Research on Evaluation of Urban Road Congestion

Abstract:

The samples marked bottlenecks and imbalanced protocol flows restrict the development of the network traffic classification technology, to solve this problem a semi-supervised machine learning traffic identification method is presented. Employ K-means algorithm to partition a training datasets that consists of a few labeled flows combined with abundant unlabeled flows.Then, identify the unlabeled samples using the labeled samples in the cluster based on k Nearest Neighbor algorithm. The theoretical analysis and experimental results show that the algorithm can improve the recognition rate of minority flows in the case of the imbalanced protocol flows.

• Periodical: Applied Mechanics and Materials (Volumes 536-537)
• Pages: 849-853
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.536-537.849
• Online since: April 2014
• Authors: Yi Dan Gao
• Keywords: Analytic Hierarchy Process (AHP), Evaluation System, Urban Road Congestion

References

[1] Wen W. A dynamic and automatic traffic light control expert system for solving the road congestion problem [J]. Expert Systems with Applications, 2008, 34(4): 2370-2381.

DOI: 10.1016/j.eswa.2007.03.007

[2] Schaefer L, Upchurch J, Ashur S A. An evaluation of freeway lane control signing using computer simulation [J]. Mathematical and computer modelling, 1998, 27(9): 177-187.

DOI: 10.1016/s0895-7177(98)00058-2

[3] Chen Y L, Yang H H. Minimization of travel time and weighted number of stops in a traffic-light network [J]. European Journal of Operational Research, 2003, 144(3): 565-580.

DOI: 10.1016/s0377-2217(02)00148-0

[4] Pongpaibool P, Tangamchit P, Noodwong K. Evaluation of road traffic congestion using fuzzy techniques [C]/TENCON 2007-2007 IEEE Region 10 Conference. IEEE, 2007: 1-4.

DOI: 10.1109/tencon.2007.4429119

[5] Glaister S, Graham D J. Proper pricing for transport infrastructure and the case of urban road congestion [J]. Urban Studies, 2006, 43(8): 1395-1418.

DOI: 10.1080/00420980600776475