Open AccessA node-centric network congestion estimation method considering average spatio-temporal scale
Author(s) -
Guoyi Wen,
Ning Huang,
Juxing Zhu
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1345/4/042062
Subject(s) - computer science , network congestion , node (physics) , estimation , traffic congestion , moment (physics) , scale (ratio) , mathematical optimization , data mining , computer network , mathematics , transport engineering , network packet , engineering , geography , physics , cartography , structural engineering , systems engineering , classical mechanics
Congestion estimation is a significant issue to analyse and mitigate network congestion. Many researchers have used various estimation methods based on the load data of roads in a single moment. However, congestion is always node-centric and formed around intersections gradually. An estimation method to describe the average congestion around intersections or nodes is also valuable and needed to discover the most congested parts but is absent according to our literature researches. In this paper, we propose a node-centric network congestion method, which can evaluate the average spatio-temporal congestion scale around nodes. Based on the widely used simulation platform NetLogo, the simulation results have proved the reasonability of the proposed estimation method by the stable values of the fixed traffic network intersections, which becomes increasingly stable as time goes on, and it is found that the node-centric values calculated by our estimation method is more stable than the values by the edge-centric because of its superiority. This node-centric network congestion estimation method considering average spatio-temporal scale will be widely used because of its simplicity and universality.