An algorithm for the recognition of levels of congestion in road traffic problems

Authors:
Angélica Lozano;Giuseppe Manfredi;Luciano Nieddu
Affiliations:
Laboratorio de Transporte y Sistemas Territoriales, Instituto de Ingeniería, Universidad Nacional Autónoma de México (UNAM), Mexico;Department of Political Science, Libera Universitá "S. Pio V", Rome, Italy;Department of Economics, Libera Universitá "S. Pio V", Rome Italy
Venue:
Mathematics and Computers in Simulation
Year:
2009

Citing 7
Cited 0

Pattern recognition: human and mechanical

Pattern recognition: human and mechanical
Pattern recognition with an imperfect supervisor

Pattern Recognition
The nature of statistical learning theory

The nature of statistical learning theory
Image processing techniques for real-time qualitative road traffic data analysis

Real-Time Imaging
K-T.R.A.C.E: A kernel k-means procedure for classification

Computers and Operations Research
Handbook of Parametric and Nonparametric Statistical Procedures

Handbook of Parametric and Nonparametric Statistical Procedures
Nearest neighbor pattern classification

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

Detection and recognition of the level of congestion at an intersection is a very important problem and a valuable source of information in traffic management. Although it is just one of all the aspects that make up a traffic management system, it seems to be a crucial point for gathering information. In this paper, we present a technique based on a k-means clustering algorithm for classification, which has been already successfully used in a number of pattern recognition problems, namely: as an algorithm for face recognition problems and in a number of medical diagnosis problems and it compares very well with the state of the art techniques.