Blockage is a kind of phenomenon occurring frequently in modern transportation network. This paper deals with the research work on the blocking now in a network with the help of network flow theory. The blockage pheno...Blockage is a kind of phenomenon occurring frequently in modern transportation network. This paper deals with the research work on the blocking now in a network with the help of network flow theory. The blockage phenomena can be divided intO local blockage and network blockage. In this paper, which deals mainly with the latter, the fundamental concepts and definitions of network blocking flow, blocking outset are presented and the related theorems are proved. It is proved that the sufficient and necessary condition for the emergence of a blocking now in a network is the existence of the blocking outset. The necessary conditions for the existence of the blocking outset in a network are analysed and the characteristic cutset of blockage which reflects the all possible situation of blocking nows in the network is defined.In the last part of the paper the mathematical model of the minimum blocking now is developed and the solution to a small network is given.展开更多
This paper deals with the research work on the phenomena of local blockage in a transportation network. Onthe basis of introducing the research results in [1], theminimum now capacity problem of a network in the mosts...This paper deals with the research work on the phenomena of local blockage in a transportation network. Onthe basis of introducing the research results in [1], theminimum now capacity problem of a network in the mostseriously blocked situation is studied. With the conceptof complete outset presented in [1], the relationship between the minimum now capacity of a network and its minimum complete cut capacity is discussed, and the reasons for the difference betweent the minimum now capacity of a network and its minimum complete cut capa-city are analysed. In order to get the solution to the problem, the concepts of normalization of a network and its blocking path graph are presented. In the paper it is proved that the necessary and sufficient conditions for the equality between the minumum now capacity and its minumum complete cut capacity are the existence of a feasible flow in the blocking path graph. For the reason that there are some dependent production points in the blocking path graph of a network, the proof about the tenability of the Gale's Theorm for the planat normalized network without circuit is made.展开更多
In this work a method called “signal flow graph (SFG)” is presented. A signal-flow graph describes a system by its signal flow by directed and weighted graph;the signals are applied to nodes and functions on edges. ...In this work a method called “signal flow graph (SFG)” is presented. A signal-flow graph describes a system by its signal flow by directed and weighted graph;the signals are applied to nodes and functions on edges. The edges of the signal flow graph are small processing units, through which the incoming signals are processed in a certain form. In this case, the result is sent to the outgoing node. The SFG allows a good visual inspection into complex feedback problems. Furthermore such a presentation allows for a clear and unambiguous description of a generating system, for example, a netview. A Signal Flow Graph (SFG) allows a fast and practical network analysis based on a clear data presentation in graphic format of the mathematical linear equations of the circuit. During creation of a SFG the Direct Current-Case (DC-Case) was observed since the correct current and voltage directions was drawn from zero frequency. In addition, the mathematical axioms, which are based on field algebra, are declared. In this work we show you in addition: How we check our SFG whether it is a consistent system or not. A signal flow graph can be verified by generating the identity of the signal flow graph itself, illustrated by the inverse signal flow graph (SFG−1). Two signal flow graphs are always generated from one circuit, so that the signal flow diagram already presented in previous sections corresponds to only half of the solution. The other half of the solution is the so-called identity, which represents the (SFG−1). If these two graphs are superposed with one another, so called 1-edges are created at the node points. In Boolean algebra, these 1-edges are given the value 1, whereas this value can be identified with a zero in the field algebra.展开更多
Urban water supply network is a modern urban survival and development of the infrastructure of a city,and its normal running conditions have important significance. The actual hydraulic process in the variableload wat...Urban water supply network is a modern urban survival and development of the infrastructure of a city,and its normal running conditions have important significance. The actual hydraulic process in the variableload water distribution networks can be treated as the slow transient flow which belongs to the unsteady flow. This paper analyzes the multi-loops network slow transient model based on graph theory,and the link flow matrix is treated as the variables of the discrete solution model to simulate the process of the slow transient flow in the network. With the simulation of hydraulic regime in an actual pipe network,the changing laws of the flow in the pipes,nodal hydraulic heads and other hydraulic factors with the passage of time are obtained. Since the transient processes offer much more information than a steady process,the slow transient theory is not only practical on analyzing the hydraulic condition of the network,but also on identifying hydraulic resistance coefficients of pipes and detecting the leakage in networks.展开更多
随着城市轨道交通的快速发展,客流量的准确预测对于改善运营服务至关重要。为了解决当前地铁客流预测存在的时空特性挖掘不充分等问题,进一步提高预测的精度与效率,研究了基于动态图神经常微分方程模型(multivariate time series with d...随着城市轨道交通的快速发展,客流量的准确预测对于改善运营服务至关重要。为了解决当前地铁客流预测存在的时空特性挖掘不充分等问题,进一步提高预测的精度与效率,研究了基于动态图神经常微分方程模型(multivariate time series with dynamic graph neural ordinary differential equations,MTGODE)的地铁短时客流预测方法。该方法通彭颢1贺玉过学习地铁站点间的动态关联强度构建动态拓扑图结构,基于学习得到的动态图进行连续图传播以传递时空信息、挖掘客流的依赖关系,并采用残差卷积提取多时间尺度下的周期性模式,实现了对站点间时空动态的连续表征,克服了传统图卷积网络模型难以刻画动态空间依赖的局限性。此外,为了充分挖掘不同站点间客流分布的时空规律,综合利用北京地铁自动售检票系统(auto fare collection,AFC)刷卡数据、天气数据、空气质量数据以及车站周边用地属性数据构建多源融合的客流预测模型。通过选取地铁北京站和积水潭站-东直门站的历史数据开展进站客流和OD客流预测实验,结果表明:与多个基准模型相比,该模型在平均绝对误差、均方根误差和平均百分比误差这3个指标中均取得了更优的预测效果,相较最优基准模型扩散卷积循环神经网络(diffusion convolutional recurrent neural network,DCRNN)分别降低了9.93%,12.30%,9.23%,对地铁客流时空分布的拟合程度更好,模型具有更好的预测精度、稳定性和拟合能力。展开更多
文摘Blockage is a kind of phenomenon occurring frequently in modern transportation network. This paper deals with the research work on the blocking now in a network with the help of network flow theory. The blockage phenomena can be divided intO local blockage and network blockage. In this paper, which deals mainly with the latter, the fundamental concepts and definitions of network blocking flow, blocking outset are presented and the related theorems are proved. It is proved that the sufficient and necessary condition for the emergence of a blocking now in a network is the existence of the blocking outset. The necessary conditions for the existence of the blocking outset in a network are analysed and the characteristic cutset of blockage which reflects the all possible situation of blocking nows in the network is defined.In the last part of the paper the mathematical model of the minimum blocking now is developed and the solution to a small network is given.
文摘This paper deals with the research work on the phenomena of local blockage in a transportation network. Onthe basis of introducing the research results in [1], theminimum now capacity problem of a network in the mostseriously blocked situation is studied. With the conceptof complete outset presented in [1], the relationship between the minimum now capacity of a network and its minimum complete cut capacity is discussed, and the reasons for the difference betweent the minimum now capacity of a network and its minimum complete cut capa-city are analysed. In order to get the solution to the problem, the concepts of normalization of a network and its blocking path graph are presented. In the paper it is proved that the necessary and sufficient conditions for the equality between the minumum now capacity and its minumum complete cut capacity are the existence of a feasible flow in the blocking path graph. For the reason that there are some dependent production points in the blocking path graph of a network, the proof about the tenability of the Gale's Theorm for the planat normalized network without circuit is made.
文摘In this work a method called “signal flow graph (SFG)” is presented. A signal-flow graph describes a system by its signal flow by directed and weighted graph;the signals are applied to nodes and functions on edges. The edges of the signal flow graph are small processing units, through which the incoming signals are processed in a certain form. In this case, the result is sent to the outgoing node. The SFG allows a good visual inspection into complex feedback problems. Furthermore such a presentation allows for a clear and unambiguous description of a generating system, for example, a netview. A Signal Flow Graph (SFG) allows a fast and practical network analysis based on a clear data presentation in graphic format of the mathematical linear equations of the circuit. During creation of a SFG the Direct Current-Case (DC-Case) was observed since the correct current and voltage directions was drawn from zero frequency. In addition, the mathematical axioms, which are based on field algebra, are declared. In this work we show you in addition: How we check our SFG whether it is a consistent system or not. A signal flow graph can be verified by generating the identity of the signal flow graph itself, illustrated by the inverse signal flow graph (SFG−1). Two signal flow graphs are always generated from one circuit, so that the signal flow diagram already presented in previous sections corresponds to only half of the solution. The other half of the solution is the so-called identity, which represents the (SFG−1). If these two graphs are superposed with one another, so called 1-edges are created at the node points. In Boolean algebra, these 1-edges are given the value 1, whereas this value can be identified with a zero in the field algebra.
基金Sponsored by the National Natural Science Foundation of China(Grant No.50908064 and 51208158)the 46thChina Postdoctoral Science Foundation(Grant No.20090460912)
文摘Urban water supply network is a modern urban survival and development of the infrastructure of a city,and its normal running conditions have important significance. The actual hydraulic process in the variableload water distribution networks can be treated as the slow transient flow which belongs to the unsteady flow. This paper analyzes the multi-loops network slow transient model based on graph theory,and the link flow matrix is treated as the variables of the discrete solution model to simulate the process of the slow transient flow in the network. With the simulation of hydraulic regime in an actual pipe network,the changing laws of the flow in the pipes,nodal hydraulic heads and other hydraulic factors with the passage of time are obtained. Since the transient processes offer much more information than a steady process,the slow transient theory is not only practical on analyzing the hydraulic condition of the network,but also on identifying hydraulic resistance coefficients of pipes and detecting the leakage in networks.
文摘随着城市轨道交通的快速发展,客流量的准确预测对于改善运营服务至关重要。为了解决当前地铁客流预测存在的时空特性挖掘不充分等问题,进一步提高预测的精度与效率,研究了基于动态图神经常微分方程模型(multivariate time series with dynamic graph neural ordinary differential equations,MTGODE)的地铁短时客流预测方法。该方法通彭颢1贺玉过学习地铁站点间的动态关联强度构建动态拓扑图结构,基于学习得到的动态图进行连续图传播以传递时空信息、挖掘客流的依赖关系,并采用残差卷积提取多时间尺度下的周期性模式,实现了对站点间时空动态的连续表征,克服了传统图卷积网络模型难以刻画动态空间依赖的局限性。此外,为了充分挖掘不同站点间客流分布的时空规律,综合利用北京地铁自动售检票系统(auto fare collection,AFC)刷卡数据、天气数据、空气质量数据以及车站周边用地属性数据构建多源融合的客流预测模型。通过选取地铁北京站和积水潭站-东直门站的历史数据开展进站客流和OD客流预测实验,结果表明:与多个基准模型相比,该模型在平均绝对误差、均方根误差和平均百分比误差这3个指标中均取得了更优的预测效果,相较最优基准模型扩散卷积循环神经网络(diffusion convolutional recurrent neural network,DCRNN)分别降低了9.93%,12.30%,9.23%,对地铁客流时空分布的拟合程度更好,模型具有更好的预测精度、稳定性和拟合能力。