Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 127-133.

• Intelligent Computing • Previous Articles     Next Articles

Traffic Efficiency Analysis of Traffic Road Network Based on Percolation Theory

GAO Hua-bing1, SONG Cong-cong2, CHEN Bo2, LIU Zhi2   

  1. (College of Information Engineering,Yichun Vocational Technical College,Yichun,Jiangxi 336000,China)1;
    (College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)2
  • Online:2019-11-10 Published:2019-11-20

Abstract: According to the congestion phenomenon of urban road network,the percolation theory is used to analyze the traffic efficiency of road network model.Firstly,by using the geographic data of actual urban roads,the original method is used to construct the traffic road network model.Then,by quantifying the traffic efficiency of the road network,the influence of the traffic jams on the traffic situation under different weather conditions is analyzed.This paper mainlyevaluated the traffic situation through the formulation of rules,the analysis of thresholds,the division of strong connec-ted subgraphs and the determination of traffic efficiency.The influence of weather factors on traffic network was verified under different weather conditions.

Key words: Complex networks, Percolation theory, Strongly connected subgraph, Traffic efficiency

CLC Number: 

  • TP391
[1]汪小帆,李翔,陈关荣.网络科学导论[M].北京:高等教育出版社,2012.
[2]齐金山,梁循,李志宇,等.大规模复杂信息网络表示学习:概念、方法与挑战[J].计算机学报,2018(10):2394-2420.
[3]BANDO M,HASEBE K,NAKAYAMA A,et al.Dynamicalmodel of traffic congestion and numerical simulation[J].Phys.Rev.E,1995,51:1035-1042.
[4]HELBING D.Traffic and related self-driven many-particle systems[J].Rev.Mod.Phys.,2001,73:1067-1141.
[5]SOLE-RIBALTA A,GOMEZ S,ARENAS A.A model to identify urban traffic congestion hotspots in complex networks.Royal Society Open Science,2016,3(10):160098.
[6]EZAKI T,NISHI R,NISHINARI K.Taming macroscopic jamming in transportation networks.Journal of Statistical Mechanics Theory and Experiment,2015,6:P06013.
[7]LI D Q,FU B W,WANG Y P,et al.Percolation transition in dynamical traffic network with evolving critical bottlenecks[J].Proc.Natl.Acad.Sci.USA,2015,112(3):669-672.
[8]WANG F L,LI D Q,XU X Y,et al.Percolation properties in a traffic model[J].Europhysics Letters,2015,112(3):38001.
[9]ZENG G W,LI D Q,GUO S M,et al.Switch between critical percolation modes in city traffic dynamics[J].Physics and Society,2019,116:23-28.
[10]GAO G,SUN H J,WU J J.Activity-based trip chaining beha-vior analysis in the network under the parking fee scheme[J].Transportation,2017,3:1-23.
[1] LI Jia-wen, GUO Bing-hui, YANG Xiao-bo, ZHENG Zhi-ming. Disease Genes Recognition Based on Information Propagation [J]. Computer Science, 2022, 49(1): 264-270.
[2] WANG Xue-guang, ZHANG Ai-xin, DOU Bing-lin. Non-linear Load Capacity Model of Complex Networks [J]. Computer Science, 2021, 48(6): 282-287.
[3] MA Yuan-yuan, HAN Hua, QU Qian-qian. Importance Evaluation Algorithm Based on Node Intimate Degree [J]. Computer Science, 2021, 48(5): 140-146.
[4] YIN Zi-qiao, GUO Bing-hui, MA Shuang-ge, MI Zhi-long, SUN Yi-fan, ZHENG Zhi-ming. Autonomous Structural Adjustment of Crowd Intelligence Network: Begin from Structure of Biological Regulatory Network [J]. Computer Science, 2021, 48(5): 184-189.
[5] ZHAO Man-yu, YE Jun. Synchronization of Uncertain Complex Networks with Sampled-data and Input Saturation [J]. Computer Science, 2021, 48(11A): 481-484.
[6] ZHAO Lei, ZHOU Jin-he. ICN Energy Efficiency Optimization Strategy Based on Content Field of Complex Networks [J]. Computer Science, 2019, 46(9): 137-142.
[7] LIU Xiao-dong, WEI Hai-ping, CAO Yu. Modeling and Stability Analysis for SIRS Model with Network Topology Changes [J]. Computer Science, 2019, 46(6A): 375-379.
[8] SHAN Na, LI Long-jie, LIU Yu-yang, CHEN Xiao-yun. Link Prediction Based on Correlation of Nodes’ Connecting Patterns [J]. Computer Science, 2019, 46(12): 20-25.
[9] FU Li-dong, LI Dan, LI Zhan-li. Following-degree Tree Algorithm to Detect Overlapping Communities in Complex Networks [J]. Computer Science, 2019, 46(12): 322-326.
[10] SONG Yan-qiu, LI Gui-jun, LI Hui-jia. Community Label Detection Algorithm Based on Potential Background Information [J]. Computer Science, 2018, 45(6A): 314-317.
[11] LUO Jin-liang, JIN Jia-cai and WANG Lei. Evaluation Method for Node Importance in Air Defense Networks Based on Functional Contribution Degree [J]. Computer Science, 2018, 45(2): 175-180.
[12] CEHN Jun-hua, BIAN Zhai-an, LI Hui-jia, GUAN Run-dan. Measuring Method of Node Influence Based on Relative Entropy [J]. Computer Science, 2018, 45(11A): 292-298.
[13] LV Ya-nan, HAN Hua, JIA Cheng-feng, WAN Yan-juan. Link Prediction Algorithm Based on Node Intimate Degree [J]. Computer Science, 2018, 45(11): 92-96.
[14] LU Yi-hong, ZHANG Zhen-ning and YANG Xiong. Community Structure Detection Algorithm Based on Nodes’ Eigenvectors [J]. Computer Science, 2017, 44(Z6): 419-423.
[15] JIANG Mao-sheng, GE Jian-fei and CHEN Ling. Link Prediction in Networks with Node Attributes Based on Space Mapping [J]. Computer Science, 2017, 44(7): 257-261.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!