00965457

Component

https://doi.org/10.3141/1836-14

http://scholar.google.com...Kwon&publication_year=2003

Intelligent transportation system (ITS) sensor networks, such as road weather information and traffic sensor networks, typically generate enormous amounts of data. As a result, archiving, retrieval, and exchange of ITS sensor data for planning and performance analysis are becoming increasingly difficult. An efficient ITS archiving system that is compact and exchangeable and allows efficient and fast retrieval of large amounts of data is essential. A proposal is made for a system that can meet the present and future archiving needs of large-scale ITS data. This system is referred to as common data format (CDF) and was developed by the National Space Science Data Center for archiving, exchange, and management of large-scale scientific array data. CDF is an open system that is free and portable and includes self-describing data abstraction. Archiving traffic data by using CDF is demonstrated, and its archival and retrieval performance is presented for the Minnesota Department of Transportation's 30-s traffic data collected from about 4,000 loop detectors around Twin Cities freeways. For comparison of the archiving performance, the same data were archived by using a commercially available relational database, which was evaluated for its archival and retrieval performance. This result is presented, along with reasons that CDF is a good fit for large-scale ITS data archiving, retrieval, and exchange of data.

This paper appears in Transportation Research Record No. 1836, Initiatives in Information Technology and Geospatial Science for Transportation.

English

p. 111-117

2003

Transportation Research Record

0309085721

References (10) ; Tables (4)

Data banks; Data storage; Information retrieval; Intelligent transportation systems; Self describing data; Traffic data

Minnesota Department of Transportation

Common data format; Data portability

Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Nov 7 2003 12:00AM

• ADJUSTING TRIP RATES IN THE CROSS-CLASSIFICATION TABLE BY USING THE FUZZY OPTIMIZATION METHOD
• APPLICATION OF LIGHT DETECTION AND RANGING TECHNOLOGY TO HIGHWAY SAFETY
• BILEVEL FORMULATION FOR OPTIMAL TRAFFIC-INFORMATION DISSEMINATION
• BUILDING AND MAINTAINING A STATEWIDE TRANSPORTATION FRAMEWORK
• DESIGN OF ARCHIVAL TRAFFIC DATABASES: QUANTITATIVE INVESTIGATION INTO APPLICATION OF ADVANCED DATA MODELING CONCEPTS
• DEVELOPED INCIDENT DETECTION ALGORITHM COMPARED WITH NEURAL NETWORK ALGORITHMS
• EXPLORING IMPUTATION TECHNIQUES FOR MISSING DATA IN TRANSPORTATION MANAGEMENT SYSTEMS
• FUZZY-NEURAL NETWORK TRAFFIC PREDICTION FRAMEWORK WITH WAVELET DECOMPOSITION
• INTEGRATION OF LIGHT DETECTION AND RANGING TECHNOLOGY WITH PHOTOGRAMMETRY IN HIGHWAY LOCATION AND DESIGN
• INTEGRATION OF SPATIAL POINT FEATURES WITH LINEAR REFERENCING METHODS
• LEARNING A CAUSAL MODEL FROM HOUSEHOLD SURVEY DATA BY USING A BAYESIAN BELIEF NETWORK
• MODULAR ARTIFICIAL NEURAL NETWORKS FOR SOLVING THE INVERSE TRANSPORTATION PLANNING PROBLEM
• OPTIMIZATION OF DYNAMIC NEURAL NETWORK PERFORMANCE FOR SHORT-TERM TRAFFIC PREDICTION
• REAL-TIME COORDINATED SIGNAL CONTROL THROUGH USE OF AGENTS WITH ONLINE REINFORCEMENT LEARNING
• ROUGH SETS AND PROBABILITY MASSES FOR DEMPSTER-SHAFER DATA FUSION AT A TRAFFIC MANAGEMENT CENTER
• SPATIOTEMPORAL OBJECT DATABASE APPROACH TO DYNAMIC SEGMENTATION
• TOWARD MORE EFFECTIVE TRANSPORTATION APPLICATIONS OF COMPUTATIONAL INTELLIGENCE PARADIGMS
• USE OF LOCAL LINEAR REGRESSION MODEL FOR SHORT-TERM TRAFFIC FORECASTING