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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8714


    Title: Collaborative real-time traffic information generation and sharing framework for the intelligent transportation system
    Authors: Lee, Wei-Hsun;Tseng, Shian-Shyong;Shieh, Wern-Yarng
    Contributors: Department of Information Science and Applications
    Keywords: Highway traffic control;Information dissemination;Intelligent vehicle highway systems;Knowledge based systems;Location;Time sharing systems;Vehicle locating systems;Advance traveler information systems;Advanced traffic management systems;Automatic agents;Collective intelligence;Front-end devices;Intelligent transportation system (ITS);Intelligent transportation systems;Knowledge base system;Location-aware;Real time traffics;Real-time traffic information;Smart traffic;Smart traffic agent;Time issues;Traffic event;Traffic information;Traffic status prediction;User-centric
    Date: 2010-01
    Issue Date: 2010-04-07 21:34:05 (UTC+8)
    Publisher: Asia University
    Abstract: Real-time traffic information collection and data fusion is one of the most important tasks in the advanced traffic management system (ATMS), and sharing traffic information to users is an essential part of the advance traveler information system (ATIS) among the intelligent transportation systems (ITS). Traditionally, sensor-based schemes or probing-vehicle based schemes have been used for collecting traffic information, but the coverage, cost, and real-time issues have remained unsolved. In this paper, a wiki-like collaborative real-time traffic information collection, fusion and sharing framework is proposed, which includes user-centric traffic event reacting mechanism, and automatic agent-centric traffic information aggregating scheme. Smart traffic agents (STA) developed for various front-end devices have the location-aware two-way real-time traffic exchange capability, and built-in event-reporting mechanism to allow users to report the real-time traffic events around their locations. In addition to collecting traffic information, the framework also integrates heterogeneous external real-time traffic information data sources and internal historical traffic information database to predict real-time traffic status by knowledge base system technique. © 2009.
    Relation: Source title: Information Sciences 180 (1) :62-70
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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