Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
The nature of statistical learning theory
The nature of statistical learning theory
Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Network Performance Assessment for Neurofuzzy Data Modelling
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Operations Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Selfish Routing in Capacitated Networks
Mathematics of Operations Research
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Recognizing vehicle classification information from blade sensor signature
Pattern Recognition Letters
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
Daily observed traffic flow can show different characteristics varying with the times of the day. They are caused by traffic incidents such as accidents, disabled cars, construction activities and other unusual events. Three different major traffic conditions can be occurred: "Flow," "Dense" and "Congested". Objective of this research is to identify the current traffic condition by examining the traffic measurement parameters. The earlier researches have dealt only with speed and volume by ignoring occupancy. In our study, the occupancy is another important parameter of classification. The previous works have used multiple sensors to classify traffic condition whereas our work uses only single microwave sensor. We have extended Multiple Linear Regression classification with our new approach of Estimating with Error Prediction. We present novel algorithms of Multiclassification with One-Against-All Method and Multiclassification with Binary Comparison for multiple SVM architecture. Finaly, a non-linear model of backpropagation neural network is introduced for classification. This combination has not been reported on previous studies. Training data are obtained from the Corsim based microscopic traffic simulator TSIS 5.1. All performances are compared using this data set. Our methods are currently installed and running at traffic management center of 2.Ring Road in Istanbul.