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研究生(外文):Huang, Chi-Hsiu
論文名稱(外文):Analysis of Freeway Accident Frequencies Using Genetic Programming
指導教授(外文):Chang, Li-Yen
外文關鍵詞:Accident frequencyNegative binomial RegressionGenetic programmingMean absolute percentage error
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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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