In order to describe the compressibility of traffic flows and determine the compression factors, the Mach number of gas dynamics is introduced, and the concept and the formula of the compression factor are obtained. A...In order to describe the compressibility of traffic flows and determine the compression factors, the Mach number of gas dynamics is introduced, and the concept and the formula of the compression factor are obtained. According to the concept of the compression factor and its differential equation, a stop-wave model is built. The theoretical value and the observed one are obtained by the survey data in Changchun city. The relative error between the two values is 20. 3%. The accuracy is improved 39% compared with the result from the traditional stop-wave model. The results show that the traffic flow is compressible, and the methods of research on gas compressibility is also applicable to the traffic flow. The stop-wave model obtained by the compression factor can better describe the phenomenon of the stop wave at a signalized intersection when compared with the traditional stop-wave model.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 Basic Research Program of China (973Pro-gram)(No.2006CB705505)
文摘In order to describe the compressibility of traffic flows and determine the compression factors, the Mach number of gas dynamics is introduced, and the concept and the formula of the compression factor are obtained. According to the concept of the compression factor and its differential equation, a stop-wave model is built. The theoretical value and the observed one are obtained by the survey data in Changchun city. The relative error between the two values is 20. 3%. The accuracy is improved 39% compared with the result from the traditional stop-wave model. The results show that the traffic flow is compressible, and the methods of research on gas compressibility is also applicable to the traffic flow. The stop-wave model obtained by the compression factor can better describe the phenomenon of the stop wave at a signalized intersection when compared with the traditional stop-wave model.
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.
