By analyzing the bus operation environment and accounting for prediction uncertainties,a bus arrival interval prediction model was developed utilizing a gated recur-rent unit(GRU)neural network.To reduce the impact of...By analyzing the bus operation environment and accounting for prediction uncertainties,a bus arrival interval prediction model was developed utilizing a gated recur-rent unit(GRU)neural network.To reduce the impact of irrelevant data and boost prediction accuracy,an attention mechanism was integrated into the point model to concen-trate on important input sequence information.Based on the point predictions,the lower upper bound estimation(LUBE)method was used,providing a range for the bus interval times predicted by the model.The model was vali-dated using data from 169 bus routes in Nanchang,Jiangxi Province.The results indicated that the attention-GRU model outperformed neural network,long short-term memory and GRU models.Compared with the Bootstrap method,the LUBE method has a narrower average interval width.The coverage width-based criterion(CWC)was reduced by 8.1%,2.2%,and 5.7%at confidence levels of 85%,90%,and 95%,respectively,during the off-peak period,and by 23.2%,26.9%,and 27.3%at confidence levels of 85%,90%,and 95%,respectively,during the peak period.Therefore,it can accurately describe the fluctuation range in bus arrival times with higher accuracy and stability.展开更多
It is particular important to identify the pattern of communication signal quickly and accurately at the airport terminal area with the increasing number of radio equipments.A signal modulation pattern recognition met...It is particular important to identify the pattern of communication signal quickly and accurately at the airport terminal area with the increasing number of radio equipments.A signal modulation pattern recognition method based on compressive sensing and improved residual network is proposed in this work.Firstly,the compressive sensing method is introduced in the signal preprocessing process to discard the redundant components for sampled signals.And the compressed measurement signals are taken as the input of the network.Furthermore,based on a scaled exponential linear units activation function,the residual unit and the residual network are constructed in this work to solve the problem of long training time and indistinguishable sample similar characteristics.Finally,the global residual is introduced into the training network to guarantee the convergence of the network.Simulation results show that the proposed method has higher recognition efficiency and accuracy compared with the state-of-the-art deep learning methods.展开更多
Transportation is one of the main sources of carbon emissions that cause climate change.The reduction of traffic carbon emissions is urgently needed.With advancements in technology,significant progress has been made i...Transportation is one of the main sources of carbon emissions that cause climate change.The reduction of traffic carbon emissions is urgently needed.With advancements in technology,significant progress has been made in reducing traffic carbon emissions.Despite numerous studies of technology's impact on traffic carbon reduction,significant challenges remain in quantitative evaluation of mitigation effects.This study conducts a systematic literature review from the perspective of carbon emission reduction in transportation and summarized the primary applications,fundamental concepts,technological maturity,and effectiveness in carbon emission reduction of four types of technologies:artificial intelligence technology,transportation energy integration,vehicle technology,and other technologies.The results indicated that the application of the reviewed technologies is expected to make significant contributions to carbon emission reduction in transportation in the near future.Additionally,this review selected 177 papers published between 2013 and 2023 and conducted general analyses such as publication time,citation frequency,and the region and countries of the study.This review provides valuable information for both public and private sectors in the field of carbon emission reduction in transportation.展开更多
基金The National Natural Science Foundation of China(No.52162042)General Science and Technology Project of Jiangxi Provincial Department of Transportation(No.2024YB039).
文摘By analyzing the bus operation environment and accounting for prediction uncertainties,a bus arrival interval prediction model was developed utilizing a gated recur-rent unit(GRU)neural network.To reduce the impact of irrelevant data and boost prediction accuracy,an attention mechanism was integrated into the point model to concen-trate on important input sequence information.Based on the point predictions,the lower upper bound estimation(LUBE)method was used,providing a range for the bus interval times predicted by the model.The model was vali-dated using data from 169 bus routes in Nanchang,Jiangxi Province.The results indicated that the attention-GRU model outperformed neural network,long short-term memory and GRU models.Compared with the Bootstrap method,the LUBE method has a narrower average interval width.The coverage width-based criterion(CWC)was reduced by 8.1%,2.2%,and 5.7%at confidence levels of 85%,90%,and 95%,respectively,during the off-peak period,and by 23.2%,26.9%,and 27.3%at confidence levels of 85%,90%,and 95%,respectively,during the peak period.Therefore,it can accurately describe the fluctuation range in bus arrival times with higher accuracy and stability.
基金supported by the National Natural Science Foundation of China(No.71874081)Special Financial Grant from China Postdoctoral Science Foundation(No.2017T100366)Open Fund of Hebei Province Key laboratory of Research on data analysis method under dynamic electro-magnetic spectrum situation.
文摘It is particular important to identify the pattern of communication signal quickly and accurately at the airport terminal area with the increasing number of radio equipments.A signal modulation pattern recognition method based on compressive sensing and improved residual network is proposed in this work.Firstly,the compressive sensing method is introduced in the signal preprocessing process to discard the redundant components for sampled signals.And the compressed measurement signals are taken as the input of the network.Furthermore,based on a scaled exponential linear units activation function,the residual unit and the residual network are constructed in this work to solve the problem of long training time and indistinguishable sample similar characteristics.Finally,the global residual is introduced into the training network to guarantee the convergence of the network.Simulation results show that the proposed method has higher recognition efficiency and accuracy compared with the state-of-the-art deep learning methods.
基金funded by Natural Science Basic Research Program of Shaanxi(024JC-YBQN-0395)Fundamental Research Funds for the Central Universities,CHD(grant no:300102344302).
文摘Transportation is one of the main sources of carbon emissions that cause climate change.The reduction of traffic carbon emissions is urgently needed.With advancements in technology,significant progress has been made in reducing traffic carbon emissions.Despite numerous studies of technology's impact on traffic carbon reduction,significant challenges remain in quantitative evaluation of mitigation effects.This study conducts a systematic literature review from the perspective of carbon emission reduction in transportation and summarized the primary applications,fundamental concepts,technological maturity,and effectiveness in carbon emission reduction of four types of technologies:artificial intelligence technology,transportation energy integration,vehicle technology,and other technologies.The results indicated that the application of the reviewed technologies is expected to make significant contributions to carbon emission reduction in transportation in the near future.Additionally,this review selected 177 papers published between 2013 and 2023 and conducted general analyses such as publication time,citation frequency,and the region and countries of the study.This review provides valuable information for both public and private sectors in the field of carbon emission reduction in transportation.
