This paper investigates an efficiency of signal control methodology, which mainly focuses on dealing with the traffic congestion problem in those key congested links and is applicable to be implemented in a hierarchic...
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This paper investigates an efficiency of signal control methodology, which mainly focuses on dealing with the traffic congestion problem in those key congested links and is applicable to be implemented in a hierarchical control structure in large-scale heterogeneous urban traffic networks. In this methodology, an algorithm for finding the most congested path is presented firstly, and the urban traffic flow is modeled by using a simplified macroscopic modeling framework. Then the problem of network-wide signal control is formulated as a linear programming problem that aims at minimizing the number of vehicles(or densities) in congested links so as to improve the mobility of the network and mitigate the traffic congestion. For the application of this method in real time, the multi-variables optimization problem including constraints is embedded in a model-based dynamic control procedure. Finally, different traffic demand scenarios are designed and four evaluation criteria are applied to measure the performance of the proposed method in a hypothetical road network. Compared with the fixed-time control strategy, the simulation results show that it is an effective and feasible way to regulate the traffic flow and mitigate the congestion in large-scale urban networks.
作者：
Lidong HeXiaofan WangDepartment of Automation
Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China
We consider the discrete linear state estimation problem over a packet-dropping network. Before transmitting the data, the linear measurement combination is designed at the local sensor side. In order to improve the r...
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We consider the discrete linear state estimation problem over a packet-dropping network. Before transmitting the data, the linear measurement combination is designed at the local sensor side. In order to improve the remote estimation performance, whether transmitting the current data or the combination depends on whether the previous packet is successfully received or not. A linear minimum mean square error estimation algorithm is proposed for the novel scheme. Moreover, the recursive expression between estimate and smooth error covariance of the last step, as well as the current estimation error covariance is explicitly established via properly constructing a state observer. The equivalency of the two methods is shown via some basic transformations in algebraic Riccati equation theory. The optimal weight for minimizing the estimation error covariance is derived for scalar systems.
Current freeway traffic flow prediction techniques pay attention to time series prediction or introduce the upstream adjacent road segments in the short-term prediction model. In this paper, all of the road segments o...
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ISBN:
(纸本)9781479929153
Current freeway traffic flow prediction techniques pay attention to time series prediction or introduce the upstream adjacent road segments in the short-term prediction model. In this paper, all of the road segments on the freeway are considered as candidates of the independent variables fed into the prediction model. A spatio-temporal multivariate adaptive regression splines (MARS) approach is proposed for the road network analysis and to predict the short-term traffic volume at the observation stations on the freeway. The actual traffic data are collected from a series of observation stations along a freeway in Portland every 15 minutes. In the first phase, the macroscopic dependency relationships of the stations on the freeway are investigated via MARS method. Subsequently the stations most related to the object station are selected and fed into the MARS prediction model to generate the short-term volume. The experiments are carried out on the actual traffic data and the results indicate that the proposed spatio-temporal MARS model can generate superior prediction accuracy in contrast with the historical data based MARS model, the parametric ARIMA, and the nonparametric PPR methods.
This paper presents several notes on the robust control method proposed in [Dong et al (2013), Sampled-data design for robust control of a single qubit. IEEE Transactions on Automatic control, in press] and improves t...
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The past few years have witnessed the rapid growth of online social networks, which have become important hubs of social activity and conduits of information. Identifying social influence in these newly emerging platf...
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Cutting Stock Problems (CSP) arise in many production industries where large stock sheets must be cut into smaller pieces. An irregular-shaped nesting approach for two-dimensional cutting stock problem is constructed ...
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Cutting Stock Problems (CSP) arise in many production industries where large stock sheets must be cut into smaller pieces. An irregular-shaped nesting approach for two-dimensional cutting stock problem is constructed in this research. We present a heuristic based on Particle Swarm Optimization Algorithm (PSO) for irregular-shaped two-dimensional cutting stock problem, where PSO is utilized to search optimal solution. Furthermore, the proposed approach combines a grid approximation method with Bottom-Left-Fill heuristic placement strategy to allocate irregular items. We evaluate the proposed approach using 15 revised benchmark problems available from the EURO Special Interest Group on Cutting and Packing. The performance illustrates the effectiveness and efficiency of our approach in solving irregular cutting stock problems.
Crowded scene analysis is becoming increasingly popular in computer vision field. In this paper, we propose a novel approach to analyze motion patterns by clustering the hybrid generative-discriminative feature maps u...
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ISBN:
(纸本)9781479923427
Crowded scene analysis is becoming increasingly popular in computer vision field. In this paper, we propose a novel approach to analyze motion patterns by clustering the hybrid generative-discriminative feature maps using unsupervised hierarchical clustering algorithm. The hybrid generative-discriminative feature maps are derived by posterior divergence based on the tracklets which are captured by tracking dense points with three effective rules. The feature maps effectively associate low-level features with the semantical motion patterns by exploiting the hidden information in crowded scenes. Motion pattern analyzing is implemented in a completely unsupervised way and the feature maps are clustered automatically through hierarchical clustering algorithm building on the basis of graphic model. The experiment results precisely reveal the distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.
This paper designs a mixed H 2 /H ∞ model predictive algorithm for systems with energy-bounded disturbance. The formulation of H 2 /H ∞ performance in previous work is improved by introducing a slack variable. Fur...
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This paper designs a mixed H 2 /H ∞ model predictive algorithm for systems with energy-bounded disturbance. The formulation of H 2 /H ∞ performance in previous work is improved by introducing a slack variable. Furthermore, we decouple the formulation of H 2 /H ∞ performance from the formulation of input and state constraints by dilated LMI technique. These two aspects are combined to develop a less conservative control algorithm. Recursive feasibility of the algorithm is able to be proved and stability of the controlled system is ensured. Numerical example shows the effectiveness of the proposed algorithm.
On the basis of the introduction to basic methods of the geometry modeling based on virtual reality modeling language (VRML) and the concepts of Minkowski sum computation of polyhedra, we present a method of Minkowski...
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