Key tactics of origin-based user equilibrium (OUE) algorithm was studied,which involved the algorithm procedure and several implementation issues.To speed up the convergence,update policies of flows,costs and bushes w...Key tactics of origin-based user equilibrium (OUE) algorithm was studied,which involved the algorithm procedure and several implementation issues.To speed up the convergence,update policies of flows,costs and bushes were proposed.The methods of step-size searching and bush construction are proved to be practical.The modified OUE algorithm procedure was also optimized to take the advantage of multi-thread process.Convergence performances were compared with those of other algorithms by different sizes of urban transportation networks.The result shows this modified OUE algorithm is more efficient and consumes less time to achieve the reasonable relative gap in practical applications.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
A model is proposed to describe the passengers’route choice behaviors in urban railway traffic with stochastic link capacity degradation by considering two types of demand,sensitive and insensitive passenger.The inse...A model is proposed to describe the passengers’route choice behaviors in urban railway traffic with stochastic link capacity degradation by considering two types of demand,sensitive and insensitive passenger.The insensitive passengers choose their route without paying much attention to congestion.To the contrary,sensitive passengers who consider route congestion choose travel route based on generalized cost.An equilibrium state is given by variational inequalities in terms of travel generalized cost,which is represented by the combinations of mean and variance of total travel time.The diagonalization algorithm is given to solve this programming.Results show that insensitive passengers have large effects on the path choice than sensitive ones,especially for the larger demand.展开更多
Advanced traveler information systems （ATIS） can not only improve drivers＇ accessibility to the more accurate route travel time information, but also can improve drivers＇ adaptability to the stochastic network cap...Advanced traveler information systems （ATIS） can not only improve drivers＇ accessibility to the more accurate route travel time information, but also can improve drivers＇ adaptability to the stochastic network capacity degradations. In this paper, a mixed stochastic user equilibrium model was proposed to describe the interactive route choice behaviors between ATIS equipped and unequipped drivers on a degradable transport network. In the proposed model the information accessibility of equipped drivers was reflected by lower degree of uncertainty in their stochastic equilibrium flow distributions, and their behavioral adaptability was captured by multiple equilibrium behaviors over the stochastic network state set. The mixed equilibrium model was formulated as a fixed point problem defined in the mixed route flows, and its solution was achieved by executing an iterative algorithm. Numerical experiments were provided to verify the properties of the mixed network equilibrium model and the efficiency of the iterative algorithm.展开更多
基金Projects(70631002,70701027) supported by the National Natural Science Foundation of ChinaProject(NCET-08-0406) supported by the Program for New Century Excellent Talents in Chinese University
文摘Key tactics of origin-based user equilibrium (OUE) algorithm was studied,which involved the algorithm procedure and several implementation issues.To speed up the convergence,update policies of flows,costs and bushes were proposed.The methods of step-size searching and bush construction are proved to be practical.The modified OUE algorithm procedure was also optimized to take the advantage of multi-thread process.Convergence performances were compared with those of other algorithms by different sizes of urban transportation networks.The result shows this modified OUE algorithm is more efficient and consumes less time to achieve the reasonable relative gap in practical applications.
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.
基金Project(71525002) supported by China National Funds for Distinguished Young ScientistsProjects(71271023,71210001) supported by the National Natural Science Foundation of ChinaProject(RCS2015ZZ003) supported by the Research Foundation of State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,China
文摘A model is proposed to describe the passengers’route choice behaviors in urban railway traffic with stochastic link capacity degradation by considering two types of demand,sensitive and insensitive passenger.The insensitive passengers choose their route without paying much attention to congestion.To the contrary,sensitive passengers who consider route congestion choose travel route based on generalized cost.An equilibrium state is given by variational inequalities in terms of travel generalized cost,which is represented by the combinations of mean and variance of total travel time.The diagonalization algorithm is given to solve this programming.Results show that insensitive passengers have large effects on the path choice than sensitive ones,especially for the larger demand.
基金Projects(51378119,51578150)supported by the National Natural Science Foundation of China
文摘Advanced traveler information systems （ATIS） can not only improve drivers＇ accessibility to the more accurate route travel time information, but also can improve drivers＇ adaptability to the stochastic network capacity degradations. In this paper, a mixed stochastic user equilibrium model was proposed to describe the interactive route choice behaviors between ATIS equipped and unequipped drivers on a degradable transport network. In the proposed model the information accessibility of equipped drivers was reflected by lower degree of uncertainty in their stochastic equilibrium flow distributions, and their behavioral adaptability was captured by multiple equilibrium behaviors over the stochastic network state set. The mixed equilibrium model was formulated as a fixed point problem defined in the mixed route flows, and its solution was achieved by executing an iterative algorithm. Numerical experiments were provided to verify the properties of the mixed network equilibrium model and the efficiency of the iterative algorithm.
