客運司機偏差駕駛行為的影響因素之研究-階層線性模型之應用

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蘇晉煒(Jin-wei Su) 查詢紙本館藏

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土木工程學系

論文名稱

客運司機偏差駕駛行為的影響因素之研究-階層線性模型之應用
(Influential factors of the bus drivers’ aberrant driving behavior-The application of hierarchical linear model)

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摘要(中)

近年來，由於國內交通運輸需求快速成長，使得汽機車數量與日俱增，而衍生出許多道路交通安全的問題。儘管歷年A1類肇事事件數之車種，主要以機踏車為主。但若以平均數比較，根據101年資料，每一千輛大客車，便有0.93輛發生事故，位居第二，僅次於大貨車。由此可知，大客車可視為具有高肇事風險的車種類別。然而，又對於交通事故原因歸類，主要以汽(機、慢)車駕駛人過失為主。換言之，交通肇事的發生多來自於駕駛者本身因素所產生的駕駛偏差行為而導致。
本研究以台灣地區48家客運業者，挑選其中30家，共有863位駕駛者參與調查，以階層線性模型進行分析，旨在探討駕駛者偏差駕駛行為的影響因素，包括：安全氣候、內在壓力與不注意。其相關量測問卷，包括專門為駕駛者修正設計之安全氣候(SCQ-MD, Wills et al., 2005)、駕駛行為量表(DBI, Westerman & Haigney, 2000)、與注意力相關的錯誤駕駛之量尺(ARDES, Ledesma et al., 2010)、駕駛行為問卷(DBQ, Reason et al., 1990)。
其結果顯示，安全氣候、內在壓力、不注意、偏差駕駛行為，彼此間具有顯著的相關性；安全氣候→內在壓力→偏差駕駛行為、安全氣候→不注意→偏差駕駛行為、安全氣候→內在壓力→不注意、內在壓力→不注意→偏差駕駛行為，皆屬於部份中介效果；安全氣候×內在壓力→偏差駕駛行為、安全氣候×不注意→偏差駕駛行為、安全氣候×內在壓力→不注意，皆不具調節效果。針對上述結果，本研究亦提出相關安全政策的討論與未來研究議題的建議。

摘要(英)

In recent years, due to the rapid growth of the demand for transportation, making cars and motorcycles growing, and extended many traffic safety problems. The motorcycles in A1 accident the number of events is the largest. However, according to the data of 101 years, if compared to the average, each of one thousand buses will be 0.93 vehicles event of an accident, and this value is second only to the freight vehicles. Therefore, buses can be regarded as the types of vehicles having a high accident risk. However, traffic accident was mainly due to driver faults. In other words, the traffic accident occurred come from the the driver factors about aberrant driving behavior.
This study conducted a survey on 863 drivers over 30 buses from 48 buses in Taiwan, and used hierarchical linear model (HLM) to analysis data. Explore the influencing factors of drivers’ aberrant driving behavior (ADB) including safety climate (SC), stress and inattention. And about this study used measurement questionnaire including Safety Climate Questionnaire-Modified for Driver (SCQ-MD, Wills et al, 2005), Driving Behaviour Inventory (DBI, Westerman & Haigney, 2000), Attention-Related Driving Error Scale (ARDES, Ledesma et al., 2010), Driver Behavior Questionnaire (DBQ, Reason et al., 1990).
The results showed that SC, stress, inattention, ADB, are significantly correlated with each other; SC→stress→ADB, SC→inattention→ADB, SC→stress→inattention, stress →inattention→ADB had partial mediating effect; SC×stress→ADB, SC×inattention→ADB, SC×stress→inattention didn’t have moderating effect. According the results, This study proposes some discussions about safety policy and recommendations of the research issues for the future.

關鍵字(中)

★ 階層線性模型
★ 安全氣候
★ 內在壓力
★ 不注意
★ 偏差駕駛行為

關鍵字(英)

★ hierarchical linear model
★ safety climate
★ stress
★ inattention
★ aberrant driving behavior

論文目次

摘要 i
ABSTRACT ii
致謝 iii
目錄 iv
圖目錄 viii
表目錄 ix
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究步驟與流程 3
第二章文獻回顧 5
2.1 偏差駕駛行為 5
2.1.1 錯誤駕駛行為定義 5
2.1.2 偏差駕駛行為定義 5
2.1.3 駕駛者行為問卷與其相關研究 6
2.2 偏差駕駛行為之影響因素 7
2.2.1 內在壓力 7
2.2.2 駕駛行為量表與其相關研究 8
2.2.3 不注意 9
2.2.3.1 對偏差駕駛行為之重要性 10
2.2.3.2 不注意與分心之差別 10
2.2.4 與注意力相關的錯誤駕駛之量尺及其相關研究 14
2.2.5 內在壓力與不注意之相關性 16
2.3 安全氣候 16
2.3.1 安全氣候定義及其相關研究 16
2.3.2 專門為駕駛人修正設計之安全氣候問卷及其相關研究 18
第三章研究方法 20
3.1 研究架構與假設 20
3.1.1 內在壓力與不注意不相關 20
3.1.2 內在壓力與不注意有相關 21
3.2 研究變項之衡量與問卷設計 23
3.2.1 偏差駕駛行為之變項衡量 23
3.2.2 內在壓力之變項衡量 23
3.2.3 不注意之變項衡量 24
3.2.4 專門為駕駛者修正設計之安全氣候之變項衡量 24
3.3 研究方法 24
3.3.1 敘述性統計分析 24
3.3.2 項目分析與信效度分析 24
3.3.2.1 項目分析 24
3.3.2.2 信效度分析 25
3.3.3 階層線性模型 27
3.3.3.1 階層線性模型之信度檢測 27
3.3.3.2 階層線性模型之子模型 28
3.3.3.3 階層線性模型檢定流程 31
第四章項目分析與信效度分析 41
4.1 問卷資料蒐集 41
4.1.1 調查對象與樣本數之界定 41
4.1.2 預試階段 41
4.1.3 正式問卷發放階段 41
4.2 受測者基本資料分析 44
4.3 項目分析 48
4.3.1 DBQ之項目分析(以違法構面為例) 49
4.3.2 項目分析小結 50
4.4 信效度分析 50
4.4.1 DBQ之信效度分析 51
4.4.1.1 初步檢測結果 51
4.4.1.2 題項調整後之檢測結果 52
4.4.2 DBI之信效度分析 53
4.4.2.1 初步檢測結果 53
4.4.2.2 題項調整後之檢測結果 54
4.4.3 ARDES之信效度分析 55
4.4.3.1 初步檢測結果 55
4.4.3.2 題項調整後之檢測結果 56
4.4.4 SCQ-MD之信效度分析 56
4.4.4.1 初步檢測結果 57
4.5 階層線性模型之信度檢測 58
4.6 相關分析 59
第五章實證分析(一) 61
5.1 第一部份(安全氣候-內在壓力-偏差駕駛行為) 61
5.1.1 隨機效果單因子變異數分析模型(零模型)–模型0 61
5.1.2 中介效果檢定 61
5.1.2.1 以平均數為結果的迴歸模型–模型1 61
5.1.2.2 延伸隨機效果單因子共變異數分析模型–模型2 61
5.1.3 調節效果檢定 62
5.1.3.1 隨機係數迴歸模型–模型3 62
5.1.3.2 以截距與斜率為結果的迴歸模型(完整模型)–模型4 62
5.2 第二部份(安全氣候-不注意-偏差駕駛行為) 63
5.2.1 隨機效果單因子變異數分析模型(零模型)–模型0 63
5.2.2 中介效果檢定 63
5.2.2.1 以平均數為結果的迴歸模型–模型1 63
5.2.2.2 延伸隨機效果單因子共變異數分析模型–模型2 63
5.2.3 調節效果檢定 63
5.2.3.1 隨機係數迴歸模型–模型3 63
5.2.3.2 以截距與斜率為結果的迴歸模型(完整模型)–模型4 64
5.3 第三部份(安全氣候-內在壓力/不注意-偏差駕駛行為) 64
5.3.1 隨機效果單因子變異數分析模型(零模型)–模型0 64
5.3.2 中介效果檢定 65
5.3.2.1 以平均數為結果的迴歸模型–模型1 65
5.3.2.2 延伸隨機效果單因子共變異數分析模型–模型2a 65
5.3.3 調節效果檢定 65
5.3.3.1 隨機係數迴歸模型–模型3a 65
5.3.3.2 以截距與斜率為結果的迴歸模型(完整模型)–模型4a 65
5.4 討論 68
第六章實證分析(二) 71
6.1 第一部份(安全氣候-內在壓力-偏差駕駛行為) 71
6.1.1 隨機效果單因子變異數分析模型(零模型)–模型0 71
6.1.2 中介效果檢定 71
6.1.2.1 以平均數為結果的迴歸模型–模型1 71
6.1.2.2 延伸隨機效果單因子共變異數分析模型–模型2 71
6.1.3 調節效果檢定 72
6.1.3.1 隨機係數迴歸模型–模型3 72
6.1.3.2 以截距與斜率為結果的迴歸模型(完整模型)–模型4 72
6.2 第二部份(安全氣候-不注意-偏差駕駛行為) 73
6.2.1 隨機效果單因子變異數分析模型(零模型)–模型0 73
6.2.2 中介效果檢定 73
5.2.2.3 以平均數為結果的迴歸模型–模型1 73
5.2.2.4 延伸隨機效果單因子共變異數分析模型–模型2 73
6.3 第三部份(安全氣候-內在壓力/不注意-偏差駕駛行為) 73
6.3.1 隨機效果單因子變異數分析模型(零模型)–模型0 73
6.3.2 中介效果檢定 74
6.3.2.1 以平均數為結果的迴歸模型–模型1 74
6.3.2.2 延伸隨機效果單因子共變數分析模型–模型2a 74
6.3.3 調節效果檢定 74
6.3.3.1 隨機係數迴歸模型–模型3a 74
6.3.3.2 以截距與斜率為結果的迴歸模型(完整模型)–模型4a 74
6.4 第四部份(安全氣候-內在壓力-不注意) 75
6.4.1 隨機效果單因子變異數分析模型(零模型)–模型0 75
6.4.2 中介效果檢定 75
6.4.2.1 以平均數為結果的迴歸模型–模型1 75
6.4.2.2 延伸隨機效果單因子共變數分析模型–模型2 76
6.4.3 調節效果檢定 76
6.4.3.1 隨機係數迴歸模型–模型3 76
6.4.3.2 以截距與斜率為結果的迴歸模型(完整模型)–模型4 76
6.5 第五部份(內在壓力-不注意-偏差駕駛行為) 77
6.5 討論 81
第七章結論與建議 85
7.1 結論 85
7.1.1 研究分析結果總結 85
7.1.2 肇事案例討論 85
7.2 建議 89
7.2.1 理論應用之建議 89
7.2.1.1 階層線性模型之應用 89
7.2.1.2 安全氣候之應用 91
7.2.1.3 Sobel中介效果檢定之應用 91
7.2.2 實務應用之建議 92
附錄A (DBQ題項篩選) 98
附錄B (DBI題項篩選) 106
附錄C (ARDES題項篩選) 108
附錄D (SCQ-MD題項篩選) 112
附錄E (預試問卷) 116
附錄F (正式問卷) 121
附錄G (DBQ之項目分析) 126
附錄H (DBI之項目分析) 129
附錄I (ARDES之項目分析) 132
附錄J (SCQ-MD之項目分析) 133
附錄K (英文對照表) 139

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指導教授

陳惠國(Huey-Kuo Chen)

審核日期

2013-6-13

推文