Context-Aware Ubiquitous Data Mining Based Agent Model for Intersection Safety

Publication Year: 2005 Publication Type : ConferenceProceeding


In USA, 2002, approximately 3.2 million intersection-related crashes occurred, corresponding to 50 percent of all reported crashes. In Japan, more than 58 percent of all traffic crashes occur at intersections. With the advances in Intelligent Transportation Systems, such as off-the-shelf and in-vehicle sensor technology, wireless communication and ubiquitous computing research, safety of intersection environments can be improved. This research aims to investigate an integration of intelligent software agents and ubiquitous data stream mining, for a novel context-aware framework that is able to: (1) monitor an intersection to learn for patterns of collisions and factors leading to a collision; (2) learn to recognize potential hazards in intersections from information communicated by road infrastructures, approaching and passing vehicles, and external entities; (3) warn particular threatened vehicles that are approaching the intersection by communicating directly to the in-vehicle system.


    author = {Flora Dilys Salim and Shonali Krishnaswamy and Seng Wai Loke and Andry Rakotonirainy},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    booktitle = {Embedded and Ubiquitous Computing - EUC 2005 Workshops, EUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES, Nagasaki, Japan, December 6-9, 2005, Proceedings},
    doi = {10.1007/11596042_7},
    editor = {Tomoya Enokido and Lu Yan and Bin Xiao and Daeyoung Kim and Yuan-Shun Dai and Laurence Tianruo Yang},
    pages = {61--70},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    timestamp = {Mon, 16 Sep 2019 01:00:00 +0200},
    title = {Context-Aware Ubiquitous Data Mining Based Agent Model for Intersection Safety},
    url = {},
    volume = {3823},
    year = {2005}


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