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研究生:黃琦琇
研究生(外文):Huang, Chi-Hsiu
論文名稱:應用基因規劃法探討高速公路交通肇事次數之研究
論文名稱(外文):Analysis of Freeway Accident Frequencies Using Genetic Programming
指導教授:張立言張立言引用關係
指導教授(外文):Chang, Li-Yen
學位類別:碩士
校院名稱:國立嘉義大學
系所名稱:行銷與運籌研究所
學門:商業及管理學門
學類:行銷與流通學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:71
中文關鍵詞:高速公路肇事負二項迴歸基因規劃法平均絕對百分比誤差
外文關鍵詞:Accident frequencyNegative binomial RegressionGenetic programmingMean absolute percentage error
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在台灣,高速公路為南北運輸的主要道路,一旦高速公路上發生交通事故,除了高速碰撞下可能造成嚴重的人員傷亡和龐大的財物損失外,事故所造成的交通阻塞也會造成整體道路服務水準的降低和社會成本的增加,因此本研究將針對高速公路交通事故的影響因素進行分析,以減少甚或避免事故的發生。由於交通事故多被視為隨機事件,而高速公路肇事因素分析的方式,過去多以統計方法-參數型迴歸模式為主,然而近年來非參數型模式被廣泛應用在各種領域,但相對於在運輸安全領域上的應用卻較為少見;因此本研究除了使用參數型迴歸模式外,並嘗試加入非參數型模式-基因規劃法作為交通肇事次數分析的研究方法,並探討非參數型模式在交通安全研究上的可行性。
本研究篩選事故資料庫為民國九十六年一月至民國九十七年十二月,共計兩年發生在國道一號高速公路主線上之事故資料做為研究範圍,並透過負二項迴歸(Negative Binomial Regression)與基因規劃法(Genetic Programming)進行模式的建構,分析非駕駛人相關因素與高速公路交通事故發生之關係,並利用平均絕對百分比誤差(Mean Absolute Percentage Error)比較兩模式結果之差異。研究結果顯示,負二項迴歸與基因規劃法在國道肇事次數分析結果上,其曲度、交流道、戰備道、車道數、平均日交通量、重型車高交通量及年降雨量上均對於肇事次數有顯著影響。而兩模式在預測誤差比較上,其平均絕對百分比誤差的值均落在合理的預測範圍(MAPE為20-50%)內,但基因規劃法的預測誤差明顯小於負二項迴歸之預測結果,顯示基因規劃法其預測能力優於負二項迴歸模式。
This study aims to understand the relationship between the non-drivers’ factors and the freeway accident frequencies. In the past, the statistical models such as regression analysis have been widely applied to identify the risk factors for vehicular accidents. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. On the other hand, non-parametric models which do not require any pre-defined underlying relationship between target(dependent)and predictors(independent variables)have been commonly applied in business administration, industry, and engineering. They have been shown to be effective tools to deal with classification and prediction problems. This study collected the 2007-2008 accident data of National Freeway 1 in Taiwan. The non-parametric model, genetic programming model, and the statistical model, negative binomial regression model, were employed to establish the empirical relationship between traffic accidents and highway geometric and environmental factors. The genetic programming and negative binomial regression models have similar findings; indicating the curve, interchange, military road, the number of lanes, average daily traffic volume, the high traffic volune of heavy vehicles, and annual rainfall were the key determinants for freeway accident frequencies. By comparing the prediction performance between genetic programming and negative binomial regression models, this study demonstrates that genetic programming is a good alternative method for analyzing freeway accident frequencies.
誌謝 I
摘要 II
Abstract III
目錄 IV
表目錄 VI
圖目錄 VII
第一章、緒論 5
1.1 研究背景與動機 5
1.2 研究目的 6
1.3 研究範圍與對象 7
1.4 研究架構與流程 7
第二章、文獻回顧 10
2.1 非駕駛人因素之交通事故相關文獻 10
2.2 卜瓦松迴歸與負二項迴歸模型相關文獻 15
2.3 基因規劃法相關文獻 19
2.4 小結 23
第三章、研究方法 24
3.1 卜瓦松迴歸模式 24
3.2 負二項迴歸模式 26
3.3 基因規劃法 29
3.4 模式預測正確率 38
3.4.1 預測正確率 38
3.4.2 預測誤差之指標 39
第四章、資料蒐集 41
4.1 資料來源與範圍 41
4.2 資料整理 43
4.3 變數定義與說明 46
第五章、模式建構與分析 47
5.1 敘述性統計分析 47
5.1.1 高速公路事故資料統計表 47
5.1.2 路段肇事次數分佈 48
5.1.3 訓練樣本與測試樣本 48
5.2 負二項迴歸模式 49
5.2.1 負二項迴歸模式建立 49
5.2.2 負二項迴歸模式變數說明與結果 50
5.3 基因規劃法模式 54
5.3.1 基因規劃法模式建立 54
5.3.2 基因規劃法模式分析結果 58
5.4 負二項迴歸模式與基因規劃法比較 60
5.4.1 變數比較 60
5.4.2 預測正確率比較 62
第六章、結論與建議 64
6.1 結論 64
6.2 建議 66
參考文獻 67
附錄一 70
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