交通事故非参数预测研究
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摘要
交通事故给人类社会造成巨大损失,为提高道路安全水平,世界各国均投入了大量人力、物力和财力进行交通事故研究,最大限度地防止交通事故发生。因此,揭示交通事故发生机理,建立交通事故预测模型,把握未来交通事故的发展趋势,对制定减少交通事故的对策,提高交通安全水平,具有重要的理论和现实意义。采用科学的预测方法建立道路事故预测模型,才能准确预计或推算交通事故的发展趋向、水平和程度。
本文采用非参数统计方法进行交通事故预测,侧重于理论体系和量化模型的构建,利用非参数统计方法定性预测交通事故形态和时空分布变化趋势,引入非参数回归模型定量地进行宏观事故预测。
在剖析事故的形态分布、时空分布特性基础上,采用适合交通事故分布趋势预测的Mann-Whitney、Cox-Stuart等模型进行预测黑龙江省近三年交通事故形态及时空分布的趋势。
以事故总数和总死亡人数之间的关系为研究对象,构建了基于核回归、多项式回归、稳健估计的交通事故非参数回归预测模型。模型特点在于,进行交通事故预测时无需过份关注变量和因变量之间的关系,突破了函数关系形式需要假定的限制。此外,考虑到不同的密度估计方法将产生不同的非参数回归预测函数,且不同的小波基也将分解出不同的非参数回归预测函数,提出了基于小波分析的非参数回归交通事故预测建模方法。
最后,根据近三年交通事故数据对未来年黑龙江省交通事故进行了非参数回归预测,通过聚类分析,给出相应的交通安全水平等级划分标准,对黑龙江省近、中、远期交通安全状况趋势进行了预测评价。
Nowadays, traffic crashes cause many death and great economic loss all over the world. And every country, especially in the western developed countries, invested great number of money, human recourses and energy to research on the traffic crashes in order to reduce the number of people died in the accidents and to improve the level of safety. It is necessary to predict the trend of future traffic safety to make effective transportation safety policy. Also Crash prediction model plays important role in explaining the reason why crashes’happening. So social organizer and government administer systems can benefit from research on traffic safety prediction model. As we all know, it is the scientific prediction models and methods that help us to get precise trend of development of traffic crashes and to acquire the level of safety.
This paper focuses on traffic prediction after introducing nonparametric statistics. At first, the future trend of time, space and forms of crashes distribution are discussed. Then nonparametric regression models are used for macro traffic prediction in Hei Longjiang province.
First of all, some useful and suitable nonparametric models are intruding into timely distribution of crashes. These power tools are used for crashes in different time and location. After that, some nonparametric test models, such as Mann-Whitney, Cox-Stuart and etc, are used for statistical analysis on the crashes. Finally, last 3 years crash data in Heilongjiang is taken for an example.
Secondly, the dissertation discusses the relationship between the number of crashes and the number of death caused by traffic accidents. In order to break through the bottleneck that sometimes function formation or the requirement of presuppose, nonparametric regression methods by kernel estimation of Nadaraya-Watson, kernel estimation by multinomial, and Locally Weighted EstimationScatter plot Smoothing (LOWESS) are introduced into the death prediction. Besides, considering different nonparametric regression methods can be dispatched by different wavelets, such as Schauder, Haar, and Daubechies, three nonparametric regression methods bases on wavelet analysis are put forward to predict the crashes.
Finally, the future safety situation in Hei Longjiang province is predicted according the recent 3 years data. Furthermore, the scale of traffic safety is made out according K-M clustering analysis.
本文采用非参数统计方法进行交通事故预测,侧重于理论体系和量化模型的构建,利用非参数统计方法定性预测交通事故形态和时空分布变化趋势,引入非参数回归模型定量地进行宏观事故预测。
在剖析事故的形态分布、时空分布特性基础上,采用适合交通事故分布趋势预测的Mann-Whitney、Cox-Stuart等模型进行预测黑龙江省近三年交通事故形态及时空分布的趋势。
以事故总数和总死亡人数之间的关系为研究对象,构建了基于核回归、多项式回归、稳健估计的交通事故非参数回归预测模型。模型特点在于,进行交通事故预测时无需过份关注变量和因变量之间的关系,突破了函数关系形式需要假定的限制。此外,考虑到不同的密度估计方法将产生不同的非参数回归预测函数,且不同的小波基也将分解出不同的非参数回归预测函数,提出了基于小波分析的非参数回归交通事故预测建模方法。
最后,根据近三年交通事故数据对未来年黑龙江省交通事故进行了非参数回归预测,通过聚类分析,给出相应的交通安全水平等级划分标准,对黑龙江省近、中、远期交通安全状况趋势进行了预测评价。
Nowadays, traffic crashes cause many death and great economic loss all over the world. And every country, especially in the western developed countries, invested great number of money, human recourses and energy to research on the traffic crashes in order to reduce the number of people died in the accidents and to improve the level of safety. It is necessary to predict the trend of future traffic safety to make effective transportation safety policy. Also Crash prediction model plays important role in explaining the reason why crashes’happening. So social organizer and government administer systems can benefit from research on traffic safety prediction model. As we all know, it is the scientific prediction models and methods that help us to get precise trend of development of traffic crashes and to acquire the level of safety.
This paper focuses on traffic prediction after introducing nonparametric statistics. At first, the future trend of time, space and forms of crashes distribution are discussed. Then nonparametric regression models are used for macro traffic prediction in Hei Longjiang province.
First of all, some useful and suitable nonparametric models are intruding into timely distribution of crashes. These power tools are used for crashes in different time and location. After that, some nonparametric test models, such as Mann-Whitney, Cox-Stuart and etc, are used for statistical analysis on the crashes. Finally, last 3 years crash data in Heilongjiang is taken for an example.
Secondly, the dissertation discusses the relationship between the number of crashes and the number of death caused by traffic accidents. In order to break through the bottleneck that sometimes function formation or the requirement of presuppose, nonparametric regression methods by kernel estimation of Nadaraya-Watson, kernel estimation by multinomial, and Locally Weighted EstimationScatter plot Smoothing (LOWESS) are introduced into the death prediction. Besides, considering different nonparametric regression methods can be dispatched by different wavelets, such as Schauder, Haar, and Daubechies, three nonparametric regression methods bases on wavelet analysis are put forward to predict the crashes.
Finally, the future safety situation in Hei Longjiang province is predicted according the recent 3 years data. Furthermore, the scale of traffic safety is made out according K-M clustering analysis.
引文
1.裴玉龙,王玮.道路交通事故成因及预防对策[M].北京:科学出版社, 2004, 10~30
2.沈斐敏,张荣贵.道路交通事故预测与预防[M].北京:人民交通出版社, 2007, 98~105
3.刘小明,朱弘戈.基于数量化理论的高速公路交通事故预测方法[J].道路交通与安全, 2003 (1), 121~131
4. Lord.D., Manar.A., Vizioli, A. Modeling Crash-Flow-Density and Crash -Flow-V/C Ration Relationships for Rural and Urbal Freeway Segments [J]. Presented at the 83rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2004
5. Garber, N. J., Ehrhart. A. A Final Report: The effect of speed, flow, and geometric characteristics on crash rates for different typers of Virginia highways. [R]. Virginia Transportation Research Council, Charlottesville, Virginia, January 1991
6. Rune Elvik, Peter Christensen, Astrid Amundsen, Speed and road accidents: An evaluation of the power model [J]. TOI report, December 2004.
7. Richard A. Retting, Helen B. Weinsteinb, Mark G. Solomon. Analysis of motor-vehicle crashes at stop signs in four U.S. cities [J]. Journal of Safety Research. 2003. 34: 485~489
8. Marc Graudry, Sylvain Lassarre.Structural Road Accident Models [J]. PERGAMON, 2000: 225~235
9. Ruediger Lamm, Basil Psarianos, Theodor Mailaender. Highway Design and Traffic Safety Engineering Handbook [M]. R.R. Donnelley & Sons Company. 1999: 93~98
10. Compton RP et al.Crash rish of alcohol impaired driving. In: Mayhew DR, Dussault C, eds. proceeding of the 16th International Conference on Alcohol, Drugs and Traffic Safety. August 2002: 123~125
11.张殿业.驾驶员动态视野与行车安全可靠度[J].西南交通大学学报. 2000,35(3): 319~322
12. Wen Cheng, Simon P. Washington. Experimental evaluation of hotspot identification methods [J]. Accident Analysis and Prevention 37 (2005) 870~881
13. Dominique Lorda, Peter Young, Jin Park, Investigating the effects of the fixedand varying dispersion parameters of poisson-gamma models on empirical Bayes estimates [J]. Accident Analysis and Prevention 40 (2008): 1441~1457.
14.应朝阳.美国关于行车交通安全的研究[J].汽车与安全, 2006,6: 13~16
15. Luis Miranda-Moreno, Aurelie Labbe, Liping Fu, Bayesian multiple testing procedures for hotspot identification [J]. Accident Analysis and Prevention 39 2007: 1192~1201.
16. Bowman B.L., Vecellio R.L., Miao J. Vehicle and Pedestrian Accident Models for Median Locations [J]. Journal of Transportation Engineering. 1995,121(6): 531~537
17. James H. Lambert, Kenneth D. Peterson, Nilesh N. Joshi Synthesis of quantitative and qualitative evidence for accident analysis in risk-based highway planning [J]. Accident Analysis and Prevention 38 (2006): 925~935.
18. Praprut Songchitruksa, Andrew P. Tarko. The extreme value theory approach to safety estimation Accident [J]. Analysis and Prevention 38 (2006): 811~822.
19. Zaid Sawalha, Tarek Sayed, Mavis Johnson. Factors Affecting the Safety of Urban Arterial Roadways [C]. Transportation Research Board 2000 Annual Meeting CD-ROM
20. Jodi Carson, Fred Mannering. The Effect of Ice Warning Signs on Ice~accident Frequencies and Severities [J]. Accident Analysis & Prevention. 2001, 33: 99~109
21. Yasuo Mori, Mitsuo Mizohata. Characteristics of older road users and their effect on road safety [J]. Accident Analysis & Prevention 2002: 391~404
22. J.A.Stoop. Accidents-In-depth Analysis towards a method AIDA [J]. Safety Science.19(1995): 125~136
23. Nakmoon Sung W.C. Taylor, M.Vincent.Another Look at Identifying Hazardous Locations [C]. Transportation Research Board 2003 Annual Meeting CD-ROM.
24.裴玉龙,程国柱.高速公路车速离散性与交通事故的关系及车速管理研究[J].中国公路学报. 2004.3: 14~18
25.孟祥海.规划阶段城市干道网潜在事故多发点鉴别及改善研究[D].哈尔滨工业大学博士论文, 2007, 103~105
26.秦利燕,许洪国.基于量化理论的高速公路交通安全评价模型[J].山东交通学院学报, 2002,12: 25~39
27.秦小虎,刘利,张颖.一种基于贝叶斯网络模型的交通事故预测方法[J].计算机仿真. 2005, 22(11): 230~232
28.陆秀荣.南昌市道路交通事故成因分析与预防[J].华东交通大学学报. 2006.2: 56~59
29.兰志雄,宋继宏,陈永胜,王广山.道路设计与规划的安全研究[J].华东公路. 2001,6: 68~74
30.刘秀清,宇仁德,王晓原,范东凯.基于最小二乘法的道路交通事故预测机理模型[J].数学的实践与认识. 2008(23): 109~113
31.林震,杨浩.基于车速的交通事故贝叶斯预测[J].中国安全科学学报. 2003,13(02): 34~36
32.崔洪军,魏连雨,庞建勋.道路条件与交通安全的研究方法[J].西安公路交通大学学报. 2003: 55~58
33.胡燕,邵毅明,徐进,束海波.道路交通事故多发路段仿真识别研究[J].淮阴工学院学报. 2005, 14(3): 62~64
34.胡新民,刘涛.道路黑点识别与改善[J].交通运输工程学报2004,4(1): 106~109
35. Pei Yulong. The Outstanding Factor Method to Differentiate the Black Spot of the Road [C]. The 15th International Road Federation World Meeting. 2005: 16~20
36.裴玉龙,戴彤宇.鉴别道路交通事故多发点的模糊评价法[J].公路交通科技.2005, 22(6): 121~125
37.夏劲,郭红卫.国内外城市智能交通系统的发展概况与趋势及其启示[J].科技进步与对策, 2003 (1): 176~179.
38.韩凤春,曹金璇.平面交叉口的安全设计[J].公安大学学报. 2002,5(5): 64~67
39.杨立才,贾磊,何立琴,基于混沌小波网络的交通流预测算法研究[J].山东大学学报. 2005, 35(2): 46~50.
40.苏梁,邵东,唐伯明,徐松.灰色理论在交通事故预测的应用[J].重庆交通大学学报. 2008, Vol. 27. No.3: 446~449.
41.梁亚莉.道路交通事故预测方法分析[J].山西科技. 2006, 2: 99~101.
42. W.J Conover.实用非参数统计[M].北京:人民邮电出版社, 2006: 10~45
43.杨芳明,朱顺应.基于小波的短时交通流预测. [J].重庆交通学院学报, 2006, 25 (3): 99~102.
44.王星.非参数统计[M].北京:中国人民大学出版社, 2004: 210~220
45. Brian L.Smith, Billy M.Willianms, R. Keith Oswals Comparison parametric and Nonparametric Traffic Volume Forecasting [J], Transportation Research Part C. Emerging Technologies, 2002, 10(4): 303~321
46. A Brief Statistical Summary Published by the Mathematical Analysis DivisionDepartment of Transportation [M]. 2005
47.邹银煌.贝叶斯原理与方法及其在医药新产品市场风险中的应用[D].暨南大学硕士学位论文. 2001: 139~150
48. Higle J.L., Witkowski J.M. Bayesian identification of hazardous locations [J] .Transportation Research Record 1185. 1988. 24~36
49.黄燕,吴平. SAS统计分析于应用[M].北京:机械工程出版社,2005: 151~160
50. A Brief Statistical Summary Published by the Mathematical Analysis Division Department of Transportation http://www.nrd.nhtsa.dot.gov/cats/listpublications
51. Khattak A J, Knapp K K, Giese K L and Smithson L D. Safety implications of snowstorms on interstate highways [J]. Presented at the Annual Meeting of the Transportation Research Board, January, Washington, D. C.,2000.
52. Perry, A.H., and Symons, L.J.. Highway Metrology [M], University of Wales,Swansea, Wales, United Kingdom, 1991: 132~140
53.刘运通.道路交通安全指南[M].北京:人民交通出版社. 2004: 132~135
54.中华人民共和国国家统计局. http://www.stats.gov.cn/tjgb/
2.沈斐敏,张荣贵.道路交通事故预测与预防[M].北京:人民交通出版社, 2007, 98~105
3.刘小明,朱弘戈.基于数量化理论的高速公路交通事故预测方法[J].道路交通与安全, 2003 (1), 121~131
4. Lord.D., Manar.A., Vizioli, A. Modeling Crash-Flow-Density and Crash -Flow-V/C Ration Relationships for Rural and Urbal Freeway Segments [J]. Presented at the 83rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2004
5. Garber, N. J., Ehrhart. A. A Final Report: The effect of speed, flow, and geometric characteristics on crash rates for different typers of Virginia highways. [R]. Virginia Transportation Research Council, Charlottesville, Virginia, January 1991
6. Rune Elvik, Peter Christensen, Astrid Amundsen, Speed and road accidents: An evaluation of the power model [J]. TOI report, December 2004.
7. Richard A. Retting, Helen B. Weinsteinb, Mark G. Solomon. Analysis of motor-vehicle crashes at stop signs in four U.S. cities [J]. Journal of Safety Research. 2003. 34: 485~489
8. Marc Graudry, Sylvain Lassarre.Structural Road Accident Models [J]. PERGAMON, 2000: 225~235
9. Ruediger Lamm, Basil Psarianos, Theodor Mailaender. Highway Design and Traffic Safety Engineering Handbook [M]. R.R. Donnelley & Sons Company. 1999: 93~98
10. Compton RP et al.Crash rish of alcohol impaired driving. In: Mayhew DR, Dussault C, eds. proceeding of the 16th International Conference on Alcohol, Drugs and Traffic Safety. August 2002: 123~125
11.张殿业.驾驶员动态视野与行车安全可靠度[J].西南交通大学学报. 2000,35(3): 319~322
12. Wen Cheng, Simon P. Washington. Experimental evaluation of hotspot identification methods [J]. Accident Analysis and Prevention 37 (2005) 870~881
13. Dominique Lorda, Peter Young, Jin Park, Investigating the effects of the fixedand varying dispersion parameters of poisson-gamma models on empirical Bayes estimates [J]. Accident Analysis and Prevention 40 (2008): 1441~1457.
14.应朝阳.美国关于行车交通安全的研究[J].汽车与安全, 2006,6: 13~16
15. Luis Miranda-Moreno, Aurelie Labbe, Liping Fu, Bayesian multiple testing procedures for hotspot identification [J]. Accident Analysis and Prevention 39 2007: 1192~1201.
16. Bowman B.L., Vecellio R.L., Miao J. Vehicle and Pedestrian Accident Models for Median Locations [J]. Journal of Transportation Engineering. 1995,121(6): 531~537
17. James H. Lambert, Kenneth D. Peterson, Nilesh N. Joshi Synthesis of quantitative and qualitative evidence for accident analysis in risk-based highway planning [J]. Accident Analysis and Prevention 38 (2006): 925~935.
18. Praprut Songchitruksa, Andrew P. Tarko. The extreme value theory approach to safety estimation Accident [J]. Analysis and Prevention 38 (2006): 811~822.
19. Zaid Sawalha, Tarek Sayed, Mavis Johnson. Factors Affecting the Safety of Urban Arterial Roadways [C]. Transportation Research Board 2000 Annual Meeting CD-ROM
20. Jodi Carson, Fred Mannering. The Effect of Ice Warning Signs on Ice~accident Frequencies and Severities [J]. Accident Analysis & Prevention. 2001, 33: 99~109
21. Yasuo Mori, Mitsuo Mizohata. Characteristics of older road users and their effect on road safety [J]. Accident Analysis & Prevention 2002: 391~404
22. J.A.Stoop. Accidents-In-depth Analysis towards a method AIDA [J]. Safety Science.19(1995): 125~136
23. Nakmoon Sung W.C. Taylor, M.Vincent.Another Look at Identifying Hazardous Locations [C]. Transportation Research Board 2003 Annual Meeting CD-ROM.
24.裴玉龙,程国柱.高速公路车速离散性与交通事故的关系及车速管理研究[J].中国公路学报. 2004.3: 14~18
25.孟祥海.规划阶段城市干道网潜在事故多发点鉴别及改善研究[D].哈尔滨工业大学博士论文, 2007, 103~105
26.秦利燕,许洪国.基于量化理论的高速公路交通安全评价模型[J].山东交通学院学报, 2002,12: 25~39
27.秦小虎,刘利,张颖.一种基于贝叶斯网络模型的交通事故预测方法[J].计算机仿真. 2005, 22(11): 230~232
28.陆秀荣.南昌市道路交通事故成因分析与预防[J].华东交通大学学报. 2006.2: 56~59
29.兰志雄,宋继宏,陈永胜,王广山.道路设计与规划的安全研究[J].华东公路. 2001,6: 68~74
30.刘秀清,宇仁德,王晓原,范东凯.基于最小二乘法的道路交通事故预测机理模型[J].数学的实践与认识. 2008(23): 109~113
31.林震,杨浩.基于车速的交通事故贝叶斯预测[J].中国安全科学学报. 2003,13(02): 34~36
32.崔洪军,魏连雨,庞建勋.道路条件与交通安全的研究方法[J].西安公路交通大学学报. 2003: 55~58
33.胡燕,邵毅明,徐进,束海波.道路交通事故多发路段仿真识别研究[J].淮阴工学院学报. 2005, 14(3): 62~64
34.胡新民,刘涛.道路黑点识别与改善[J].交通运输工程学报2004,4(1): 106~109
35. Pei Yulong. The Outstanding Factor Method to Differentiate the Black Spot of the Road [C]. The 15th International Road Federation World Meeting. 2005: 16~20
36.裴玉龙,戴彤宇.鉴别道路交通事故多发点的模糊评价法[J].公路交通科技.2005, 22(6): 121~125
37.夏劲,郭红卫.国内外城市智能交通系统的发展概况与趋势及其启示[J].科技进步与对策, 2003 (1): 176~179.
38.韩凤春,曹金璇.平面交叉口的安全设计[J].公安大学学报. 2002,5(5): 64~67
39.杨立才,贾磊,何立琴,基于混沌小波网络的交通流预测算法研究[J].山东大学学报. 2005, 35(2): 46~50.
40.苏梁,邵东,唐伯明,徐松.灰色理论在交通事故预测的应用[J].重庆交通大学学报. 2008, Vol. 27. No.3: 446~449.
41.梁亚莉.道路交通事故预测方法分析[J].山西科技. 2006, 2: 99~101.
42. W.J Conover.实用非参数统计[M].北京:人民邮电出版社, 2006: 10~45
43.杨芳明,朱顺应.基于小波的短时交通流预测. [J].重庆交通学院学报, 2006, 25 (3): 99~102.
44.王星.非参数统计[M].北京:中国人民大学出版社, 2004: 210~220
45. Brian L.Smith, Billy M.Willianms, R. Keith Oswals Comparison parametric and Nonparametric Traffic Volume Forecasting [J], Transportation Research Part C. Emerging Technologies, 2002, 10(4): 303~321
46. A Brief Statistical Summary Published by the Mathematical Analysis DivisionDepartment of Transportation [M]. 2005
47.邹银煌.贝叶斯原理与方法及其在医药新产品市场风险中的应用[D].暨南大学硕士学位论文. 2001: 139~150
48. Higle J.L., Witkowski J.M. Bayesian identification of hazardous locations [J] .Transportation Research Record 1185. 1988. 24~36
49.黄燕,吴平. SAS统计分析于应用[M].北京:机械工程出版社,2005: 151~160
50. A Brief Statistical Summary Published by the Mathematical Analysis Division Department of Transportation http://www.nrd.nhtsa.dot.gov/cats/listpublications
51. Khattak A J, Knapp K K, Giese K L and Smithson L D. Safety implications of snowstorms on interstate highways [J]. Presented at the Annual Meeting of the Transportation Research Board, January, Washington, D. C.,2000.
52. Perry, A.H., and Symons, L.J.. Highway Metrology [M], University of Wales,Swansea, Wales, United Kingdom, 1991: 132~140
53.刘运通.道路交通安全指南[M].北京:人民交通出版社. 2004: 132~135
54.中华人民共和国国家统计局. http://www.stats.gov.cn/tjgb/