Result: Visual exploratory data analysis of traffic volume

Title:
Visual exploratory data analysis of traffic volume
Source:
MICAI 2006 (advances in artificial intelligence)Lecture notes in computer science. :695-703
Publisher Information:
Berlin; Heidelberg; New York: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 15 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, 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, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Transports terrestres, transports aeriens, transports maritimes, constructions navales, Ground, air and sea transportation, marine construction, Analyse donnée, Data analysis, Análisis datos, Analyse exploratoire, Exploratory analysis, Análisis exploratorio, Base donnée très grande, Very large databases, Congestion trafic, Traffic congestion, Congestión tráfico, Contrôleur trafic, Traffic controller, Supervisor tráfico, Extraction information, Information extraction, Extracción información, Fouille donnée, Data mining, Busca dato, Gestion trafic, Traffic management, Gestión tráfico, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Milieu urbain, Urban environment, Medio urbano, Méthode potentiel, Potential method, Método potencial, Planification automatisée, Assisted planning, Planificación automatizada, Poutre, Beam(mechanics), Viga, Route, Highway, Carretera, Stéganographie, Stéganography, Esteganografía, Surveillance système, System monitoring, Système acquisition donnée, Data acquisition system, Sistema adquisición dato, Système intelligent, Intelligent system, Sistema inteligente, Système transport, Transportation system, Sistema de transporte, Traitement donnée, Data processing, Tratamiento datos, Vision ordinateur, Computer vision, Visión ordenador, Visualisation donnée, Data visualization, Zone urbaine, Urban area, Zona urbana
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Institute of Geographic Sciences & Natural Resources Research, CAS, No. 11A Datun Road, Beijing 100101, China
Department of Geography, University of Tennessee, Knoxville, TN 37996-0925, United States
ISSN:
0302-9743
Rights:
Copyright 2007 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.19151752
Database:
PASCAL Archive

Further Information

Beijing has deployed Intelligent Transportation System (ITS) monitoring devices along selected major roads in the core urban area in order to help relieve traffic congestion and improve traffic conditions. The huge amount of traffic data from ITS originally collected for the control of traffic signals can be a useful source to assist in transportation designing, planning, managing, and research by identifying major traffic patterns from the ITS data. The importance of data visualization as one of the useful data mining methods for reflecting the potential patterns of large sets of data has long been recognized in many disciplines. This paper will discuss several comprehensible and appropriate data visualization techniques, including line chart, bi-directional bar chart, rose diagram, and data image, as exploratory data analysis tools to explore traffic volume data intuitively and to discover the implicit and valuable traffic patterns. These methods could be applied at the same time to gain better and more comprehensive insights of traffic patterns and data relationships hidden in the massive data set. The visual exploratory analysis results could help transportation managers, engineers, and planners make more efficient and effective decisions on the design of traffic operation strategies and future transportation planning scientifically.