中大機構典藏-NCU Institutional Repository-提供博碩士論文、考古題、期刊論文、研究計畫等下載:Item 987654321/71248
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 66984/66984 (100%)
Visitors : 22602578      Online Users : 166
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/71248


    Title: 鐵路事故危險程度分析;The Analysis of Dangerous Degrees For Railway Accidents
    Authors: 吳杰安;Wu,Chieh-An
    Contributors: 土木工程學系
    Keywords: 鐵路事故;機車故障;卜瓦松迴歸;卜瓦松迴歸;負二項迴歸
    Date: 2016-09-13
    Issue Date: 2016-10-13 12:15:10 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 鐵路運輸是在陸上運輸系統中最可靠的系統,但一發生事故就會造成嚴重影響。根據台鐵過去的統計資料,機務事故當中與旅客息息最切身相關的車輛事故每年占了整體事故超過4成,高居台鐵運務、工務、機務、電務四大處之首。因此本研究目的在建立一套車輛故障的分析模式以用來預測未來可能發生的肇事情況,作為改善參考。本研究蒐集台鐵機務部門2010 年至2014 年五年間之事故資料,利用一般線性模式中的卜瓦松迴歸及負二項迴歸,針對鐵路行車特性及軌道幾何等等不同特性,構建鐵路事故次數與故障次數模式預測。
    研究結果發現,事故次數模式與故障次數模式適合以卜瓦松迴歸預測事故,同時尖離峰跟平假日不同的乘載條件下對事故有顯著影響。
    ;Railway transport is the most reliable system in the land transportation system, but it may cause serious effect while the accidents happen. According to the Taiwan Railway Administration’s former statistic data, rolling stock accident accounted for above 40% of the overall rail accidents which closely related to the passenger. Therefore, the purpose of the study aims to establish rolling stock breakdowns analysis model to predict the future accidents. The study collecting the Taiwan Railway Administration five years of accident data from 2010 to 2014, using general linear models of Poisson and Negative Binomial Regression, in relation to railway travel characteristics, track geometry and so forth. Constructing the railway accident frequency model and failed mode to predict accident in the future. .
    The result indicated that railway accidents’ frequency model and failed model are suitable to using Poisson Regression is better than Binomial Regression to predict accidents in the future. The peak/off-peak hours and rush /general hours under different loading conditions have significant influence on accident.
    Appears in Collections:[Graduate Institute of Civil Engineering] Electronic Thesis & Dissertation

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML238View/Open


    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明