A DELAY PROPAGATION ALGORITHM FOR LARGE-SCALE SCHEDULED RAIL TRAFFIC

2006 ◽  
Vol 39 (12) ◽  
pp. 245-250
Author(s):  
Rob M.P. Goverde
Keyword(s):  
Large Scale ◽  
Delay Propagation ◽  
Propagation Algorithm ◽  
Rail Traffic

A real-time control method-based simulation for high-speed trains on large-scale rail network

2017 ◽  
Vol 28 (10) ◽  
pp. 1750126 ◽  
Author(s):  
Yutong Liu ◽  
Chengxuan Cao ◽  
Yaling Zhou ◽  
Ziyan Feng
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
High Speed ◽  
Large Scale ◽  
Simulation Method ◽  
Real Time Control ◽  
Rail Network ◽  
Time Control ◽  
High Speed Trains ◽  
Rail Traffic

In this paper, an improved real-time control model based on the discrete-time method is constructed to control and simulate the movement of high-speed trains on large-scale rail network. The constraints of acceleration and deceleration are introduced in this model, and a more reasonable definition of the minimal headway is also presented. Considering the complicated rail traffic environment in practice, we propose a set of sound operational strategies to excellently control traffic flow on rail network under various conditions. Several simulation experiments with different parameter combinations are conducted to verify the effectiveness of the control simulation method. The experimental results are similar to realistic environment and some characteristics of rail traffic flow are also investigated, especially the impact of stochastic disturbances and the minimal headway on the rail traffic flow on large-scale rail network, which can better assist dispatchers in analysis and decision-making. Meanwhile, experimental results also demonstrate that the proposed control simulation method can be in real-time control of traffic flow for high-speed trains not only on the simple rail line, but also on the complicated large-scale network such as China’s high-speed rail network and serve as a tool of simulating the traffic flow on large-scale rail network to study the characteristics of rail traffic flow.

scholarly journals Local memory boosts label propagation for community detection

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Antonio Maria Fiscarelli ◽  
Matthias R. Brust ◽  
Grégoire Danoy ◽  
Pascal Bouvry
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Linear Time ◽  
Topological Analysis ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Performance Study ◽  
Global Knowledge ◽  
Local Optima ◽  
Propagation Algorithm

Abstract The objective of a community detection algorithm is to group similar nodes that are more connected to each other than with the rest of the network. Several methods have been proposed but many are of high complexity and require global knowledge of the network, which makes them less suitable for large-scale networks. The Label Propagation Algorithm initially assigns a distinct label to each node that iteratively updates its label with the one of the majority of its neighbors, until consensus is reached among all nodes in the network. Nodes sharing the same label are then grouped into communities. It runs in near linear time and is decentralized, but it gets easily stuck in local optima and often returns a single giant community. To overcome these problems we propose MemLPA, a variation of the classical Label Propagation Algorithm where each node implements a memory mechanism that allows them to “remember” about past states of the network and uses a decision rule that takes this information into account. We demonstrate through extensive experiments, on the Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world networks, that MemLPA outperforms other existing label propagation algorithms that implement memory and some of the well-known community detection algorithms. We also perform a topological analysis to extend the performance study and compare the topological properties of the communities found to the ground-truth community structure.

Label propagation algorithm based on node similarity driven by local information

2019 ◽  
Vol 33 (30) ◽  
pp. 1950363
Author(s):  
Chen Song ◽  
Guoyan Huang ◽  
Bo Yin ◽  
Bing Zhang ◽  
Xinqian Liu
Keyword(s):  
Large Scale ◽  
Linear Time ◽  
Local Information ◽  
Label Propagation ◽  
Selection Scheme ◽  
Original Algorithm ◽  
Large Scale Network ◽  
Propagation Algorithm ◽  
Node Similarity ◽  
Scale Network

Label propagation algorithm (LPA) attracts wide attention in community detection field for its near linear time complexity in large scale network. However, the algorithm adopts a random selection scheme in label updating strategy, which results in unstable division and poor accuracy. In this paper, five different indicators of node similarity are introduced based on network local information to distinguish nodes and a new label updating method is proposed. When there are multiple maximum neighbor labels in the propagation process, the maximum label corresponding to the most similar node is selected for updating instead of a random one. Five different forms of improved LPA are proposed which are named as SAL-LPA, SOR-LPA, JAC-LPA, SOR-LPA, HDI-LPA and HPI-LPA. The experiment results on real-world and artificial benchmark networks show that the improved LPA greatly improves the performance of the original algorithm, among which HPI-LPA is the best.

scholarly journals A Node Influence Based Label Propagation Algorithm for Community Detection in Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Xing ◽  
Fanrong Meng ◽  
Yong Zhou ◽  
Mu Zhu ◽  
Mengyu Shi ◽  
...  
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Detection Method ◽  
Label Propagation ◽  
Experimental Results ◽  
Superior Performance ◽  
Propagation Algorithm ◽  
Large Scale Networks ◽  
Poor Stability

Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of label updating and the mechanism of label choosing when more than one label is contained by the maximum number of nodes. NIBLPA can get more stable results than LPA since it avoids the complete randomness of LPA. The experimental results on both synthetic and real networks demonstrate that NIBLPA maintains the efficiency of the traditional LPA algorithm, and, at the same time, it has a superior performance to some representative methods.

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