A Traffic Information Fusion Algorithm Based on Self Organizing Maps
نویسنده
چکیده
Today with the rapid development of information technology, it is becoming more and more important to be able to share traffic information between various traffic management administrations. The purpose of this paper is to discuss a data fusion algorithm based on Self Organizing Maps (SOMs) for Integrated Traffic Information System (ITIS), which is one of an essential part of Intelligent Transportation Systems (ITS) and is being implemented in many metropolises in China recently. In this paper, the architecture of SOMs network is first described; secondly, the learning and training rules of SOMs are briefly addressed; in the third place, a data fusion algorithm based on SOMs neural network is validated with quantitative traffic data collected on urban expressway. Through the test in a certain area in the city of Shenzhen, we got the conclusion that data fusion algorithm studied in this paper can provide precise and comprehensive traffic information in ITIS for travelers and decision-makers so to improve the safety and efficiency of the surface transportation system.
منابع مشابه
Landforms identification using neural network-self organizing map and SRTM data
During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic ...
متن کاملDetecting Anomalous Network Traffic with Self-organizing Maps
Integrated Network-Based Ohio University Network Detective Service (INBOUNDS) is a network based intrusion detection system being developed at Ohio University. The Anomalous Network-Traffic Detection with Self Organizing Maps (ANDSOM) module for INBOUNDS detects anomalous network traffic based on the Self-Organizing Map algorithm. Each network connection is characterized by six parameters and s...
متن کاملUncertainty Modeling of a Group Tourism Recommendation System Based on Pearson Similarity Criteria, Bayesian Network and Self-Organizing Map Clustering Algorithm
Group tourism is one of the most important tasks in tourist recommender systems. These systems, despite of the potential contradictions among the group's tastes, seek to provide joint suggestions to all members of the group, and propose recommendations that would allow the satisfaction of a group of users rather than individual user satisfaction. Another issue that has received less attention i...
متن کاملGait Based Vertical Ground Reaction Force Analysis for Parkinson’s Disease Diagnosis Using Self Organizing Map
The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson’s disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysi...
متن کاملGait Based Vertical Ground Reaction Force Analysis for Parkinson’s Disease Diagnosis Using Self Organizing Map
The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson’s disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysi...
متن کامل