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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 許添本(Tien-Pen Hsu) | |
dc.contributor.author | Wei-Chien Hsu | en |
dc.contributor.author | 徐維謙 | zh_TW |
dc.date.accessioned | 2021-06-07T23:47:10Z | - |
dc.date.copyright | 2020-08-25 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-10 | |
dc.identifier.citation | Aguero-Valverde, J. (2014). Direct spatial correlation in crash frequency models: estimation of the effective range. Journal of Transportation Safety Security, 6(1), 21-33. Aguero-Valverde, J., Jovanis, P. P. (2006). Spatial analysis of fatal and injury crashes in Pennsylvania. Accident Analysis Prevention, 38(3), 618-625. Aguero-Valverde, J., Jovanis, P. P. (2008). Analysis of road crash frequency with spatial models. Transportation Research Record, 2061(1), 55-63. Aguero-Valverde, J., Jovanis, P. P. (2010). Spatial correlation in multilevel crash frequency models: Effects of different neighboring structures. Transportation Research Record, 2165(1), 21-32. Alarifi, S. A., Abdel-Aty, M. A., Lee, J., Wang, X. (2018). Exploring the effect of different neighboring structures on spatial hierarchical joint crash frequency models. Transportation Research Record, 2672(38), 210-222. Ali, M., Nelson, A. R., Lopez, A. L., Sack, D. A. (2015). Updated global burden of cholera in endemic countries. PLoS neglected tropical diseases, 9(6). Amin, M. S., Bhuiyan, M. A. S., Reaz, M. B. I., Nasir, S. S. (2013). GPS and Map matching based vehicle accident detection system. Paper presented at the 2013 IEEE Student Conference on Research and Developement. Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis Prevention, 41(3), 359-364. Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93-115. Anselin, L. (2001). Spatial econometrics. A companion to theoretical econometrics, 310330. Anselin, L. (2005a). Exploring spatial data with GeoDaTM: a workbook. Center for spatially integrated social science. Anselin, L. (2005b). Spatial regression analysis in R: a workbook. Urbana, 51, 61801. Anselin, L., Bera, A. K. (1998). Introduction to spatial econometrics. Handbook of applied economic statistics, 237. Anselin, L., Bera, A. K., Florax, R., Yoon, M. J. (1996). Simple diagnostic tests for spatial dependence. Regional science and urban economics, 26(1), 77-104. Anselin, L., Griffith, D. A. (1988). Do spatial effecfs really matter in regression analysis? Papers in Regional Science, 65(1), 11-34. Arbia, G., Piras, G. (2005). Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects. Barua, S., El-Basyouny, K., Islam, M. T. (2014). A full Bayesian multivariate count data model of collision severity with spatial correlation. Analytic methods in accident research, 3, 28-43. Belsley, D. A. (1991). A guide to using the collinearity diagnostics. Computer Science in Economics and Management, 4(1), 33-50. Brunsdon, C., Fotheringham, A., Charlton, M. (2002). Geographically weighted summary statistics—a framework for localised exploratory data analysis. Computers, Environment and Urban Systems, 26(6), 501-524. Castro, M., Paleti, R., Bhat, C. R. (2012). A latent variable representation of count data models to accommodate spatial and temporal dependence: Application to predicting crash frequency at intersections. Transportation research part B: methodological, 46(1), 253-272. Clarke, K. (1999). Getting started with GIS. Cliff, A. D., Ord, J. K. (1981). Spatial processes: models applications: Taylor Francis. Dewey, K. G., Nommsen-Rivers, L. A., Heinig, M. J., Cohen, R. J. (2003). Risk factors for suboptimal infant breastfeeding behavior, delayed onset of lactation, and excess neonatal weight loss. Pediatrics, 112(3), 607-619. Dillon, W. R., Goldstein, M. (1984). Multivariate analysismethods and applications. Dong, N., Huang, H., Zheng, L. (2015). Support vector machine in crash prediction at the level of traffic analysis zones: assessing the spatial proximity effects. Accident Analysis Prevention, 82, 192-198. El-Basyouny, K., Sayed, T. (2009). Urban arterial accident prediction models with spatial effects. Transportation Research Record, 2102(1), 27-33. Elhorst, J. P. (2003). Specification and estimation of spatial panel data models. International regional science review, 26(3), 244-268. Elvik, R., Høye, A., Vaa, T., Sørensen, M. (2009). Driver Training and Regulation of Professional Drivers', The Handbook of Road Safety Measures: Emerald Group Publishing Limited. Geary, R. C. (1954). The contiguity ratio and statistical mapping. The incorporated statistician, 5(3), 115-146. Getis, A., Aldstadt, J. (2004). Constructing the spatial weights matrix using a local statistic. Geographical Analysis, 36(2), 90-104. Getis, A., Griffith, D. A. (2002). Comparative spatial filtering in regression analysis. Geographical Analysis, 34(2), 130-140. Getis, A., Ord, J. K. (2010). The analysis of spatial association by use of distance statistics. In Perspectives on spatial data analysis (pp. 127-145): Springer. Gregory, I. N., Arts, Service, H. D. S. H. D. (2003). A place in history: A guide to using GIS in historical research: Oxbow Oxford. Gregory, I. N., Kemp, K. K., Mostern, R. (2001). Geographical information and historical research: Current progress and future directions. History and Computing, 13(1), 7-23. Griffith, D. A. (1992). What is spatial autocorrelation? Reflections on the past 25 years of spatial statistics. L'Espace géographique, 265-280. Guo, F., Wang, X., Abdel-Aty, M. A. (2010). Modeling signalized intersection safety with corridor-level spatial correlations. Accident Analysis Prevention, 42(1), 84-92. Hadayeghi, A., Shalaby, A. S., Persaud, B. N. (2010). Development of planning level transportation safety tools using Geographically Weighted Poisson Regression. Accident Analysis Prevention, 42(2), 676-688. Haining, R. P., Haining, R. (2003). Spatial data analysis: theory and practice: Cambridge university press. Hong, J., Lee, S., Lim, J., Kim, J. (2013). Application of spatial econometrics analysis for traffic accident prediction models in urban areas. Paper presented at the Proceedings of the Eastern Asia Society for Transportation Studies. Hsiao, C.-Y. (2019). 貨物旅行的距離是否越來越遠? 全球貨物的進出口距離樣態. 成功大學交通管理科學系學位論文, 1-128. Hsiao, C. (2014). Analysis of panel data: Cambridge university press. Hubert, L. J., Golledge, R. G., Costanzo, C. M. (1981). Generalized procedures for evaluating spatial autocorrelation. Geographical Analysis, 13(3), 224-233. Ian, H. (2010). An introduction to geographical information systems: Pearson Education India. Kelejian, H. H., Prucha, I. R. (1998). A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. The Journal of Real Estate Finance and Economics, 17(1), 99-121. Kelejian, H. H., Prucha, I. R. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model. International economic review, 40(2), 509-533. Kosfeld, R. (2018). Spatial Econometrics with R - Spatial Data Analysis of the 5-Region Script Example. Kuo, P.-F., Lord, D. (2017). Estimating the safety impacts in before–after studies using the Naïve Adjustment Method. Transportmetrica A: Transport Science, 13(10), 915-931. Kuo, P.-F., Lord, D., Walden, T. D. (2013). Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data. Journal of Transport Geography, 30, 138-148. Lee, A. S., Lin, W.-H., Gill, G. S., Cheng, W. (2019). An enhanced empirical bayesian method for identifying road hotspots and predicting number of crashes. Journal of Transportation Safety Security, 11(5), 562-578. Lee, J., Abdel-Aty, M. (2018). Macro-level analysis of bicycle safety: Focusing on the characteristics of both crash location and residence. International journal of sustainable transportation, 12(8), 553-560. LeSage, J., Pace, R. K. (2009). Introduction to spatial econometrics: CRC press. Boca Raton, FL. LeSage, J. P. (2008). An introduction to spatial econometrics. Revue d'économie industrielle(123), 19-44. Levine, N., Kim, K. E., Nitz, L. H. (1995). Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns. Accident Analysis Prevention, 27(5), 663-674. Li, L., Zhu, L., Sui, D. Z. (2007). A GIS-based Bayesian approach for analyzing spatial–temporal patterns of intra-city motor vehicle crashes. Journal of Transport Geography, 15(4), 274-285. Longley, P. A., Goodchild, M. F., Maguire, D. J., Rhind, D. W. (2005). Geographic information systems and science: John Wiley Sons. Loo, B. P., Anderson, T. K. (2015). Spatial analysis methods of road traffic collisions: CRC Press. Lord, D., Kuo, P.-F. (2012). Examining the effects of site selection criteria for evaluating the effectiveness of traffic safety countermeasures. Accident Analysis Prevention, 47, 52-63. Lord, D., Mannering, F. (2010). The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transportation research part A: policy and practice, 44(5), 291-305. Mannering, F. L., Shankar, V., Bhat, C. R. (2016). Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic methods in accident research, 11, 1-16. McGraw, K. O., Wong, S. P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological methods, 1(1), 30. Mitra, S. (2009). Spatial autocorrelation and Bayesian spatial statistical method for analyzing intersections prone to injury crashes. Transportation Research Record, 2136(1), 92-100. Montella, A. (2010). A comparative analysis of hotspot identification methods. Accident Analysis Prevention, 42(2), 571-581. Moran, P. A. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society. Series B (Methodological), 10(2), 243-251. Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17-23. Mukaka, M. M. (2012). A guide to appropriate use of correlation coefficient in medical research. Malawi medical journal, 24(3), 69-71. Okabe, A., Sugihara, K. (2012). Spatial analysis along networks: statistical and computational methods: John Wiley Sons. Ord, J., Cliff, A. (1973). Spatial autocorrelation. London: Pion. Ord, J. K., Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27(4), 286-306. Paelinck, J. H. P., Klaassen, L. L. H. (1979). Spatial econometrics (Vol. 1): Saxon House. Papadimitriou, E., Filtness, A., Theofilatos, A., Ziakopoulos, A., Quigley, C., Yannis, G. (2019). Review and ranking of crash risk factors related to the road infrastructure. Accident Analysis Prevention, 125, 85-97. Pedhazur, E. (1997). Multiple regression in behavioral research: Explanation and prediction . Thompson Learning. Inc: New York, NY. Quantum, G. (2013). Development Team.(2013). Quantum GIS geographic information system. Open Source Geospatial Foundation Project. In. Rhee, K.-A., Kim, J.-K., Lee, Y.-I., Ulfarsson, G. F. (2016). Spatial regression analysis of traffic crashes in Seoul. Accident Analysis Prevention, 91, 190-199. Sellin, N. (1989). PLSPath version 3.01 application manual. Hamburg, Germany. Sokal, R. R., Oden, N. L. (1978a). Spatial autocorrelation in biology: 1. Methodology. Biological journal of the Linnean Society, 10(2), 199-228. Sokal, R. R., Oden, N. L. (1978b). Spatial autocorrelation in biology: 2. Some biological implications and four applications of evolutionary and ecological interest. Biological journal of the Linnean Society, 10(2), 229-249. Theofilatos, A., Yannis, G. (2014). A review of the effect of traffic and weather characteristics on road safety. Accident Analysis Prevention, 72, 244-256. Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic geography, 46(sup1), 234-240. Traynor, T. L. (2008). Regional economic conditions and crash fatality rates–a cross-county analysis. Journal of safety research, 39(1), 33-39. Upton, G., Fingleton, B. (1985). Spatial data analysis by example. Volume 1: Point pattern and quantitative data: John Wiley Sons Ltd. Whittle, P. (1954). On stationary processes in the plane. Biometrika, 434-449. Wong, D. W.-S., Lee, J. (2005). Statistical analysis of geographic information with ArcView GIS and ArcGIS: John Wiley Sons Hoboken, NJ. Xie, Z., Yan, J. (2008). Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 32(5), 396-406. Xie, Z., Yan, J. (2013). Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach. Journal of Transport Geography, 31, 64-71. Xu, P., Huang, H. (2015). Modeling crash spatial heterogeneity: Random parameter versus geographically weighting. Accident Analysis Prevention, 75, 16-25. Yao, S., Loo, B. P., Yang, B. Z. (2016). Traffic collisions in space: four decades of advancement in applied GIS. Annals of GIS, 22(1), 1-14. Yong-an, D. (2012). Spatial Econometric Analysis on Occurrance Mechanism of Traffic Accident in China [J]. Paper presented at the Statistics Information Forum. Zeng, Q., Huang, H. (2014). Bayesian spatial joint modeling of traffic crashes on an urban road network. Accident Analysis Prevention, 67, 105-112. Zhang, Y., Bigham, J., Ragland, D., Chen, X. (2015). Investigating the associations between road network structure and non-motorist accidents. Journal of Transport Geography, 42, 34-47. Ziakopoulos, A., Yannis, G. (2020). A review of spatial approaches in road safety. Accident Analysis Prevention, 135, 105323. Zovko, I. I. (2008). Topics in market microstructure: Amsterdam University Press. 朱健銘. (2000). 土地利用空間型態之硏究. National Taiwan University Department of Geography, 艾兆蕾. (2005). 影響住宅區地價因素之空間分析-以鄉鎮與縣市為例. 台北: 世新大學經濟學研究所碩士論文. 沈昌懋. (2004). 空間自相關在人口分布資料之應用研究. 林豐福, 賴靜慧. (2004). 道路交通事故資料基本分析方法介紹. 都市交通, 19(3), 84-95. 紀凱婷. (2007). 台北市新推個案訂價之時間與空間相依性分析. 政治大學地政研究所學位論文, 1-55. 涂仁維. (2018). 公共自行車系統站點區位優化之研究―以屏東縣為例. 張哲寧. (2016). 建立風險決策模式於路段機車空間管制. 臺灣大學土木工程學研究所學位論文(2016 年), 1-100. 許銘峰. (2008). 台灣地區都市型態特徵之比較研究. 陳良敬. (2016). 高雄市人口變遷與空間分布之社會影響因素探討. 中山大學公共事務管理研究所學位論文, 1-98. 陳慈仁. (2001). 台北市資訊軟體業與網際網路服務業區位分佈之硏究. National Taiwan University Department of Building and Planning. 陳學祥. (2011). 台灣地區高速公路系統建設對人口及產業空間分布之影響. 政治大學地政研究所學位論文, 1-186. 黃于祐. (2007). 台北市房價影響因素之空間分析-地理加權迴歸方法之應用. 臺北大學都市計劃研究所學位論文, 1-117. 黃紹東. (2004). 台南市東區住宅價格之空間自我迴歸分析. 撰者, 戴誥芬. (2010). 都市土地使用與二氧化碳濃度影響之關聯研究. 成功大學都市計劃學系學位論文, 1-106. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16821 | - |
dc.description.abstract | 鑒於過去文獻可知,同一路廊之路段或路口之事故發生具有類似的區位與特性,存在相互影響關係,空間相依性的產生往往是因為鄰近的路段具有類似的事故影響因子,而路段事故因子之影響力會隨著距離遞減,因此建置事故分析模型時需要考量空間因素。 本研究目的在於將空間相依性納入事故分析模型進行探討,採用台北市41條近年來取消第三車道禁行機車之三車道以上路段進行實證。首先,使用Moran’s I值與LISA值二指標來檢測空間自相關情形,並以空間計量模型探討事故之影響因子,比較傳統迴歸、空間落遲模型與空間誤差模型橫斷面分析之預測能力,其次使用空間追蹤資料之空間落遲與空間誤差模型進行分析,期望獲得橫斷面模型無法取得之訊息,並比較橫斷面分析與追蹤資料分析可能存在之差異。 實證結果顯示,實證區域事故資料之Moran’s I值均顯著大於0,說明其有正向之空間相依性,考量空間相依性之空間迴歸模型解釋能力優於一般傳統回歸模型,在空間迴歸模型中,空間落遲模型之估計優於空間誤差模型,而空間追蹤資料分析結果亦顯示空間落遲模型為較適之估計模型,與橫斷面資料分析結果一致。 | zh_TW |
dc.description.abstract | It is well-known from the literature that accidents in segments or interactions along the same corridor would have similar location and characteristics. Spatial dependence mainly derives from the similar accident impact factors between adjacent road sections. The influence of the accident factor in road segment will decrease as distance increases. Therefore, spatial factors need to be considered when constructing an accident analysis model. This paper aims to incorporate spatial dependence into the accident analysis model. We use a database of 41 road sections with three or more lanes in Taipei City which have been canceling the driving restriction of motorcycles on third lane in recent years. Firstly, two indices for measuring spatial autocorrelation are considered, including (i) Moran’s I Index and (ii) LISA Index. Secondly, we compared the explanation power of three cross-sectional analysis model including (i) ordinary least squares (OLS), (ii) spatial lag model (SLM), and (iii) spatial error model (SEM). Then, a spital panel data analysis with SLM and SEM is applied to obtain the unmeasured information in cross-sectional analysis. According to the empirical results, we find that the Moran’s I value of accidents is greater than 0, implying a positive spatial autocorrelation among segments. Besides, the spatial regression model performs better that the traditional OLS model. In the comparison of cross-sectional analysis models, the SLM is more appropriate than SEM. Additionally, in the spatial panel data analysis, results show that fixed-effect SLM is also the more appropriate model, which is consistent with the cross-sectional analysis. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T23:47:10Z (GMT). No. of bitstreams: 1 U0001-1008202003212300.pdf: 8131852 bytes, checksum: f467a4be99b3aa5e3f3864a568e8f999 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 致謝 I 摘要 II ABSTRACT III 目錄 IV 圖目錄 VI 表目錄 VIII 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 2 1.3 研究問題 4 1.4 研究範圍 4 1.5 研究架構與流程 4 第二章 文獻回顧 7 2.1 事故風險評定指標 7 2.2 事故之空間關聯性 11 2.3 空間單元鄰近之意義 15 2.4 空間分析方法在事故分析之相關應用 20 2.5 空間迴歸模型與地理資訊系統應用之相關研究 22 2.6 小結 24 第三章 方法論 26 3.1 研究架構 26 3.2 事故風險分析方法 27 3.3 空間自相關檢測 28 3.4 空間迴歸模型建構 36 3.5 追蹤資料分析法 41 3.6 空間追蹤資料模型(Spatial Panel Data Model) 43 第四章 資料分析 47 4.1 資料來源與定義 47 4.2 模型因子選取 57 4.3 小結 63 第五章 實證分析結果 64 5.1 空間特性分布 64 5.2 橫斷面空間迴歸模型 73 5.3 空間追蹤資料模型 83 5.4 橫斷面與空間追蹤資料分析之比較 85 第六章 結論與建議 87 6.1 結論 87 6.2 建議 88 參考文獻 90 附錄 97 | |
dc.language.iso | zh-TW | |
dc.title | 三車道以上路段車禍事故之空間相依性分析 - 以台北市為例 | zh_TW |
dc.title | Spatial Dependence Analysis of Accidents in Road Sections with three or more lanes – A Case Study of Taipei City | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 馮正民(Cheng-Min Feng),李明璁(Ming-Tsung Lee) | |
dc.subject.keyword | 空間相依性分析,空間自相關,空間迴歸模型,空間追蹤資料, | zh_TW |
dc.subject.keyword | Spatial Dependence Analysis,Spatial Autocorrelation,Spatial Regression Model,Spatial Panel Data Analysis, | en |
dc.relation.page | 109 | |
dc.identifier.doi | 10.6342/NTU202002758 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2020-08-10 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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