Preliminary communication
https://doi.org/10.7307/ptt.v30i2.2388
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
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.
Keywords
parking guidance system; remaining parking space; time series method; short-term forecasting method; neural network
Hrčak ID:
200063
URI
Publication date:
20.4.2018.
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