跳到主要內容

臺灣博碩士論文加值系統

(216.73.216.246) 您好!臺灣時間:2026/07/01 07:27
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:王嵐
研究生(外文):Wang, Lan
論文名稱:高速公路行車動態對事故發生過程之影響分析
論文名稱(外文):Analyzing the impacts of traffic states on freeway rear-end crash sequences
指導教授:吳昆峯
指導教授(外文):Wu, Kun-Feng
口試委員:吳昆峯張新立邱裕鈞鍾易詩
口試委員(外文):Wu, Kun-FengChang, Hsin-LiChiou, Yu-ChiunChung, Yi-Shih
口試日期:2018-07-27
學位類別:碩士
校院名稱:國立交通大學
系所名稱:運輸與物流管理學系
學門:運輸服務學門
學類:運輸管理學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:60
中文關鍵詞:行車動態自然駕駛研究序列式結構模型
外文關鍵詞:Traffic stateNaturalistic driving studySequential model
相關次數:
  • 被引用被引用:0
  • 點閱點閱:380
  • 評分評分:
  • 下載下載:7
  • 收藏至我的研究室書目清單書目收藏:1
過去探討高速公路車流對事故發生之影響的相關研究,即使可證實在某些車流狀態下,具有較高的事故風險,但巨觀之車流資訊並無法用以解釋實際之車輛互動行為與事故發生過程,此外過去之研究大多致力於找出與事故相關之影響因素,卻鮮少對於事故發生過程中不同因素之連結與在時間上的演變關係進行研究。自然駕駛研究(Naturalistic Driving Study)提供了絕佳的機會能以更微觀及更多不同面相了解事故與近似事故(Near Crash)的發生,本研究運用NDS資料分類出行經不同車流狀態時的行車動態,並提出利用序列式結構之模型連結行車動態與其他因素在事故發生過程不同階段中的影響。研究結果發現高速公路追撞事故發生的過程與機制是在不同的行車動態情況下,因為容易觸發不同的車輛行為例如前車減速或變換車道,而在此同時當駕駛受到其他因素例如分心的影響時,感知到該情況的時間便會增加,因而可能反應不及進而造成事故之發生。
According to the findings of previous research, there are relations between traffic flow and crash occurrences on freeway However, the actual behaviors and how crashes happened under different traffic state cannot be clearly verified and explained.
Previous studies have indicated many factors are associated with rear-end crash risk or freeway crash, but the connections between factors and the contribution to the progression of a freeway rear-end crash are scarcely been discussed. Naturalistic Driving Study provides a great opportunity to better understand the causality of crash and near crash. This study utilize NDS data to define behavioral-based microscopic traffic states, then develop a sequential model to evaluate the impacts of traffic states and other factors on the progression of a crash.The model results shows that the mechanism of a crash happened is that under different traffic states, different driving behaviors(events) are more likely to happen(for instance, vehicle ahead decelerating versus other vehicle lane change).When the event trigger, if at the same time the driver is distracted , driver’s perception time will be increased, which may finally causing the driver not able to react in time, eventually evolving into a crash.
中文摘要 …………………………………………………………………………… I
英文摘要 ………………………………………………………………………… II
誌謝 ……………………………………………………………………… III
目錄 ………………………………………………………………………… IV
圖目錄 ………………………………………………………………………… V
表目錄 ………………………………………………………………………… VI
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研究範圍與對象 ……………………………………………………………………3
1.4研究架構與流程 ……………………………………………………………………4
第二章 文獻回顧 ……………………………………………………………………6
2.1高速公路追撞事故影響因素 …………………………………………6
2.2高速公路車流狀態與事故發生率…………………………………………7
2.3自然駕駛研究分析…………………………………………………………………… 8
2.4風險指標 ……………………………………………………………………9
第三章 研究方法 …………………………………………………………………11
3.1事故過程影響因素分析……………………………………………………………11
3.2研究模型 ……………………………………………………………………13
第四章 資料介紹與整理……………………………………………………………………17
4.1資料介紹 ……………………………………………………………………17
4.2資料蒐集與項目 ……………………………………………………………………19
4.3資料處理 ……………………………………………………………………21
4.3.1時間序列資料處理……………………………………………………………………21
4.3.2事件屬性資料處理……………………………………………………………………29
第五章 研究結果與分析 ……………………………………………………………………31
5.1行車動態分類結果 ……………………………………………………………………31
5.2模型結果與分析 ……………………………………………………………………33
5.2.1模型比較 ……………………………………………………………………35
5.2.2單層結構模型 ……………………………………………………………………39
5.2.3序列式結構模型 ……………………………………………………………………46
第六章 結論與建議 ……………………………………………………………………54
6.1結論與研究應用 ……………………………………………………………………54
6.2建議 ……………………………………………………………………55
參考文獻 ……………………………………………………………………57
簡 歷 ………………………………………………………………………… 60
Abdelwahab,H.,& M.A.Abdel-Aty.(2001). Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections. Transportation Research Record,1746: p. 6-13.
Abdel-Aty, M., Uddin, N., Abdalla, F., Pande, A.,&Hsia,L.(2004). Predicting freeway crashes based on loop detector data using matched case-control logistic regression. Transportation Research Record 1897, 88–95.
Abdel-Aty,M., Uddin, N.,& Pande, A.(2005).Split models for predicting multi-vehicle crashes during high-speed and low-speed operating conditions on freeways. Transportation Research Record 1908, 51–58.
Charlton,J.L., Catchlove,M., Scully,M., Koppel,S., &Newstead,S.(2013). Older driver distraction: a naturalistic study of behaviour at intersections. Accident Analysis and Prevention 58, 271–278.
Currie, L.(1969). The perception of danger in a simulated driving task. Ergonomics 12 (6), 841–849
Davis,G.A., &Swenson,T.(2006). Collective responsibility for freeway rear-ending accidents: an application of probabilistic causal models. Accident Analysis and Prevention, 38 (4), 728–736.
Dozza,M.,Bärgman,J.&Lee,J.D.(2013). Chunking: a procedure to improve naturalistic data analysis. Accident Analysis and Prevention,58, 309–317.
Firth, D.(1993) Bias reduction of maximum likelihood estimates, Biometrika, 80(1): 27-38.
Guo, F.,Klauer,K.G.,Hankey,J.M.,& Dingus,T.A.(2010). Near crashes as crash surrogate for naturalistic driving studies. Transportation Research Record: Journal Transportation Research Board 2147, 66–74.
Guo, F.,& Fang, Y.(2013). Individual driver risk assessment using naturalistic driving data. Accident Analysis and Prevention 61 , 3–9.
Golob, T. F., W. W. Recker, & V.M. Alvarez.(2004). Freeway Safety as a Function of Traffic Flow. Accident Analysis and Prevention, Vol. 36,No. 6, 2004, pp. 933–946.

Khattak, A.J., Khattak, A.J.,& Council, F.M.(2002). Effects of work zone presence on injury and non-injury crashes. Accident Analysis and Prevention 34 (1), 19–29.
Knipling, R.R.,& Wang, J.S. (1995). Revised estimates of the US drowsy driver crash problem size based on general estimates system case reviews. In: Proceedings of the39th Annual Association for the Advancement of Automotive Medicine, Chicago, IL, October 1995, pp. 451–466.
Leger, D.(1995). The cost of sleepiness: a response to comments. Sleep 18, 281–284.
Lee, C., Saccomanno, F.,& Hellinga, B.(2003). Real-time crash prediction model for the application to crash prevention in freeway traffic. Transportation Research Record 1840, 67–77.
Lyznicki, J.M., Doege, T.C., Davis, R.M.,& Williams, W.A.(1998). Sleepiness, driving, and motor vehicle crashes. J. Am. Med. Assoc. 279, 1908–1913.
McKnight, A.J.,& McKnight, A.S. (1993). The effect of cellular phone use upon driver attention. Accident Analysis and Prevention. 25 (3), 259–265.
Oh, C., Oh, J.,& Ritchie, S. (2001). Real-time estimation of freeway accident likelihood. In: Presented at 80th Annual Meeting of the Transportation Research Board,CD-ROM, Washington, D.C.
Pelz, D.C.,&Krupat, E.(1974). Caution profile and driving record of undergraduate males. Accident Analysis and Prevention. 6, 45–58
Quimby, A.R., Maycock, G., Carter, I.D., Dixon, R.,& Wall, J.G.(1986). Perceptual Abilities of Accident Involved Drivers. Transport and Road Research Laboratory, Crowthorne.
Rouphail,N.M.,Yang,Z.S.,&Fazio,J.(1988).Comparative study of short- and long-term urban freeway work zones. Transportation Research Record 1163, 4–14.
Salmon,P.M., Stanton,N.A, Walker,G.H., Jenkins,D., Ladva,D., Rafferty,L., & Young, M. (2009). Measuring situation awareness in complex systems: Comparison of measures study. Int J Ind Ergon 2009; 39:490–500
Shinar, D.(2007). Traffic Safety and Human Behavior. Elsevier.
Sparrow, A.R Mollicone, D.J., Kan,K.,Bartels, R.,Satterfield, B.C.,Samantha,M.R.,
Unice, R., & Dongena H. P.A.V.(2016). Naturalistic field study of the restart break in US commercial motor vehicle drivers: Truck driving, sleep, and fatigue. Accident Analysis and Prevention, Vol. 93, pp. 55–64.
Wang, J., Hughes, W.E., Council, F.M., & Paniati, J.F.(1996). Investigation of highway work zone crashes: what we know and what we don’t know. In: Transportation Research Record 1529. TRB, National Research Council, Washington, DC, pp. 54–62.
Wang, J.S., Knipling, R.R., & Goodman, M.J.(1996). The role of driver inattention in crashes: new statistics from the 1995 Crashworthiness Data System. In: Proceedings of the 40th Annual Association for the Advancement of Automotive Medicine, Vancouver, BC, pp. 377– 392.
Wu, K., & Jovanis, P.P.(2012). The relationship between crashes and crash-surrogate events in naturalistic driving data. Accident Analysis and Prevention 45, 507–516.
Wu, K., & Jovanis, P.P.(2013). Defining and screening crash surrogate events using naturalistic driving data. Accident Analysis and Prevention 61, 10–22
Wu, K., Donnell, E.T., Himes, S.C.,& Sasidharan,L.(2013). Exploring the Association between Traffic Safety and Geometric Design Consistency Based on Vehicle Speed Metrics. Journal of Transportation Engineering 139,738-748
Wu, K., & Thor, C.P.(2015). Method for using naturalistic driving study data to analyze rear-end crash sequences.Transportation Research Record: Journal Transportation Research Board 2518, 27-36
Wu, K., Thor, C.P., & Ardiansyah, N.(2016). Identify sequence of events likely to result in severe crash outcomes. Accident Analysis and Prevention, Vol. 96, pp.198–207.
Xu, C., Liu, P., Wang, W.,& Li, Z.(2012). Evaluation of the impacts of traffic states on crash risks on freeways. Accident Analysis and Prevention 47, 162–171.
Xu, C., Tarko, A.P., Wang, W., & Liu, P.(2013). Predicting crash likelihood and severity on freeways with real-time loop detector data. Accident Analysis and Prevention 57,30– 39.
Yeo, H., Jang, K., Skabardonis, A., & Kang, S.(2013). Impact of traffic states on freeway crash involvement rates. Accident Analysis and Prevention 50, 713–723
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top