• Intersection traffic flow fore...

Treffer: Intersection traffic flow forecasting based on v-GSVR with a new hybrid evolutionary algorithm

Title:
Intersection traffic flow forecasting based on v-GSVR with a new hybrid evolutionary algorithm
Authors:
HUANG, Min-Liang
Source:
Neurocomputing (Amsterdam). 147:343-349
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 36 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Transports terrestres, transports aeriens, transports maritimes, constructions navales, Ground, air and sea transportation, marine construction, Transports et trafic routiers, Road transportation and traffic, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse conceptuelle, Conceptual analysis, Análisis conceptual, Analyse régression, Regression analysis, Análisis regresión, Carrefour routier, Road junction, Cruce carretera, Chaos, Caos, Chaussée, Pavement, Calzada, Classification à vaste marge, Vector support machine, Máquina ejemplo soporte, Condition météorologique, Atmospheric condition, Condición meteorológica, Corps fonction, Function field, Campo función, Court terme, Short term, Corto plazo, Croisement routier, Cross roads, Intersección carretera, Culture, Cultivo, Ecoulement trafic, Traffic flow, Flujo tráfico, Erreur aléatoire, Random error, Error aleatorio, Etude expérimentale, Experimental study, Estudio experimental, Fonction perte, Loss function, Función pérdida, Gestion trafic, Traffic management, Gestión tráfico, Intersection, Intersección, Intervalle temps, Time interval, Intervalo tiempo, Modélisation, Modeling, Modelización, Processus Gauss, Gaussian process, Proceso Gauss, Prévision, Forecasting, Previsión, Route, Highway, Carretera, Système chaotique, Chaotic systems, Temps séjour, Residence time, Tiempo estancia, Chaos map, Cloud model, Gaussian loss function, Short-term traffic flow forecasting, Support vector machine
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Industrial Management, Oriental Institute of Technology, 58, Section 2, Sichuan Road, Panchiao, 220 Taipei, Tawain, Province of China
ISSN:
0925-2312
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28836758
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Building. Public works. Transport. Civil engineering

Computer science; theoretical automation; systems
Accession Number:
edscal.28836758
Database:
PASCAL Archive

Weitere Informationen

To deal well with the normally distributed random error existed in the traffic flow series, this paper introduces the v-Support Vector Regression (v-GSVR) model with the Gaussian loss function to the prediction field of short-term traffic flow. A new hybrid evolutionary algorithm (namely CCGA) is established to search the appropriate parameters of the v-GSVR, coupling the Chaos map, Cloud model and genetic algorithm. Consequently, a new forecasting approach for short-term traffic flow, combining v-GSVR model and CCGA algorithm, is proposed. The forecasting process considers the traffic flow for the road during the first few time intervals, the traffic flow for the upstream road section and weather conditions. A numerical example from the intersection between Culture Road and Shi-Full Road in Banqiao is used to verify the forecasting performance of the proposed model. The experiment indicates that the model yield more accurate results than the compared models in forecasting the short-term traffic flow at the intersection.