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
Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanc...Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.展开更多
This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)f...This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.展开更多
Mixed traffic flow composed of autos and non-autos widely exists in developing countries and areas. To investigate the operational characteristics of the mixed traffic flow consisting of vehicles in different types （...Mixed traffic flow composed of autos and non-autos widely exists in developing countries and areas. To investigate the operational characteristics of the mixed traffic flow consisting of vehicles in different types （large vehicles, cars, and bicycles）, we develop a cellular automaton model to repli- cate the travel behaviors on a bi-directional road segment with respect to the physical and mechanic features of different vehicle types. By implementing the essential parameters calibrated through the field data collection, a numerical study is carried out considering the variation in volume, density, and velocity with different compositions of mixed traffic flows. The primary findings include： the average ve- locity of traffic flow and total volume decrease 60% and 30% after incorporating 10% bicycles, respectively; the phenomenon of double-summit in terms bicycle is beyond 60 % ; the maximal total volume higher than 10 %. of the total volume appears when the proportion of starts to recover when the proportion of bicycle is展开更多
为解决传统网络路由转发体系缺乏灵活性的问题,提出了OpenTDR开放式架构方案。该方案通过集成二维路由和软件定义网络(software defined network,SDN)技术,可在不同时间段实施不同的流量均衡和节能机制。实验结果表明,由一维升级至二维...为解决传统网络路由转发体系缺乏灵活性的问题,提出了OpenTDR开放式架构方案。该方案通过集成二维路由和软件定义网络(software defined network,SDN)技术,可在不同时间段实施不同的流量均衡和节能机制。实验结果表明,由一维升级至二维的路由转发方式,配合SDN的集中式网络规划,可明显增强网络(尤其是广域网)的流量调度与服务质量控制的灵活性,达到流量均衡与节约能耗的目的。另外,二维路由机制兼容传统路由,为基于传统网络的SDN改造提供了可行的过渡方案。展开更多
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.
基金Project(2012CB725403)supported by the National Basic Research Program of ChinaProjects(71210001,51338008)supported by the National Natural Science Foundation of ChinaProject supported by World Capital Cities Smooth Traffic Collaborative Innovation Center and Singapore National Research Foundation Under Its Campus for Research Excellence and Technology Enterprise(CREATE)Programme
文摘Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.
文摘This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.
文摘Mixed traffic flow composed of autos and non-autos widely exists in developing countries and areas. To investigate the operational characteristics of the mixed traffic flow consisting of vehicles in different types （large vehicles, cars, and bicycles）, we develop a cellular automaton model to repli- cate the travel behaviors on a bi-directional road segment with respect to the physical and mechanic features of different vehicle types. By implementing the essential parameters calibrated through the field data collection, a numerical study is carried out considering the variation in volume, density, and velocity with different compositions of mixed traffic flows. The primary findings include： the average ve- locity of traffic flow and total volume decrease 60% and 30% after incorporating 10% bicycles, respectively; the phenomenon of double-summit in terms bicycle is beyond 60 % ; the maximal total volume higher than 10 %. of the total volume appears when the proportion of starts to recover when the proportion of bicycle is
文摘为解决传统网络路由转发体系缺乏灵活性的问题,提出了OpenTDR开放式架构方案。该方案通过集成二维路由和软件定义网络(software defined network,SDN)技术,可在不同时间段实施不同的流量均衡和节能机制。实验结果表明,由一维升级至二维的路由转发方式,配合SDN的集中式网络规划,可明显增强网络(尤其是广域网)的流量调度与服务质量控制的灵活性,达到流量均衡与节约能耗的目的。另外,二维路由机制兼容传统路由,为基于传统网络的SDN改造提供了可行的过渡方案。
