intelligent Transportation System Research Center, Southeast University
Dr. Guo, Jianhua is a professor in Transportation Engineering at the Intelligent Transportation System Research Center of the Southeast University, Nanjing, Jiangsu Province, P.R. China.  His major research fields include intelligent transportation system applications, traffic management and control, statistical time series analysis, and discrete choice modeling.  Prior to joining Southeast University, he worked in University of Virginia and Federal Highway Administration.  He received research funds from the Chinese Natural Science Foundation, Ministry of Science and Technology of China, and various provincial agencies.  He serves as reviewer for Chinese National Science Foundation and international journals. He received the Outstanding Reviewer Award for ASCE Journal of Transportation Engineering of the year 2012, and the Outstanding Reviewer Award for ASCE Journal of Computing in Civil Engineering of the years 2012, 2013, and 2014, consecutively.
Wei Huang, Intelligent Transportation System Research Center, Southeast University
Wei Huang is a distinguished professor in Civil Engineer at the Intelligent Transportation System Research Center of the Southeast University, Nanjing, Jiangsu Province, P.R. China.  He is a member of Chinese Academy of Engineering.  He enjoys the State Council special allowance and receives supports from the New Century Talent Program, the National Outstanding Mid-aged Experts Program, the National Talents Engineering Program, and the Yangtze Scholar Program from various agencies and organizations.  He is one of the forerunners in the research fields of long span steel bridge pavement and intelligent transportation systems of China. As the leading awardee, he receives 26 awards from both the national and provincial level.  He published 13 books.
Fengquan Yu, Intelligent Transportation System Research Center, Southeast University
Fengquan Yu is a Ph.D. student in School of Transportation, Southeast University (Nanjing, China). He received the M.S. degree from Southeast University. His research interests are intelligent transportation system and traffic data analysis and modeling.
Byungkyu Brian Park, Department of Civil and Environmental Engineering, University of Virginia

Brian Park is an Associate Professor of Civil and Environmental Engineering Department at the University of Virginia.Prior to joining the University of Virginia, he was a Research Fellow at the National Institute of Statistical Sciences and a Post-Doctoral Research Associate at North Carolina State University. Dr. Park received the B.S. and the M.S. from the Hanyang University and the Ph.D. from the Texas A&M University.  His research interests are in the areas of traffic control and management, intelligent transportation system and conneted vehicle technology. He has authored about 108 technical publications.


Dr. Park is a recipient of PTV America Best Paper Award, Outstanding Reviewer Award from the American Society of Civil Engineers, Jack H. Dillard Outstanding Paper Award from the Virginia Transportation Research Council and Charley V. Wootan Award (for best Ph.D. dissertation) from the Council of University Transportation Centers. He is an ASCE ExCEEd teaching fellow.


Dr. Park is an Editor in Chief of the International Journal of Transportation, an Associate Editor of the American Society of Civil Engineers Journal of Transportation Engineering, Journal of Intelligent Transportation Systems and the KSCE Journal of Civil Engineering, and an editorial board member of the International Journal of Sustainable Transportation. Furthermore, he is a member of TRB (a division of the National Academies) vehicle highway automation committee and statistical methods committee, and chair of the simulation subcommittee of the traffic signal systems.

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Published
2018-04-20
How to Cite
1.
Zhu X, Guo J, Huang W, Yu F, Park BB. Real Time Short-term Forecasting Method of Remaining Parking Space in Urban Parking Guidance Systems. Promet [Internet]. 2018Apr.20 [cited 2024Apr.20];30(2):173-85. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/2388
Issue
Vol 30 No 2 (2018)
Section
Articles

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