The effect of the information delay, which was caused by thc naturc of the distance sensors and wireless communication systems, on the string stability of platoon of automated vehicles was studied. The longitudinal ve...The effect of the information delay, which was caused by thc naturc of the distance sensors and wireless communication systems, on the string stability of platoon of automated vehicles was studied. The longitudinal vehicle dynamics model was built by taking the information delay into consideration, and three typical information frameworks, i.e., leader-predecessor framework （LPF）, multiple-predecessors framework （MPF） and predecessor-successor framework （PSF）, were defined and their related spacing error dynamics models in frequency domain were proposed. The string stability of platoon of automated vehicles was analyzed for the LPF, MPF and PSF, respectively. Meanwhile, the related sufficient string stable conditions were also obtained. The results demonstrate that the string stability can be guaranteed tbr the LPF and PSF with considering the information delay, but the ranges of the control gains of the control laws are smaller than those without considering the information delay. For the MPF, the ＂weak＂ string stability, which can be guaranteed without considering the information delay, cannot be obtained with considering the information delay. The comparative simulations further demonstrate that the LPF shows better string stability, but the PSF shows better string scalable performance.展开更多
Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department t...Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.展开更多
基金Project(20070006011) supported by the Doctoral Foundation of Ministry of Education of China
文摘The effect of the information delay, which was caused by thc naturc of the distance sensors and wireless communication systems, on the string stability of platoon of automated vehicles was studied. The longitudinal vehicle dynamics model was built by taking the information delay into consideration, and three typical information frameworks, i.e., leader-predecessor framework （LPF）, multiple-predecessors framework （MPF） and predecessor-successor framework （PSF）, were defined and their related spacing error dynamics models in frequency domain were proposed. The string stability of platoon of automated vehicles was analyzed for the LPF, MPF and PSF, respectively. Meanwhile, the related sufficient string stable conditions were also obtained. The results demonstrate that the string stability can be guaranteed tbr the LPF and PSF with considering the information delay, but the ranges of the control gains of the control laws are smaller than those without considering the information delay. For the MPF, the ＂weak＂ string stability, which can be guaranteed without considering the information delay, cannot be obtained with considering the information delay. The comparative simulations further demonstrate that the LPF shows better string stability, but the PSF shows better string scalable performance.
基金Project(61873283)supported by the National Natural Science Foundation of ChinaProject(KQ1707017)supported by the Changsha Science&Technology Project,ChinaProject(2019CX005)supported by the Innovation Driven Project of the Central South University,China。
文摘Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.
