請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21943完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 溫在弘(Tzai-Hung Wen) | |
| dc.contributor.author | Hao-Sheng Fang | en |
| dc.contributor.author | 方皓聖 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:54:17Z | - |
| dc.date.copyright | 2018-09-04 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-16 | |
| dc.identifier.citation | Aarts, L., van Schagen, I., 2006. Driving speed and the risk of road crashes: A review. Accid. Anal. Prev. 38, 215–224.
Aguero-Valverde, J., Jovanis, P.P. (2008) Analysis of road crash frequency with spatial models. Transportation Research Record 2061, 55–63. Elvik, R., Christensen, P., Amundsen, A., (2004) Speed and road accidents. An evaluation of the Power Model. TØI report 740/2004. Institute of Transport Economics TOI, Oslo. Guo, F., Wang, X., Abdel-Aty, M. (2010) Modeling signalized intersection safety with corridor spatial correlations. Accident Analysis and Prevention 42 (1), 84–92. Hanna, R., Kreindler G., Olken, B.A. (2017) Citywide effects of high-occupancy vehicle restrictions: Evidence from “three-in-one” in Jakarta. Science 357(6346):89–93. Jones, B., Janssen, L., Mannering, F. (1991) Analysis of the frequency and duration of freeway accidents in Seattle. Accident Analysis and Prevention 23 (2), 239–255. Johansson, P. (1996) Speed limitation and motorway casualties: a time series count data regression approach. Accident Analysis and Prevention 28 (1), 73–87. Jovanis, P.P., Chang, H.L. (1986) Modeling the relationship of accidents to miles traveled. Transportation Research Record 1068, 42–51. Kloeden, C.N., McLean, A.J., Glonek, G., 2002. Reanalysis of travelling speed and the rate of crash involvement in Adelaide South Australia. Report No. CR 207. Australian Transport Safety Bureau ATSB, Civic Square, ACT. Krogh, A., Larsson, B., von Heijne, G., Sonnhammer, E. L. (2001). Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580. Kulp, D., Haussler, D., Reese, M. G. & Eeckman, F. H. (1996) A generalized Hidden Markov Model for the recognition of human genes in DNA. In Proceedings of the Fourth International Conference on Intelligent System for Molecular Biology.AAAI Press, Menlo Park, CA. Kupiec, Julian. (1992). Robust part-of-speech tagging using a hidden Markov model. Computer Speech and Language, 6. Li, X., Lord, D., Zhang, Y. (2009) Development of accident modification factors for rural frontage road segments in Texas using results from generalized additive models. Working Paper, Zachry Department of Civil Engineering, Texas A&M University, College Station, TX. Lord, D., Persaud, B.N. (2000) Accident prediction models with and without trend: application of the generalized estimating equations procedure. Transportation Research Record 1717, 102–108. Lord, D., Guikema, S., Geedipally, S.R. (2008) Application of the Conway–Maxwell–Poisson generalized linear model for analyzing motor vehicle crashes. Accident Analysis and Prevention 40 (3), 1123–1134. Lord, D., Miranda-Moreno, L.F. (2008) Effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter of Poisson-gamma models for modeling motor vehicle crashes: a Bayesian perspective. Safety Science 46 (5), 751–770. Lord, D., Mannering, F. (2010) The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transportation Research Part A 44 (5), 291–305. Maycock, G., Hall, R.D. (1984) Accidents at 4-Arm Roundabouts. TRRL Laboratory Report 1120, Transportation and Road Research Laboratory, Crowthorne, UK. McKinney SA, Joo C, Ha T. (2006) Analysis of single-molecule FRET trajectories using hidden Markov modeling. Biophys. J. 91:1941–51 Miaou, S.-P., Lum, H. (1993) Modeling vehicle accidents and highway geometric design relationships. Accident Analysis and Prevention 25 (6), 689–709. Miaou, S.-P. (1994) The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accident Analysis and Prevention 26 (4), 471–482. Miaou, S.-P., Lord, D. (2003) Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus Empirical Bayes. Transportation Research Record 1840, 31–40. Miaou, S.-P., Bligh, R.P., Lord, D. (2005) Developing median barrier installation guidelines: a benefit/cost analysis using Texas data. Transportation Research Record 1904, 3–19. Nilsson, G., 2004. Traffic safety dimensions and the power model to describe the effect of speed on safety. Bulletin 221, Lund Institute of Technology, Lund. Nguyen, N. T., Phung, D. Q., Venkatesh, S., Bui, H. H. (2005) Learning and detecting activities from movement trajectories using the hierarchical hidden Markov models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Vol. 2. 955–960. Rabiner, L. R. (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, vol. 77, 257-286. Shankar, V., Milton, J., Mannering, F.L. (1997) Modeling accident frequency as zero-altered probability processes: an empirical inquiry. Accident Analysis and Prevention 29 (6), 829–837. Shankar, V.N., Albin, R.B., Milton, J.C., Mannering, F.L. (1998) Evaluating median cross-over likelihoods with clustered accident counts: an empirical inquiry using random effects negative binomial model. Transportation Research Record 1635,44–48. Sonnhammer, E. L., von Heijne, G., Krogh, A. (1998). A hidden Markov model for predicting transmembrane helices in protein sequences. Proc. Int. Conf. Intell. Syst. Mol. Biol. 6, 175–182. Wang, C., Quddus, M.A., Ison, S. (2009) The effects of area-wide road speed and curvature on traffic casualties in England. Journal of Transport Geography 17(5), 385–395. Wang, X., Zhou, Q., Quddus, M., Fan, T., Fang, S. (2018) Speed, speed variation and crash relationships for urban arterials. Accident Analysis & Prevention, Volume 113, April 2018, Pages 236-243. Wen, T. H., Chin, W. C., & Lai, P. C. (2017) Understanding the topological characteristics and flow complexity of urban traffic congestion. Physica A: Statistical Mechanics and its Applications, 473(1), 166–177. WHO (World Health Organization). (2015) Global status report on road safety 2015. Available: http://www.who.int/violence_injury_prevention/road_safety_status/2015/en/ WHO (World Health Organization). (2018) The top 10 causes of death. Available: http://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death Wu, J., Wang C., Wang, X., He X. (2018) Application of the hidden Markov model in a dynamic risk assessment of rainstorms in Dalian, China. Stoch Env Res Risk Assess. Xie, Y., Zhang, Y. (2008) Crash frequency analysis with generalized additive models. Transportation Research Record 2061, 39–45. Yamato J., Ohya J., Ishii K. (1992) Recognizing human action in time-sequential images using Hidden Markov Model, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 379–385. Yip CF, Ng WL, Yau CY. (2017) A hidden Markov model for earthquake prediction. Stoch Env Res Risk Assess. 臺北市政府交通局。(2018) 當期交通統計月報。 臺北市政府資料開放平台。 (2016) 臺北市事故點座標97-102年。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21943 | - |
| dc.description.abstract | 對於連年增長的交通事故事件數，研究者致力於更進一步地了解交通事故發生的可能因素。然而，因為沒有更多詳細資料能夠了解個別事故發生的原因，多數研究者投入於了解影響交通事故發生頻率的因素，透過統計方法來解決此問題。因此，本研究對於此領域提出一個新的蒐集資料方法，可應用於交通事故，將Google Maps內部蒐集的路網車速資料，以應用程式介面(Application Programming Interface; API)提取出來，並將它與隱藏式馬可夫模型做結合，進行隱藏狀態(交通事故發生率)的辨識與預測。
本研究將臺北市於2008-2013年發生的交通事故點位置，編入臺北市的路網結構內，以此分析每條路段上發生交通事故的時序資料，再與車速資料做對比並估計機率參數來建立模型。研究結果顯示，以此方法預測交通事故的發生率，在交通事故發生較多的路段上有很好的表現。且對於界定隱藏狀態的臨界值，因應不同路段給定不同的臨界值，能對於模型的預測強度再提升。本研究的應用可延伸為評估路段的風險指數，為都市風險管理者或即時導航系統做出決策。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:54:17Z (GMT). No. of bitstreams: 1 ntu-107-R05h41008-1.pdf: 2145029 bytes, checksum: 91a349b16cbbf7646c295048a5a3ecca (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 謝辭 i
摘要 ii Abstract iii 目錄 iv 圖目錄 vi 表目錄 vii 第一章 緒論 1 第二章 文獻回顧 3 2.1 行車速度與交通事故的關聯性 3 2.2 交通事故頻率資料的統計建模方法 3 2.3 小結 5 第三章 隱藏式馬可夫模型 7 3.1 馬可夫鏈 7 3.2 隱藏式馬可夫模型的基本元素 9 3.3 三類基本問題 10 3.3.1 評估問題(Evaluation Problem) 11 3.3.2 解碼問題(Decoding Problem) 13 3.3.3 訓練問題(Training Problem) 15 第四章 研究方法 16 4.1 研究架構 16 4.2 研究地區 16 4.3 資料處理 17 4.3.1 道路路網資料 17 4.3.2 交通事故座標資料 17 4.3.3 路段車速資料 19 4.3.4 HMM的觀測符號 21 4.3.5 HMM的隱藏狀態 22 第五章 研究成果 27 5.1 原始資料 27 5.1.1 車速各狀態占比 27 5.1.2 交通事故狀態分類的臨界值 27 5.2 模型預測率 28 第六章 討論 32 6.1 對於研究結果的討論 32 6.2 對於整體研究的討論 32 第七章 結論 34 參考文獻 35 附錄 40 | |
| dc.language.iso | zh-TW | |
| dc.title | 隱藏式馬可夫模型於交通事故發生率的預測 | zh_TW |
| dc.title | A Hidden Markov Model for Predicting the Incidence of Traffic Accidents | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林楨家(Jen-Jia Lin),余清祥(Jack C. Yue),黃崇源(Chung-Yuan Huang),蔡宇軒(Yu-Shiuan Tsai) | |
| dc.subject.keyword | Google Maps API,交通事故,交通事故頻率資料,隨機過程,隱藏式馬可夫模型, | zh_TW |
| dc.subject.keyword | Crash-Frequency Data,Google Maps API,Hidden Markov Model,Stochastic Process,Traffic Accidents, | en |
| dc.relation.page | 42 | |
| dc.identifier.doi | 10.6342/NTU201803771 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2018-08-16 | |
| dc.contributor.author-college | 共同教育中心 | zh_TW |
| dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
| 顯示於系所單位： | 統計碩士學位學程 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-107-1.pdf 未授權公開取用 | 2.09 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。