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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2006  >  6(3): 47-50
ZOU Guo-ping, ZOU Qun, HUANG Zheng. Architecture of expressway maintenance management system based on SDSS[J]. Journal of Traffic and Transportation Engineering, 2006, 6(3): 47-50.
Citation: ZOU Guo-ping, ZOU Qun, HUANG Zheng. Architecture of expressway maintenance management system based on SDSS[J]. Journal of Traffic and Transportation Engineering, 2006, 6(3): 47-50.
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Architecture of expressway maintenance management system based on SDSS

  • 1.

    Jiangxi Ganyue Expressway Co.Ltd., Nanchang 330025, Jiangxi, China

  • 2.

    Department of Hydraulic and Civil Engineering, Nanchang Institute of Technology, Nanchang 330029, Jiangxi, China

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  • Author Bio:

    Zou Guo-ping(1967-), male, PhD, senior engineer, 86-791-6526386, zgping@jXjt.gov.cn

  • Received Date: 2005-12-09
  • Publish Date: 2006-09-25
    Abstract
  • Present expressway maintenance management system (EMMS) is a simple decision support system (DSS), its decision-making is drived by model, and its intelligent level of decision-making is lower.The EMMS based on synthetic decision support system (SDSS) was presented in order to improve the ability and intelligent level of expressway maintenance decision-making, it integrated intelligence decision support system (IDSS) and business intelligence (BI) technology, and drived the system decision-making by model, knowledge and data.The system is composed of data warehouse (DW), on line analytical processing (OLAP), data mining (DM), model storehouse, knowledge storehouse and database.DW can store and synthetize the data of decision theme, OLAP can realize multi-dimension data analysis, DM can mine the knowledge from database and DW, model storehouse can realize combination assistant decision-making by many generalized models, database can provide data for assistant decision-making, expert system (ES) can realize qualitative analysis by knowledge reasoning.The integrated system can exert its each advantage, and realize more effective assistant decision-making by their mutual makeup and dependence.

     

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