鐵路平交道事故危險程度之分析; The analysis of dangerous degrees for railway level crossing accidents

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Title:	鐵路平交道事故危險程度之分析;The analysis of dangerous degrees for railway level crossing accidents
Authors:	黃俊福;Chun-fu Huang
Contributors:	土木工程研究所
Keywords:	卜瓦松迴歸;平交道事故;平交道;負二項迴歸;類神經倒傳遞網路;Back-Propagation Neural Network;Negative Binomial regression;Poission regression;Level crossing accidents;Level crossing
Date:	2009-06-18
Issue Date:	2009-09-18 17:27:25 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	鐵路運輸是在陸上運輸系統中肇事率最小的運輸系統，但往往每當發生事故輕者阻礙交通、重者車毀人亡，其中又以鐵路與公路二大運輸系統之平面交會處的平交道最易發生事故，而平交道事故也最常帶來極大生命及財產損失，因此本研究其目的在於建立一套鐵路平交道事故之分析模式可以用來預測未來可能發生肇事情形，也可以藉此了解鐵路平交道事故之相關因素。本研究透過民國87 年台灣省政府交通處出版一套四冊之平交道改善規劃，包括當時交通事故資料、平交道種類、道路幾何型態、列車班次數與平均每年每日交通量等曝光量資料，並藉由台灣鐵路局運務處部門民國91年至96年行車事故資料，整理成各處平交道事故資料，最後透過卜瓦松迴歸、負二項迴歸、類神經倒傳遞網路，三種模式分別來構建平交道事故次數、死亡人數、受傷人數三種預測模式，並找出最適模式及相關顯著影響因素以供未來相關鐵路單位做為未來肇事預測。研究結果發現，事故次數結果顯示以卜瓦松模式與負二項模式預測皆優於類神經倒傳遞網路預測結果；影響平交道相關模式的因素中，以全日列車班次為最重要影響因子。 In all kind of the land transportation, the railway transportation system has the lowest rate of accident. While once an accident is happened, it usually causes life and property loss. Especially the level crossing which is crossed between railway and highway. Slight accident will tie up traffic, and serious accident will cause death and car crash. This research forecasts the possiblity of accident happening by setting a railway crossing accident analysis model. Based on the research, we can realize more about the correlation factor. The research uses data from Crossing Improvement Plan published by Taiwan Provincial Government in 1998 as references. It consists of both historical accident data and railway level crossing related data in Taiwan, such as crossing types, highway geometric characteristics, daily trains and average annual daily traffic (AADT), etc. We try to reorganize the traffic accident data since 2002 to 2007, collected from Transportation Department, Taiwan Railway Administration, to the level crossing accident data. Finally, the research could be applied to Poisson Regression Model, Negative Binomial regression, and Back-Propagation Neural Network for evaluating the prediction of the level crossing accident frequency models, fatalities models, and hurt models then to find the fittest model with the major factor which is relative significantly. It can provide the relation with the TRA for forecasting accident in the future. As a consequence of this research, we can find Poisson Regression Model and Negative Binomial regression are better than Back-Propagation Neural Network. The most significant factor of effect on level crossing model is the number of pass trains.
Appears in Collections:	[Graduate Institute of Civil Engineering] Electronic Thesis & Dissertation

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