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研究生: 曾詩雅
Tseng, Shih-Ya
論文名稱: 探討主動運輸使用者在交岔路口的肇事嚴重程度
Analyzing Accident Injury Severity at Intersections of Active Transport Users
指導教授: 陳勁甫
Chen, Ching-Fu
學位類別: 碩士
Master
系所名稱: 管理學院 - 交通管理科學系
Department of Transportation and Communication Management Science
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 70
中文關鍵詞: 肇事嚴重程度主動運輸潛在類別模型多項式羅吉特混合羅吉特
外文關鍵詞: Injury severity, Active transportation, Latent class analysis, Multinomial logit model, Random parameter logit model
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  • 主動運輸有利於身心健康、交通、社會上各方面的發展,因此各國積極推動民眾使用。雖然倡議主動運輸具有許多好處,但若無合適的交通環境,則將會面臨道路上的危險狀況,而成為易受傷害的族群,政策的推廣就變得沒有意義。因此本研究目標為探討影響臺灣的行人及自行車的肇事嚴重程度之因素。
    過去臺灣針對行人及自行車的事故研究,多忽略整體數據及個體差異造成的異質性帶來的影響,為解決異質性問題,以潛在類別分析法將數據分群後,利用多項式羅吉特模型及具有隨機效果的隨機參數模型進行事故的分析,以彌補研究空缺。本研究使用2016年至2020年的交通事故資料,利用潛在類別模型將行人及自行車的整體數據分成三群,再將整體及分群的數據進行多項式羅吉特模型及隨機參數模型進行分析。
    綜合多項式羅吉特模型及隨機參數模型結果,關於行人傷害嚴重程度的估計結果,發現會增加行人發生傷亡事故可能性的變數包括:清晨或黃昏、晚上有照明、晚上無照明、女性及65歲以上的行人,會降低行人發生傷亡事故可能性的變數包括:雨天、一般號誌附設行人專用號誌、閃光號誌、無號誌、第一當事者、25歲以下的行人。關於自行車傷害嚴重程度的估計結果,發現會增加自行車發生傷亡事故可能性的變數包括:夜間有照明、閃光號誌、女性、飲酒及65歲以上的自行車使用者,會降低自行車發生傷亡事故可能性的變數包括,包括:陰天、雨天、第一當事者、25歲以下的自行車使用者。
    綜合上述研究結果可作為提供改善行人及自行車的交通安全之依據,根據研究結果我們建議:針對高龄駕駛人再教育訓練及加強管理;對於飲酒上路的民眾加重罰金;在光線不夠明亮時,行人可穿戴較明亮的衣物或反光手環、自行車使用者可將反光條貼於車上或強制自行車車燈保持開啟;對於號誌不明確的號誌路口應減速接近路口小心行駛、在行人事故較多的路口可增設行人專用號誌,以增加主動運輸的用路安全。

    Active transportation is beneficial to health, transportation, and social development. Without safety environment, people will become vulnerable road users. Therefore, this study aims to investigate the factors that affect the severity of pedestrian and bicycle accidents in Taiwan.
    The past research ignored the influence of heterogeneity in the data and individual differences. This study uses the traffic accident data from 2016 to 2020, used latent class method to divide the overall data into three groups, and then analyzes the overall and clustered data with a multinomial Logit model(MNL) and a random parameter(RP) model.
    Combining the results of the MNL and RP model to estimate the severity of pedestrian injuries, it is found that the variables that increase the probability of pedestrian casualties include: dusky light, female and older over 65 years old. Estimated results of bicycle injury severity estimates found that variables that increase the probability of bicycle injuries include: Light at night, flashing signs, female, drunk driving, and older over 65 years old.
    Based on the above research results, we make the following recommendations. Re-education for the older. Increased fines for drunk driving. When the light isn't bright enough, pedestrians and cyclists can wear brighter clothing or stick reflective strips on their bikes and keep bicycle lights open. Normal sign with pedestrian signal can be added at intersections with more pedestrian accidents to increase road safety for active transport user.

    第一章 緒論 1 1.1研究背景 1 1.2研究動機 3 1.3研究目的 5 1.4研究流程與方法 5 第二章 文獻回顧 7 2.1道路肇事嚴重程度相關研究方法 7 2.1.1二元羅吉特及多項式羅吉特 8 2.1.2次序羅吉特 8 2.1.3隨機參數模型(Random Parameter logit, RPL) 9 2.1.4潛在類別分析(Latent Class Analysis, LCA) 9 2.1.5小結 9 2.2主動運輸肇事嚴重程度相關課題 10 2.3影響事故因素相關研究 11 2.3.1駕駛與當事者因素 12 2.3.2車輛因素 13 2.3.3道路與環境因素 13 2.3.4小結 13 第三章 研究方法 15 3.1潛在類別分析 15 3.2多項式羅吉特模型 16 3.3隨機參數模型 17 第四章 事故資料特性與實證分析 19 4.1資料描述性統計 19 4.1.1資料來源與整理 19 4.1.2變數定義與說明 19 4.1.3樣本特性統計分析 21 4.1.4變數假設 26 4.1.5總體、行人、自行車多項式羅吉特比較 29 4.2潛在類別模型分析結果 31 4.2.1潛在類別模型檢定 31 4.2.2分群結果 33 4.3多項式羅吉特模型分析結果 39 4.3.1行人事故多項式羅吉特迴歸分析結果 39 4.3.2自行車事故多項式羅吉特迴歸分析結果 43 4.4隨機參數模型分析結果 46 4.4.1行人隨機參數模型分析結果 46 4.4.2自行車隨機參數模型分析結果 50 第五章 結論與建議 53 5.1研究結論 53 5.1.1潛在類別模型 53 5.1.2整體羅吉特模型 54 5.2研究建議 61 5.3研究限制與未來研究方向 62 參考文獻 64

    Abdel-Aty, M. A., Chen, C. L., & Schott, J. R. (1998). An assessment of the effect of driver age on traffic accident involvement using log-linear models. Accident Analysis & Prevention, 30(6), 851-861. 

    Al-Ghamdi, A. S. (2002). Pedestrian–vehicle crashes and analytical techniques for stratified contingency tables. Accident Analysis & Prevention, 34(2), 205-214. 

    Anderson, J. A. (1984). Regression and ordered categorical variables. Journal of the Royal Statistical Society: Series B (Methodological), 46(1), 1-22. 

    Andrey, J., & Yagar, S. (1993). A temporal analysis of rain-related crash risk. Accident Analysis & Prevention, 25(4), 465-472. 

    Ariffin, A. H., Hamzah, A., Solah, M. S., Paiman, N. F., Hussin, S. F. M., & Osman, M. R. (2017). Pedestrian-motorcycle collisions: associated risks and issues. Paper presented at the MATEC web of conferences.

    Aziz, H. A., Ukkusuri, S. V., & Hasan, S. (2013). Exploring the determinants of pedestrian–vehicle crash severity in New York City. Accident Analysis & Prevention, 50, 1298-1309. 

    Batouli, G., Guo, M., Janson, B., & Marshall, W. (2020). Analysis of pedestrian-vehicle crash injury severity factors in Colorado 2006–2016. Accident Analysis & Prevention, 148, 105782. 

    Behnood, A., & Mannering, F. (2017). Determinants of bicyclist injury severities in bicycle-vehicle crashes: A random parameters approach with heterogeneity in means and variances. Analytic methods in accident research, 16, 35-47. 

    Behnood, A., & Mannering, F. L. (2015). The temporal stability of factors affecting driver-injury severities in single-vehicle crashes: Some empirical evidence. Analytic methods in accident research, 8, 7-32. 

    Behnood, A., & Mannering, F. L. (2016). An empirical assessment of the effects of economic recessions on pedestrian-injury crashes using mixed and latent-class models. Analytic methods in accident research, 12, 1-17. 

    Behnood, A., Roshandeh, A. M., & Mannering, F. L. (2014). Latent class analysis of the effects of age, gender, and alcohol consumption on driver-injury severities. Analytic methods in accident research, 3, 56-91. 

    Bhat, C. R. (2003). Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences. Transportation Research Part B: Methodological, 37(9), 837-855. 

    Bijmolt, T. H., Paas, L. J., & Vermunt, J. K. (2004). Country and consumer segmentation: Multi-level latent class analysis of financial product ownership. International Journal of Research in Marketing, 21(4), 323-340. 

    Boufous, S., de Rome, L., Senserrick, T., & Ivers, R. (2012). Risk factors for severe injury in cyclists involved in traffic crashes in Victoria, Australia. Accident Analysis & Prevention, 49, 404-409. 

    Cafiso, S., Di Graziano, A., Di Silvestro, G., La Cava, G., & Persaud, B. (2010). Development of comprehensive accident models for two-lane rural highways using exposure, geometry, consistency and context variables. Accident Analysis & Prevention, 42(4), 1072-1079. 

    Chang, F., Xu, P., Zhou, H., Chan, A. H., & Huang, H. (2019). Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model. Accident Analysis & Prevention, 131, 316-326. 

    Chen, F., & Chen, S. (2011). Injury severities of truck drivers in single-and multi-vehicle accidents on rural highways. Accident Analysis & Prevention, 43(5), 1677-1688. 

    Chen, F., Chen, S., & Ma, X. (2018). Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. Journal of safety research, 65, 153-159. 

    Chen, P., & Shen, Q. (2016). Built environment effects on cyclist injury severity in automobile-involved bicycle crashes. Accident Analysis & Prevention, 86, 239-246. 

    Chen, Z., & Fan, W. (2019). Modeling pedestrian injury severity in pedestrian-vehicle crashes in rural and urban areas: mixed logit model approach. Transportation research record, 2673(4), 1023-1034. 

    Clifton, K. J., Burnier, C. V., & Akar, G. (2009). Severity of injury resulting from pedestrian–vehicle crashes: What can we learn from examining the built environment? Transportation Research Part D: Transport and Environment, 14(6), 425-436. 

    Collins, L. M., & Lanza, S. T. (2009). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences (Vol. 718): John Wiley & Sons.

    Cripton, P. A., Shen, H., Brubacher, J. R., Chipman, M., Friedman, S. M., Harris, M. A., . . . Babul, S. (2015). Severity of urban cycling injuries and the relationship with personal, trip, route and crash characteristics: analyses using four severity metrics. BMJ open, 5(1), e006654. 

    Daniels, S., Brijs, T., Nuyts, E., & Wets, G. (2010). Explaining variation in safety performance of roundabouts. Accident Analysis & Prevention, 42(2), 393-402. 

    De Ona, J., López, G., Mujalli, R., & Calvo, F. J. (2013). Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident Analysis & Prevention, 51, 1-10. 

    Depaire, B., Wets, G., & Vanhoof, K. (2008). Traffic accident segmentation by means of latent class clustering. Accident Analysis & Prevention, 40(4), 1257-1266. 

    Dissanayake, S., & Lu, J. J. (2002). Factors influential in making an injury severity difference to older drivers involved in fixed object–passenger car crashes. Accident Analysis & Prevention, 34(5), 609-618. 

    Eluru, N., Bhat, C. R., & Hensher, D. A. (2008). A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes. Accident Analysis & Prevention, 40(3), 1033-1054. 

    Gkritza, K., & Mannering, F. L. (2008). Mixed logit analysis of safety-belt use in single-and multi-occupant vehicles. Accident Analysis & Prevention, 40(2), 443-451. 

    Gong, L., & Fan, W. D. (2017). Modeling single-vehicle run-off-road crash severity in rural areas: Accounting for unobserved heterogeneity and age difference. Accident Analysis & Prevention, 101, 124-134. 

    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. 

    Guo, R., Xin, C., Lin, P.-S., & Kourtellis, A. (2017). Mixed effects logistic model to address demographics and neighborhood environment on pedestrian injury severity. Transportation research record, 2659(1), 174-181. 

    Hair, J. F. (2009). Multivariate data analysis. 

    Haleem, K., Alluri, P., & Gan, A. (2015). Analyzing pedestrian crash injury severity at signalized and non-signalized locations. Accident Analysis & Prevention, 81, 14-23. 

    Handy, S., & McCann, B. (2010). The regional response to federal funding for bicycle and pedestrian projects: An exploratory study. Journal of the American Planning Association, 77(1), 23-38. 

    Hels, T., & Orozova-Bekkevold, I. (2007). The effect of roundabout design features on cyclist accident rate. Accident Analysis & Prevention, 39(2), 300-307. 

    Hensher, D. A., & Greene, W. H. (2003). The mixed logit model: the state of practice. Transportation, 30(2), 133-176. 

    Islam, S., & Mannering, F. (2006). Driver aging and its effect on male and female single-vehicle accident injuries: Some additional evidence. Journal of safety research, 37(3), 267-276. 

    Jones, S., & Hensher, D. A. (2007). Evaluating the behavioural performance of alternative logit models: an application to corporate takeovers research. Journal of Business Finance & Accounting, 34(7‐8), 1193-1220. 

    Jung, S., Qin, X., & Noyce, D. A. (2010). Rainfall effect on single-vehicle crash severities using polychotomous response models. Accident Analysis & Prevention, 42(1), 213-224. 

    Kim, J.-K., Kim, S., Ulfarsson, G. F., & Porrello, L. A. (2007). Bicyclist injury severities in bicycle–motor vehicle accidents. Accident Analysis & Prevention, 39(2), 238-251. 

    Kim, J.-K., Ulfarsson, G. F., Kim, S., & Shankar, V. N. (2013). Driver-injury severity in single-vehicle crashes in California: a mixed logit analysis of heterogeneity due to age and gender. Accident Analysis & Prevention, 50, 1073-1081. 

    Kim, J.-K., Ulfarsson, G. F., Shankar, V. N., & Kim, S. (2008). Age and pedestrian injury severity in motor-vehicle crashes: A heteroskedastic logit analysis. Accident Analysis & Prevention, 40(5), 1695-1702. 

    Kim, M., Kho, S.-Y., & Kim, D.-K. (2017). Hierarchical ordered model for injury severity of pedestrian crashes in South Korea. Journal of safety research, 61, 33-40. 

    Klop, J. R., & Khattak, A. J. (1999). Factors influencing bicycle crash severity on two-lane, undivided roadways in North Carolina. Transportation research record, 1674(1), 78-85. 

    Kononen, D. W., Flannagan, C. A., & Wang, S. C. (2011). Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accident Analysis & Prevention, 43(1), 112-122. 

    Lam, L. T. (2002). Distractions and the risk of car crash injury: The effect of drivers' age. Journal of safety research, 33(3), 411-419. 

    Lanza, S. T., Dziak, J. J., Huang, L., Wagner, A. T., & Collins, L. M. (2015). LCA Stata plugin users’ guide (Version 1.2). University Park: The Methodology Center, Penn State. 

    Lanza, S. T., & Rhoades, B. L. (2013). Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prevention science, 14(2), 157-168. 

    Lee, C., & Abdel-Aty, M. (2005). Comprehensive analysis of vehicle–pedestrian crashes at intersections in Florida. Accident Analysis & Prevention, 37(4), 775-786. 

    Lee, S., Yoon, J., & Woo, A. (2020). Does elderly safety matter? Associations between built environments and pedestrian crashes in Seoul, Korea. Accident Analysis & Prevention, 144, 105621. 

    Li, Z., Ci, Y., Chen, C., Zhang, G., Wu, Q., Qian, Z. S., . . . Ma, D. T. (2019). Investigation of driver injury severities in rural single-vehicle crashes under rain conditions using mixed logit and latent class models. Accident Analysis & Prevention, 124, 219-229. 

    Litman, T. (2015). Evaluating active transport benefits and costs: Victoria Transport Policy Institute.

    Liu, J., Hainen, A., Li, X., Nie, Q., & Nambisan, S. (2019). Pedestrian injury severity in motor vehicle crashes: an integrated spatio-temporal modeling approach. Accident Analysis & Prevention, 132, 105272. 

    Liu, J., Khattak, A. J., Li, X., Nie, Q., & Ling, Z. (2020). Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates. Journal of safety research, 73, 25-35. 

    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. 

    Louviere, J. J., Meyer, R. J., Bunch, D. S., Carson, R., Dellaert, B., Hanemann, W. M., . . . Irwin, J. (1999). Combining sources of preference data for modeling complex decision processes. Marketing Letters, 10(3), 205-217. 

    Ma, Z., Lu, X., Chien, S. I.-J., & Hu, D. (2018). Investigating factors influencing pedestrian injury severity at intersections. Traffic injury prevention, 19(2), 159-164. 

    Mann, R. E., Stoduto, G., Vingilis, E., Asbridge, M., Wickens, C. M., Ialomiteanu, A., . . . Smart, R. G. (2010). Alcohol and driving factors in collision risk. Accident Analysis & Prevention, 42(6), 1538-1544. 

    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. 

    McCormack, G. R., & Shiell, A. (2011). In search of causality: a systematic review of the relationship between the built environment and physical activity among adults. International journal of behavioral nutrition and physical activity, 8(1), 1-11. 

    McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response. Journal of applied Econometrics, 15(5), 447-470. 

    Milton, J. C., Shankar, V. N., & Mannering, F. L. (2008). Highway accident severities and the mixed logit model: an exploratory empirical analysis. Accident Analysis & Prevention, 40(1), 260-266. 

    Mohamed, M. G., Saunier, N., Miranda-Moreno, L. F., & Ukkusuri, S. V. (2013). A clustering regression approach: A comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada. Safety science, 54, 27-37. 

    Moore, D. N., Schneider IV, W. H., Savolainen, P. T., & Farzaneh, M. (2011). Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations. Accident Analysis & Prevention, 43(3), 621-630. 

    Moudon, A. V., Lin, L., Jiao, J., Hurvitz, P., & Reeves, P. (2011). The risk of pedestrian injury and fatality in collisions with motor vehicles, a social ecological study of state routes and city streets in King County, Washington. Accident Analysis & Prevention, 43(1), 11-24. 

    N’Guessan, A. (2010). Analytical existence of solutions to a system of nonlinear equations with application. Journal of computational and applied mathematics, 234(1), 297-304. 

    Olkkonen, S., & Honkanen, R. (1990). The role of alcohol in nonfatal bicycle injuries. Accident Analysis & Prevention, 22(1), 89-96. 

    Organization, W. H. (2018). Global status report on road safety 2018. World Health Organization. 

    Osman, M., Paleti, R., Mishra, S., & Golias, M. M. (2016). Analysis of injury severity of large truck crashes in work zones. Accident Analysis & Prevention, 97, 261-273. 

    Öström, M., & Eriksson, A. (2001). Pedestrian fatalities and alcohol. Accident Analysis & Prevention, 33(2), 173-180. 

    Oxley, J., Fildes, B., Ihsen, E., Charlton, J., & Day, R. (1997). Differences in traffic judgements between young and old adult pedestrians. Accident Analysis & Prevention, 29(6), 839-847. 

    Park, E. S., Park, J., & Lomax, T. J. (2010). A fully Bayesian multivariate approach to before–after safety evaluation. Accident Analysis & Prevention, 42(4), 1118-1127. 

    Park, S., Jang, K., Park, S. H., Kim, D.-K., & Chon, K. S. (2012). Analysis of injury severity in traffic crashes: a case study of Korean expressways. KSCE Journal of Civil Engineering, 16(7), 1280-1288. 

    Peek-Asa, C., Britton, C., Young, T., Pawlovich, M., & Falb, S. (2010). Teenage driver crash incidence and factors influencing crash injury by rurality. Journal of safety research, 41(6), 487-492. 

    Persaud, N., Coleman, E., Zwolakowski, D., Lauwers, B., & Cass, D. (2012). Nonuse of bicycle helmets and risk of fatal head injury: a proportional mortality, case–control study. Cmaj, 184(17), E921-E923. 

    Pour-Rouholamin, M., & Zhou, H. (2016). Investigating the risk factors associated with pedestrian injury severity in Illinois. Journal of safety research, 57, 9-17. 

    Prijon, T., & Ermenc, B. (2009). Influence of alcohol intoxication of pedestrians on injuries in fatal road accidents. Forensic Science International Supplement Series, 1(1), 33-34. 

    Qi, Y., Srinivasan, R., Teng, H., & Baker, R. (2013). Analysis of the frequency and severity of rear-end crashes in work zones. Traffic injury prevention, 14(1), 61-72. 

    Qiu, L., & Nixon, W. A. (2008). Effects of adverse weather on traffic crashes: systematic review and meta-analysis. Transportation research record, 2055(1), 139-146. 

    Quddus, M. A., Wang, C., & Ison, S. G. (2010). Road traffic congestion and crash severity: econometric analysis using ordered response models. Journal of Transportation Engineering, 136(5), 424-435. 

    Rabl, A., & De Nazelle, A. (2012). Benefits of shift from car to active transport. Transport policy, 19(1), 121-131. 

    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. 

    Rifaat, S. M., Tay, R., & De Barros, A. (2011). Effect of street pattern on the severity of crashes involving vulnerable road users. Accident Analysis & Prevention, 43(1), 276-283. 

    Roe, M., Shin, H., Ukkusuri, S., Blatt, A., & Majka, K. (2010). The New York City Pedestrian Safety Study and Action Plan Technical Supplement. New York City Department of Transportation. 

    Samerei, S. A., Aghabayk, K., Shiwakoti, N., & Mohammadi, A. (2021). Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle–bicycle crashes. Journal of safety research, 79, 246-256. 

    Sasidharan, L., Wu, K.-F., & Menendez, M. (2015). Exploring the application of latent class cluster analysis for investigating pedestrian crash injury severities in Switzerland. Accident Analysis & Prevention, 85, 219-228. 

    Savolainen, P. T., Mannering, F. L., Lord, D., & Quddus, M. A. (2011). The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. Accident Analysis & Prevention, 43(5), 1666-1676. 

    Seraneeprakarn, P., Huang, S., Shankar, V., Mannering, F., Venkataraman, N., & Milton, J. (2017). Occupant injury severities in hybrid-vehicle involved crashes: A random parameters approach with heterogeneity in means and variances. Analytic methods in accident research, 15, 41-55. 

    Shaheed, M. S., & Gkritza, K. (2014). A latent class analysis of single-vehicle motorcycle crash severity outcomes. Analytic methods in accident research, 2, 30-38. 

    Stipancic, J., Zangenehpour, S., Miranda-Moreno, L., Saunier, N., & Granié, M.-A. (2016). Investigating the gender differences on bicycle-vehicle conflicts at urban intersections using an ordered logit methodology. Accident Analysis & Prevention, 97, 19-27. 

    Sun, M., Sun, X., & Shan, D. (2019). Pedestrian crash analysis with latent class clustering method. Accident Analysis & Prevention, 124, 50-57. 

    Tay, R., Choi, J., Kattan, L., & Khan, A. (2011). A multinomial logit model of pedestrian–vehicle crash severity. International journal of sustainable transportation, 5(4), 233-249. 

    Train, K. E. (2009). Discrete choice methods with simulation: Cambridge university press.

    Ulfarsson, G. F., Kim, S., & Booth, K. M. (2010). Analyzing fault in pedestrian–motor vehicle crashes in North Carolina. Accident Analysis & Prevention, 42(6), 1805-1813. 

    Wu, Q., Chen, F., Zhang, G., Liu, X. C., Wang, H., & Bogus, S. M. (2014). Mixed logit model-based driver injury severity investigations in single-and multi-vehicle crashes on rural two-lane highways. Accident Analysis & Prevention, 72, 105-115. 

    Wu, Q., Zhang, G., Zhu, X., Liu, X. C., & Tarefder, R. (2016). Analysis of driver injury severity in single-vehicle crashes on rural and urban roadways. Accident Analysis & Prevention, 94, 35-45. 

    Xie, Y., Zhao, K., & Huynh, N. (2012). Analysis of driver injury severity in rural single-vehicle crashes. Accident Analysis & Prevention, 47, 36-44. 

    Yau, K. K. (2004). Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong. Accident Analysis & Prevention, 36(3), 333-340. 

    Ye, F., & Lord, D. (2011). Investigation of effects of underreporting crash data on three commonly used traffic crash severity models: multinomial logit, ordered probit, and mixed logit. Transportation Research Record, 2241(1), 51-58. 

    Zahabi, S. A. H., Strauss, J., Manaugh, K., & Miranda-Moreno, L. F. (2011). Estimating potential effect of speed limits, built environment, and other factors on severity of pedestrian and cyclist injuries in crashes. Transportation research record, 2247(1), 81-90. 

    Zajac, S. S., & Ivan, J. N. (2003). Factors influencing injury severity of motor vehicle–crossing pedestrian crashes in rural Connecticut. Accident Analysis & Prevention, 35(3), 369-379. 

    Zhang, G., Cao, L., Hu, J., & Yang, K. H. (2008). A field data analysis of risk factors affecting the injury risks in vehicle-to-pedestrian crashes. Paper presented at the Annals of Advances in Automotive Medicine/Annual Scientific Conference.

    Zhang, G., Yau, K. K., & Zhang, X. (2014). Analyzing fault and severity in pedestrian–motor vehicle accidents in China. Accident Analysis & Prevention, 73, 141-150. 

    Zhang, J., Lindsay, J., Clarke, K., Robbins, G., & Mao, Y. (2000). Factors affecting the severity of motor vehicle traffic crashes involving elderly drivers in Ontario. Accident Analysis & Prevention, 32(1), 117-125. 

    Zhu, X., & Srinivasan, S. (2011). A comprehensive analysis of factors influencing the injury severity of large-truck crashes. Accident Analysis & Prevention, 43(1), 49-57.

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