根據世界衛生組織（World Health Organization, WHO）統計，2018年交通事故傷害為全球十大死因第八名，且有一半是脆弱的道路使用者(vulnerable user)：行人，自行車騎士和機車騎士。根據我國交通部道路交通安全委員會的統計顯示，所有運具類別中以自行車與機車的機慢車交通事故上升的最為顯著，目前相關部門僅透過加強取締及裝設測速照相等方式處理事故死傷之問題，並未將交通安全納入運輸規劃程序。因此藉由巨觀的角度探討機慢車交通事故，有利在都市規劃及運輸規劃層面協助改善交通安全。

目前在事故安全研究領域中，大致上分為以使用者角度出發分析，經由問卷調查獲得資料來探討其感知行為或是透過事故統計資料進行計量模型分析導致事故發生的因素兩種類型。目前國內事故安全研究領域，有以個體事故分析輔以事故統計及微觀尺度建構事故計量模型，但鮮少建構巨觀尺度的事故計量模型。另外，在國外期刊論文方面，由於地理資訊系統(Geographic Information System, GIS)之發展，近年有許多研究以巨觀事故計量模型為研究方法分析事故發生的因素，其研究成果透過巨觀尺度辨識高風險的環境與地理空間單元，可以運用在運輸規劃與交通安全計畫(Transportation Safety Planning, TSP)擬定上，使策略擬定更容易聚焦，對於改善交通安全並落實策略有極大的幫助。不過，其研究地區土地使用、運具使用及道路設計等特性，與我國高密度混合的土地使用、機慢車混合車流且無明確車種分流的道路設計差異甚大，加上巨觀尺度的事故安全研究會因研究地區特性產生不同的研究成果，因此目前現有的國外期刊論文不足以全然代表我國的環境特性，凸顯巨觀尺度事故分析研究於國內事故安全領域之重要性。

然而，在準確校估對機慢車事故影響因素的過程存在許多挑戰，由於事故資料分布特性為泊松分布，無法透過一般傳統迴歸進行分析，且具有變異數大於平均數過度分散及零值過多之問題，加上於空間單元間有相關的狀況，而在變數資料蒐集缺乏以里為起迄的暴露度數據，因此研究設計在貝氏統計理論框架下，建構泊松對數常態空間 (Spatial Poisson log-normal model, SPLN) 模型，透過馬可夫鏈蒙地卡羅法(Markov chain Monte Carlo, MCMC)進行模型校估，並將家戶旅次調查資料以最短路徑程式碼轉換成為暴露度資料解決前述面臨之挑戰。

本研究以臺南為研究範圍，回應其混合車流之特性，細分涉及事故車種為機車與自行車碰撞、汽車與自行車碰撞、機車與汽車以及機車相撞碰撞的四種組合。研究成果顯示人口密度皆呈負向顯著影響，高齡人口數、商業使用占比、自行車暴露度、機車暴露度及汽車暴露度於顯著的碰撞組合中皆呈正向影響，而公車站密度推論因車輛行駛模式及可視範圍之差異造成不同影響關係。最後研究成果可推論交通量、交通衝突點以及速度為造成機慢車事故的直接原因。研究成果可以提供相關部門進行運輸規劃，將主動安全管理(Proactive Safety Management)納入程序中，預先辨識導致事故發生的高風險因素，擬定降低風險策略。

The aim of this study is to investigate the relationships between the traffic accidents of two-wheelers and urban built environments in Tainan, Taiwan. Currently in Taiwan, the rising trend of two-wheeler accidents is mainly tackled by the enforcement of traffic laws. It might be helpful to improve traffic safety from the transportation planning perspective. In addition, the particular Taiwanese urban environments such as mixed land use patterns and mixed traffic characteristics make this topic an interesting subject to explore. The accidents were divided into four collision types, i.e. scooter-bicycle, car-bicycle, car-scooter, and scooter-scooter collisions. To deal with the dispersion of accident data caused errors and the spatial heterogeneity, three model types were proposed to describe how the urban environments relate to the number of collisions, i.e. the Poisson regression model, the Poisson-lognormal model and the Spatial Poisson lognormal model. The Bayesian analysis using Markov Chain Monte Carlo method was employed to calibrate the models. The calibration outcomes show that the Spatial Poisson lognormal model has a good fit with the data. The variables such as land-use density, street network density, traffic exposure and socio-economic factors are related to the number of collisions significantly. The research results are able to facilitate the recognition of high-risk built environment for two-wheeler traffic safety. Also, the results provide possibilities to improve traffic safety from the fundamental level in urban planning and transportation planning processes.

天下雜誌. (2019). 第681期幸福城市完整指標.
交通部運輸研究所. (2019). 道路交通事故成本推估之研究.
交通部道路交通安全委員會. (2020). 道安資訊查詢網趨勢分析
交通部道路交通安全督導委員會道安資訊查詢網. (2018). 全國道路交通事故30日內死傷人數(與去年同期比較).
周榮昌, & 陳孜穎. (2010). 應用計數模式分析機車肇事行為. [Application of Count Models to Analyze Motorcycle Accident Behavior]. 運輸學刊, 22(4), 391-414.
蕭力文, 汪進財, & 鍾易詩. (2008). 年輕機車族群高風險駕駛行為異質性研究.
白伊彤. (2010). 自行車騎乘環境友善性評估之研究. 淡江大學,新北市.
白詩滎. (2013). 臺北公共自行車使用行為特性分析與友善環境建構之研究. (碩士). 國立政治大學, 臺北市.
王定環. (2016). 新北市河濱公園自行車道意外空間資料探勘. (碩士). 世新大學, 臺北市.
道路交通安全督導委員會道安資訊查詢網. (2018). 全國道路交通事故30日內死傷人數(與去年同期比較) 行政院交通部, 臺北市.
吳聲汶. (2010). 臺北市河濱自行車道參與者傳播媒介使用對風險知覺影響之研究. (碩士). 國立臺灣師範大學, 臺北市.
李亭儀. (2016). 探討混合使用發展對於混合使用之影響-以臺南市為例. (碩士). 國立成功大學, 臺南市.
李訓誠. (2010). 應用資料探勘方法於自行車交通事故特性之研究. (碩士). 中央警察大學, 桃園縣.
沈希哲, 黃松元, 兵逸儂, 陳正誠, & 史璦溱. (2009). 臺北巿青少年機車事故傷害程度之相關因素研究. [Investigating Influences of Injured Degree of MOTorcycle Traffic Accident among Adolescent in Taipei City]. 學校衛生(55), 19-39.
邱皓政. (2020). 貝氏統計：原理與應用: 雙葉書廊.
韋懿軒. (2016). 都市運輸型自行車適騎性之評估方法. (碩士). 國立臺灣大學, 臺北市.
員旭成. (2007). 臺灣地區交通事故傷害肇因之研究. (碩士). 銘傳大學, 臺北市.
張瑞予. (2020). 串連交通事故資料庫與健保資料庫探討高齡機車騎士事故受傷嚴重性之影響因素. 淡江大學, 新北市
莊凱翔. (2014). 道路環境和車輛因素對自行車騎乘行為影響之研究. (博士). 國立中央大學, 桃園縣.
許添本. (1994). 多車種組合式模組化車流模擬模型之研究.國立臺灣大學土木工程學系暨研究所. 臺北市.
許敦淵. (2001). 混合車流下路段機車安全評估參數之建立. 國立臺灣大學, 臺北市.
陳彥儒. (2008). 自行車專用道安全性評估指標體系之研究. (碩士). 朝陽科技大學, 臺中市.
陳柏叡. (2018). 應用多變量事故頻次模型分析市區車道寬度與車種組成對道路安全之研究. (碩士). 國立交通大學, 新竹市.
陳騰輝. (2009). 自行車友善的街巷空間之研究. 淡江大學, 新北市.
喻世祥, 張開國, 白志偉, 簡戊鑑, 林大煜, 邱文達, & 林樹基. (2019). 機車交通事故住院傷患嚴重度之影響因子分析. 運輸計劃季刊, 48(1), 1-28.
黃湘芸. (2015). 都市公共自行車系統使用行為特性與環境認知之研究-以臺北市大安區為例. (碩士). 國立臺北科技大學, 臺北市.
黃碧芬. (2010). 自行車事故特性之研究—以臺中市為例. (碩士). 逢甲大學, 臺中市.
劉秉宜. (2015). 從臺北市自行車安全分析探討都市街道改善策略之研究. (碩士). 國立政治大學, 臺北市.
盧建中. (2010). 急診室自行車與交通事故探討. (碩士). 亞洲大學, 臺中市.
顏愉. (2015). 應用貝氏方法針對肇事碰撞型態建立號誌化交叉口機車交通事故因子分析模式. 臺灣大學, 臺北市.
Admussen, F., & Hyden, C. (1977). Proceeding of first workshop on traffic conflicts. Institute of Transport Economics, Oslo/Lund Institute of Technology, Oslo, Norway.
Aguero-Valverde, J. (2013). Full Bayes Poisson gamma, Poisson lognormal, and zero inflated random effects models: Comparing the precision of crash frequency estimates. Accident Analysis & Prevention, 50, 289-297.
Aguero-Valverde, J., & Jovanis, P. P. (2008). Analysis of Road Crash Frequency with Spatial Models. Transportation Research Record, 2061(1), 55-63.
Amoh-Gyimah, R., Saberi, M., & Sarvi, M. (2016). Macroscopic modeling of pedestrian and bicycle crashes: A cross-comparison of estimation methods. Accident Analysis & Prevention, 93, 147-159.
Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 192-225.
Besag, J., York, J., & Mollié, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43(1), 1-20.
Cai, Q., Lee, J., Eluru, N., & Abdel-Aty, M. (2016). Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models. Accident Analysis & Prevention, 93, 14-22.
Cameron, A., & Trivedi, P. (1990). Regression-based tests for overdispersion in the Poisson model. Journal of Econometrics, 46(3), 347-364.
Castro, M., Paleti, R., & Bhat, C. R. (2013). A spatial generalized ordered response model to examine highway crash injury severity. Accident Analysis & Prevention, 52, 188-203.
Cervero, R., & Duncan, M. (2003). Walking, Bicycling, and Urban Landscapes: Evidence From the San Francisco Bay Area. American Journal of Public Health, 93(9), 1478-1483.
Chaney, R. A., & Kim, C. (2013). Characterizing Bicycle Collisions by Neighborhood in a Large Midwestern City. Health Promotion Practice, 15(2), 232-242.
Chen, L., Chen, C., Srinivasan, R., SCKnight, C. E., Ewing, R., & Roe, M. (2012). Evaluating the safety effects of bicycle lanes in New York City. American Journal of Public Health, 102(6), 1120-1127.
Chen, P. (2015). Built environment factors in explaining the automobile-involved bicycle crash frequencies: A spatial statistic approach. Safety Science, 79, 336-343.
Chen, P., & Shen, Q. (2016). Built environment effects on cyclist injury severity in automobile-involved bicycle crashes. Accident Analysis & Prevention, 86, 239-246.
Chen, P., & Shen, Q. (2019). Identifying high-risk built environments for severe bicycling injuries. Journal of Safety Research, 68, 1-7.
Chen, P., Sun, F., Wang, Z., Gao, X., Jiao, J., & Tao, Z. (2018). Built environment effects on bike crash frequency and risk in Beijing. Journal of Safety Research, 64, 135-143.
Chen, P., Zhou, J., & Sun, F. (2017). Built environment determinants of bicycle volume: A longitudinal analysis. Journal of Transport and Land Use, 10(1).
Das, D. (2011). A Survey on Cellular Automata and Its Applications (Vol. 269).
Dozza, M. (2017). Crash risk: How cycling flow can help explain crash data. Accident Analysis & Prevention, 105, 21-29.
Dumbaugh, E., & Li, W. (2010). Designing for the Safety of Pedestrians, Cyclists, and Motorists in Urban Environments. Journal of the American Planning Association, 77(1), 69-88.
El-Basyouny, K., & Sayed, T. (2009). Urban Arterial Accident Prediction Models with Spatial Effects. Transportation Research Record, 2102(1), 27-33.
El Esawey, M., Mosa, A. I., & Nasr, K. (2015). Estimation of daily bicycle traffic volumes using sparse data. Computers, Environment and Urban Systems, 54, 195-203.
Ewing, R., & Dumbaugh, E. (2009). The built environment and traffic safety: a review of empirical evidence. Journal of Planning Literature, 23(4), 347-367.
Fournier, N., Christofa, E., & Knodler, M. A. (2017). A sinusoidal model for seasonal bicycle demand estimation. Transportation Research Part D: Transport and Environment, 50, 154-169.
Fournier, N., Christofa, E., & Knodler, M. A. (2019). A mixed methods investigation of bicycle exposure in crash rates. Accident Analysis & Prevention, 130, 54-61.
Fuller, D., Gauvin, L., Morency, P., Kestens, Y., & Drouin, L. (2013). The impact of implementing a public bicycle share program on the likelihood of collisions and near misses in Montreal, Canada. Preventive Medicine, 57(6), 920-924.
Goudie, R. J. B., Turner, R. M., De Angelis, D., & Thomas, A. (2020). MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference. Journal of Statistical Software; Vol 1, Issue 7 (2020).
Guo, Y., Osama, A., & Sayed, T. (2018). A cross-comparison of different techniques for modeling macro-level cyclist crashes. Accident Analysis & Prevention, 113, 38-46.
Hagel, B. E., Romanow, N. T. R., Morgunov, N., Embree, T., Couperthwaite, A. B., Voaklander, D., & Rowe, B. H. (2014). The relationship between visibility aid use and motor vehicle related injuries among bicyclists presenting to emergency departments. Accident Analysis & Prevention, 65, 85-96.
Hakkert, A. S., Braimaister, L., & Van Schagen, I. (2002). The uses of exposure and risk in road safety studies.
Hamann, C., & Peek-Asa, C. (2013). On-road bicycle facilities and bicycle crashes in Iowa, 2007–2010. Accident Analysis & Prevention, 56, 103-109.
Hauer, E. (1982). Traffic conflicts and exposure. Accident Analysis & Prevention, 14(5), 359-364.
Heinen, E., van Wee, B., & Maat, K. (2010). Commuting by Bicycle: An Overview of the Literature. Transport Reviews, 30(1), 59-96.
Hilbe, J. M. (2011). Negative binomial regression: Cambridge University Press.
Huang, H., Abdel-Aty, M. A., & Darwiche, A. L. (2010). County-Level Crash Risk Analysis in Florida: Bayesian Spatial Modeling. Transportation Research Record, 2148(1), 27-37.
Jacobsen, P. L. (2003). Safety in numbers: More walkers and bicyclists, safer walking and bicycling. Injury Prevention, 9(3), 205-209.
Kaplan, S., & Giacomo Prato, C. (2015). A Spatial Analysis of Land Use and Network Effects on Frequency and Severity of Cyclist–Motorist Crashes in the Copenhagen Region. Traffic Injury Prevention, 16(7), 724-731.
Karim, M. A., Wahba, M. M., & Sayed, T. (2013). Spatial Effects on Zone-Level Collision Prediction Models. Transportation Research Record, 2398(1), 50-59.
Kim, K., Pant, P., Yamashita, E., & Brunner, I. M. (2012). Entropy and Accidents. Transportation Research Record, 2280(1), 173-182.
Larsen, J., & El-Geneidy, A. (2011). A travel behavior analysis of urban cycling facilities in Montréal, Canada. Transportation Research Part D: Transport and Environment, 16(2), 172-177.
Lee, J., Abdel-Aty, M., & Jiang, X. (2015). Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level. Accident Analysis & Prevention, 78, 146-154.
Lee, J., & Mannering, F. (2002). Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. Accident Analysis & Prevention, 34(2), 149-161.
Lord, D., Geedipally, S. R., & Guikema, S. D. (2010). Extension of the Application of Conway-Maxwell-Poisson Models: Analyzing Traffic Crash Data Exhibiting Underdispersion. Risk Analysis, 30(8), 1268-1276.
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.
Lord, D., Washington, S. P., & Ivan, J. N. (2005). Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. Accident Analysis & Prevention, 37(1), 35-46.
Merlin, L. A., Guerra, E., & Dumbaugh, E. (2020). Crash risk, crash exposure, and the built environment: A conceptual review. Accident Analysis & Prevention, 134, 105244.
Miaou, S.-P. (1994). The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accident Analysis & Prevention, 26(4), 471-482.
Miaou, S.-P., Song, J. J., & Mallick, B. K. (2003). Roadway traffic crash mapping: a space-time modeling approach. Journal of transportation and Statistics, 6, 33-58.
Miranda-Moreno, L. F., Morency, P., & El-Geneidy, A. M. (2011). The link between built environment, pedestrian activity and pedestrian–vehicle collision occurrence at signalized intersections. Accident Analysis & Prevention, 43(5), 1624-1634.
Miranda-Moreno, L. F., Strauss, J., & Morency, P. (2011). Disaggregate Exposure Measures and Injury Frequency Models of Cyclist Safety at Signalized Intersections. Transportation Research Record, 2236(1), 74-82.
Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17-23.
Narayanamoorthy, S., Paleti, R., & Bhat, C. R. (2013). On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level. Transportation Research Part B: Methodological, 55, 245-264.
Noland, R. B., & Quddus, M. A. (2004) Analysis of pedestrian and bicycle casualties with regional panel data. In. Transportation Research Record (pp. 28-33).
Ntzoufras, I. (2011). Bayesian modeling using WinBUGS (Vol. 698): John Wiley & Sons.
Osama, A., & Sayed, T. (2017). Investigating the effect of spatial and mode correlations on active transportation safety modeling. Analytic Methods in Accident Research, 16, 60-74.
Prati, G., Marín Puchades, V., De Angelis, M., Fraboni, F., & Pietrantoni, L. (2018). Factors contributing to bicycle–motorised vehicle collisions: a systematic literature review. Transport Reviews, 38(2), 184-208.
Priyantha Wedagama, D. M., Bird, R. N., & Metcalfe, A. V. (2006). The influence of urban land-use on non-motorised transport casualties. Accident Analysis & Prevention, 38(6), 1049-1057.
Romanow, N. T. R., Couperthwaite, A. B., SCCormack, G. R., Nettel-Aguirre, A., Rowe, B. H., & Hagel, B. E. (2012). Environmental Determinants of Bicycling Injuries in Alberta, Canada. Journal of Environmental and Public Health, 2012, 487681.
Räsänen, M., & Summala, H. (1998). Attention and expectation problems in bicycle–car collisions: an in-depth study. Accident Analysis & Prevention, 30(5), 657-666.
Saha, D., Alluri, P., Gan, A., & Wu, W. (2018). Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models. Accident Analysis & Prevention, 118, 166-177.
Siddiqui, C., Abdel-Aty, M., & Choi, K. (2012). Macroscopic spatial analysis of pedestrian and bicycle crashes. Accident Analysis & Prevention, 45, 382-391.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583-639.
Strauss, J., Miranda-Moreno, L. F., & Morency, P. (2013). Cyclist activity and injury risk analysis at signalized intersections: A Bayesian modelling approach. Accident Analysis & Prevention, 59, 9-17.
Tin, S. T., Woodward, A., Thornley, S., & Ameratunga, S. (2011). Regional variations in pedal cyclist injuries in New Zealand: safety in numbers or risk in scarcity? Australian and New Zealand Journal of Public Health, 35(4), 357-363.
Tobler, W. R. (1979). Cellular Geography. In S. Gale & G. Olsson (Eds.), Philosophy in Geography (pp. 379-386). Dordrecht: Springer Netherlands.
Vandenbulcke, G., Steenberghen, T., & Thomas, I. (2009). Mapping accessibility in Belgium: a tool for land-use and transport planning? Journal of Transport Geography, 17(1), 39-53.
Vandenbulcke, G., Thomas, I., & Int Panis, L. (2014). Predicting cycling accident risk in Brussels: A spatial case–control approach. Accident Analysis & Prevention, 62, 341-357.
Vanparijs, J., Int Panis, L., Meeusen, R., & de Geus, B. (2015). Exposure measurement in bicycle safety analysis: A review of the literature. Accident Analysis & Prevention, 84, 9-19.
Wang, J., Huang, H., & Zeng, Q. (2017). The effect of zonal factors in estimating crash risks by transportation modes: Motor vehicle, bicycle and pedestrian. Accident Analysis & Prevention, 98, 223-231.
Wang, Y., & Kockelman, K. M. (2013). A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods. Accident Analysis & Prevention, 60, 71-84.
Washington, S. P., Karlaftis, M. G., & Mannering, F. (2010). Statistical and econometric methods for transportation data analysis. LONDON: Chapman and Hall/CRC.
Wegman, F., Zhang, F., & Dijkstra, A. (2012). How to make more cycling good for road safety? Accident Analysis & Prevention, 44(1), 19-29.
Wei, F., & Lovegrove, G. (2013). An empirical tool to evaluate the safety of cyclists: Community based, macro-level collision prediction models using negative binomial regression. Accident Analysis & Prevention, 61, 129-137.
WHO. (2018). Global status report on road safety 2018: summary.
Yan, X., Ma, M., Huang, H., Abdel-Aty, M., & Wu, C. (2011). Motor vehicle–bicycle crashes in Beijing: Irregular maneuvers, crash patterns, and injury severity. Accident Analysis & Prevention, 43(5), 1751-1758.