Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-secti...Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-sections are periodical and self-similar, and the fluctuation of the APSO increases with the decrease in time-sections. Taking the short-time change behavior into account, an APSO forecasting model combined wavelet analysis and a weighted Markov chain is presented. In this model, an original APSO time series is first decomposed by wavelet analysis, and the results include low frequency signals representing the basic trends of APSO and several high frequency signals representing disturbances of the APSO. Then different Markov models are used to forecast the changes of low and high frequency signals, respectively. Finally, integrating the predicted results induces the final forecasted APSO. A case study verifies the applicability of the proposed model. The comparisons between measured and forecasted results show that the model is a competent model and its accuracy relies on real-time update of the APSO database.展开更多
The techniques to forecast available parking space(APS) are indispensable components for parking guidance systems(PGS). According to the data collected in Newcastle upon Tyne, England, the changing characteristics of ...The techniques to forecast available parking space(APS) are indispensable components for parking guidance systems(PGS). According to the data collected in Newcastle upon Tyne, England, the changing characteristics of APS were studied. Thereafter, aiming to build up a multi-step APS forecasting model that provides richer information than a conventional one-step model, the largest Lyapunov exponents(largest LEs) method was introduced into PGS. By experimental tests conducted using the same dataset, its prediction performance was compared with traditional wavelet neural network(WNN) method in both one-step and multi-step processes. Based on the results, a new multi-step forecasting model called WNN-LE method was proposed, where WNN, which enjoys a more accurate performance along with a better learning ability in short-term forecasting, was applied in the early forecast steps while the Lyapunov exponent prediction method in the latter steps precisely reflect the chaotic feature in latter forecast period. The MSE of APS forecasting for one hour time period can be reduced from 83.1 to 27.1(in a parking building with 492 berths) by using largest LEs method instead of WNN and further reduced to 19.0 by conducted the new method.展开更多
基金The National Natural Science Foundation of China(No50738001)the National Basic Research Program of China (973Program) (No2006CB705501)
文摘Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-sections are periodical and self-similar, and the fluctuation of the APSO increases with the decrease in time-sections. Taking the short-time change behavior into account, an APSO forecasting model combined wavelet analysis and a weighted Markov chain is presented. In this model, an original APSO time series is first decomposed by wavelet analysis, and the results include low frequency signals representing the basic trends of APSO and several high frequency signals representing disturbances of the APSO. Then different Markov models are used to forecast the changes of low and high frequency signals, respectively. Finally, integrating the predicted results induces the final forecasted APSO. A case study verifies the applicability of the proposed model. The comparisons between measured and forecasted results show that the model is a competent model and its accuracy relies on real-time update of the APSO database.
基金Project(2012CB725402)supported by the National Key Basic Research Program of ChinaProjects(51338003,50908051)supported by the National Natural Science Foundation of China
文摘The techniques to forecast available parking space(APS) are indispensable components for parking guidance systems(PGS). According to the data collected in Newcastle upon Tyne, England, the changing characteristics of APS were studied. Thereafter, aiming to build up a multi-step APS forecasting model that provides richer information than a conventional one-step model, the largest Lyapunov exponents(largest LEs) method was introduced into PGS. By experimental tests conducted using the same dataset, its prediction performance was compared with traditional wavelet neural network(WNN) method in both one-step and multi-step processes. Based on the results, a new multi-step forecasting model called WNN-LE method was proposed, where WNN, which enjoys a more accurate performance along with a better learning ability in short-term forecasting, was applied in the early forecast steps while the Lyapunov exponent prediction method in the latter steps precisely reflect the chaotic feature in latter forecast period. The MSE of APS forecasting for one hour time period can be reduced from 83.1 to 27.1(in a parking building with 492 berths) by using largest LEs method instead of WNN and further reduced to 19.0 by conducted the new method.
