中国31个省市道路交通事故现状分析及实现SDGs死伤减半目标趋势预测

龚雪蕾; 方菁; 谭晓萍; 廖爱梅; 肖传浩

doi:10.16462/j.cnki.zhjbkz.2020.01.002

中国31个省市道路交通事故现状分析及实现SDGs死伤减半目标趋势预测

doi: 10.16462/j.cnki.zhjbkz.2020.01.002

650500 昆明, 昆明医科大学公共卫生学院健康研究所

基金项目: 比尔及梅琳达·盖茨基金会在中国实现联合国健康相关可持续发展目标:开发循证政策行动建议国际合作项目(2830622)

详细信息

通讯作者:
方菁, E-mail:fangjing07@126.com

中图分类号: R181.2
计量
- 文章访问数: 361
- HTML全文浏览量: 150
- PDF下载量: 54
- 被引次数: 0
出版历程
- 收稿日期: 2019-05-09
- 修回日期: 2019-10-19
- 刊出日期: 2020-01-10

Analysis of the current situation of road traffic accidents in the 31 provinces/municipalities of China and the projection for achieving the SDGs target of halving the numbers of death and injury

Health Institude, School of Public Health, Kunming Medical University, Kunming 650500, China

Funds: Bill & Melinda Gates Foundation."Achieving Health SDGs in China:Developing Evidence-based Policy Options for Action"International Cooperation Project(2830622)

More Information

Corresponding author: FANG Jing, E-mail:fangjing07@126.com

摘要

摘要: 目的分析2010-2017年我国31个省市道路交通事故发生数和死伤情况, 预测到2020年各省市实现可持续发展目标(sustainable development goals, SDGs)中设定的死伤人数较2015年减半的情况。方法分析31个省市道路交通事故现状, 利用趋势外推法预测2020年能否实现SDGs目标。结果 2010-2017年广东、江苏、浙江、山东的交通事故发生数、死伤数呈下降趋势但仍居前列。2017年死亡率前两位是北京和贵州, 受伤率贵州和天津居高。预测显示, 到2020年全国道路交通事故的死亡和受伤人数分别增加了17%和1.3%, 难以实现减半的目标; 预计2020年湖南的死伤人数分别降至2015年的50.7%和65.3%, 能够达到目标; 上海的受伤人数降低83.3%, 但死亡人数只降低34.5%, 与目标有差距; 其余省市难以达到目标。预计到2020年11个省市的死亡人数将增加, 其中北京、吉林、江西、湖北和贵州增长最为明显; 有8个省市的受伤人数呈增长趋势, 其中吉林、江西、湖北和贵州四省增幅明显。结论除湖南省外, 全国和各省市均难以达到SDGs目标, 可以根据预测结果和实际现状制定有针对性的科学防控策略。湖南和上海在交通事故防控方面的措施有效, 值得借鉴。
- 道路交通事故 /
- 可持续发展目标 /
- 趋势外推法
Abstract: Objective To analyze the data of road traffic accidents, deaths and injuries in 31 provinces and municipalities in China from 2010 to 2017, and to predict that the number of deaths and injuries caused by road accidents set in the sustainable development goals(SDGs) will be halved by 2020 compared with the target set in 2015. Methods Describing and analyzing the current situation of road traffic accidents in 31 provinces and municipalities in China, and using the trend extrapolation method to predict whether the SDGs target can be achieved by 2020. Results The numbers of traffic accidents, deaths and injuries in Guangdong, Jiangsu, Zhejiang and Shandong Provinces showed a downward trend from 2010 to 2017, but still ranked the front in China. In 2017, Beijing had the highest death rate followed by Guizhou as the second, and Guizhou had the highest injury rate followed by Tianjin as the second. The projected results showed that the numbers of deaths and injuries caused by road traffic accidents in China would be increased by 17% and 1.3% respectively in 2020 indicating that SDGs target could not be met. Among the 31 provinces/municipalities, it was predicted that the numbers of deaths and injuries in Hunan would be reduced to 50.7% and 65.3% in 2015, respectively by 2020, and the target could be achieved; the number of injuries in Shanghai was decreased by 83.3%, but the deaths was only decreased by 34.5%, and there was still a gap with the target; the rest 29 provinces/municipalities could not meet SDGs target. It was expected that the deaths in 11 provinces and municipalities would increase by 2020, with Beijing, Jilin, Jiangxi, Hubei and Guizhou Provinces showing the most significant growth. The number of injured people in 8 provinces and municipalities shows an increasing trend, with Jilin, Jiangxi, Hubei and Guizhou provinces showing significant growth. Conclusions Except for Hunan Province, it was difficult for the whole country and the rest provinces and municipalities to reach the SDGs target. According to the forecast results and the actual situation, a targeted scientific prevention and control strategy can be formulated. The measures taken by Hunan and Shanghai in traffic accident prevention and control were effective and worth learning.
- Road traffic accidents /
- Sustainable development goals /
- Trend extrapolation method

HTML全文

图 1 2017年全国31个省市交通事故发生起数分布图

Figure 1. Number of traffic accidents in 31 provinces and municipalities in China mainland in 2017

下载: 全尺寸图片幻灯片

表 1 全国31个省市2010-2017年交通事故死亡人数(人)及2018-2020年的预测值

Table 1. The number of deaths in traffic accidents in 31 provinces and municipalities in mainland China from 2010 to 2017 and their predicted values from 2018 to 2020

	实际值	预测值			幅度 (倍/%)	达标情况
	2015(基年)	2018	2019	2020	幅度 (倍/%)	达标情况
全国	58 022	65 106	66 469	67 860	+17	难
北京	922	1543	1 727	1 934	+1.1^a	难
天津	826	805	797	790	-4.4	较难
河北	2 498	2 491	2 486	2 482	-0.7	较难
山西	2 015	2 124	2 123	2 122	+5.3	难
内蒙古	973	971	929	890	-8.5	较难
辽宁	1 993	1 957	1 944	1 931	-3.1	较难
吉林	1 301	2 253	2 522	2 822	+1.2^a	难
黑龙江	1 151	1 127	1 117	1 106	-3.9	较难
上海	869	638	603	569	-34.5	有差距
江苏	4 642	4 530	4 494	4 458	-4	较难
浙江	4 275	3 656	3 483	3 319	-22.4	有差距
安徽	2 651	2 701	2 709	2 718	+2.5	难
福建	1 890	1 773	1 673	1 578	-16.5	较难
江西	1 439	2 358	2 620	2 911	+1^a	难
山东	3 652	3 648	3 631	3 614	-1.04	较难
河南	1 776	2 203	2 337	2 480	+39.6	难
湖北	1 695	6 190	8 025	10 404	+5.1^a	难
湖南	1 792	1 057	967	884	-50.7	已达标
广东	5 562	5 262	5 179	5 098	-8.3	较难
广西	2 099	2 270	2 293	2 316	+10.4	难
海南	629	712	751	793	+26.1	难
重庆	969	939	932	924	-4.6	较难
四川	2 640	2 074	1 987	1 903	-27.9	有差距
贵州	741	5 217	7 839	11 779	+14.9^a	难
云南	3 036	3 108	3 308	3 522	+16	难
西藏	168	151	138	126	-25	有差距
陕西	1 615	1 513	1 466	1 421	-12	较难
甘肃	1 396	1 295	1 271	1 247	-10.6	较难
青海	531	525	522	520	-2.1	较难
宁夏	395	370	365	359	-9.1	较难
新疆	1 881	1 614	1 569	1 525	-18.9	较难
注：^a表示2020年与2015年相比，上升或降低多少倍，不足1倍用%表示，即2020年在2015年的基础上，上升或降低百分之几；“+”表示与2015年相比，2020年上升；“-”表示下降。下表同。达标情况：与2015年相比，2020年预测的死亡人数上升，判断为“难”；下降20%以内，判断为“较难”；下降20%—40%，判断为“有差距”；下降40%~50%，判断为“可能达标”；下降50%以上，判断为“已达标”。下表同。

下载: 导出CSV

表 2 全国31个省市2010-2017年交通事故受伤人数(人)及其2018-2020年预测值

Table 2. The number of injuries caused by traffic accidents in 31 provinces and municipalities in mainland China from 2010 to 2017 and their predicted values from 2018 to 2020

	实际值	预测值			幅度 (倍/%)	达标情况
	2015(基年)	2018	2019	2020	幅度 (倍/%)	达标情况
全国	199 880	207 259	204 890	202 549	+1.3	难
北京	2 617	2 628	2 464	2 310	-11.7	较难
天津	5 954	5 878	6 234	6 612	+11.1	难
河北	4 319	4 388	4 349	4 311	-0.2	较难
山西	5 495	4 717	4 509	4 310	-21.6	有差距
内蒙古	3 121	3 486	3 492	3 499	-12.1	较难
辽宁	4 774	4 098	3 874	3 662	-23.3	有差距
吉林	2 697	6 593	7 825	9 286	+2.4^a	难
黑龙江	3 442	3 927	4 018	4 110	+19.4	难
上海	454	155	108	76	-83.3	已达标
江苏	11 698	11 431	11 269	11 110	-5	较难
浙江	16 157	11 062	10 063	9 154	-43.3	可能达标
安徽	15 382	11 644	10 588	9 627	-37.4	有差距
福建	8 737	9 139	8 947	8 760	+0.26	难
江西	3 136	6 840	8 071	9 524	+2^a	难
山东	13 002	12 515	12 436	12 357	-5	较难
河南	6 129	6 079	6 036	5 993	-2.2	较难
湖北	4 638	11 620	12 197	12 803	+1.8^a	难
湖南	11 615	5 633	4 765	4 032	-65.3	已达标
广东	27 754	23 117	21 833	20 620	-25.7	有差距
广西	4 009	3 560	3 463	3 368	-16	较难
海南	2 903	2 521	2 563	2 607	-10.2	较难
重庆	6 577	5 537	5 188	4 862	-26.1	有差距
四川	10 188	6 654	5 919	5 265	-48.3	可能达标
贵州	1 150	29 890	41 183	54 278	+46^a	难
云南	5 914	5 450	5 396	5 343	-9.7	较难
西藏	435	380	338	301	-30.9	有差距
陕西	5 137	5 502	5 426	5 352	+4.2	难
甘肃	3 553	3 053	2 977	2 904	-18.3	较难
青海	1 183	1 135	1 097	1 060	-10.4	较难
宁夏	1 963	1 578	1 485	1 398	-28.8	有差距
新疆	5 747	5 460	5 399	5 339	-7.1	较难
^a表示上升或降低的倍数，未标注的均为上升或降低的幅度(%)。

下载: 导出CSV

参考文献(12)

[1]	Nantulya VM, Reich MR. The neglected epidemic: road traffic injuries in developing countries[J]. BMJ. 2002, 11;324(7346): 1139-1141. DOI: 10.1136/bmj.324.7346.1139.
[2]	World Health Organization. Global status report on road safety 2018[EB/OL]. (2018-12-7)[2019-4-26]. https://www.who.int/violence_injury_prevention/road_safety_status/2018/en/.
[3]	United Nations General Assembly. Transforming our world: the 2030 agenda for sustainable development[EB/OL]. (2015-8-12)[2019-4-27]. https://sustainabledevelopment.un.org/post 2015/transformingourworld.
[4]	孟祥海, 蒋艳辉, 郑洪岚.全国道路交通事故死亡人数预测研究[J].公路交通技术, 2017, 33(5): 126-131. DOI: 10.13607/j.cnki.gljt.2017.05.025. Meng XH, Jiang YH, Zheng HL, et al. Prediction of the number of deaths from road traffic accidents nationwide[J]. Technology of Highway and Transport, 2017, 33(5): 126-131. DOI: 10.13607/j.cnki.gljt.2017.05.025.
[5]	杨书霞, 胡艳.基于动态灰色预测的交通事故损失预测[J].武汉理工大学学报(信息与管理工程版), 2017, 39(6): 674-678. DOI: 10.3963/j.issn.2095-3852.2017.06.006. Yang SX, Hu Y. Traffic accident loss prediction based on dynamic grey prediction[J]. Journal of WUT(Information & Management Engineering), 2017, 39(6): 674-678. DOI: 10.3963/j.issn.2095-3852.2017.06.006.
[6]	赵玲, 许宏科.基于改进的灰色马尔可夫链模型的交通事故预测[J].数学的实践与认识, 2013, 43(20): 92-98. DOI: 10.3969/j.issn.1000-0984.2013.20.013. Zhao L, Xu HK. Traffic accident prediction based on improved grey markov chain model[J]. Mathematics In Practice and Theory, 2013, 43(20): 92-98. DOI: 10.3969/j.issn.1000-0984.2013.20.013.
[7]	唐秋生, 杜营营.基于BP神经网络的交通事故预测模型及仿真[J].交通信息与安全, 2011, 29(1): 68-70. DOI: 10.3963/j.ISSN1674-4861.2011.01.017. Tang QS, Du YY. Traffic accident prediction model and simulation based on bp neural network[J]. Traffic Information and Security, 2011, 29(1): 68-70. DOI: 10.3963/j.ISSN1674-4861.2011.01.017.
[8]	GBD SDG Collaborators. Measuring progress and projecting attainment on the basis of past trends of the health-related sustainable development goals in 188 countries: an analysis from the global burden of disease study 2016[J]. Lancet, 2017, 390(10100): 1423. DOI: 10.1016/S0140-6736(17)32336-X.
[9]	沈坤.我国道路交通事故死亡人数预测与管理对策[J].安全与环境工程, 2017, 24(5): 138-144. DOI: 10.13578/j.cnki.issn.1671-1556.2017.05.025. Shen K. Forecast and management countermeasures of deaths in road traffic accidents in China[J]. Safety and Environmental Engineering, 2017, 24(5): 138-144. DOI: 10.13578/j.cnki.issn.1671-1556.2017.05.025.
[10]	陈锦华, 黄琳, 吴爱平.福建省卫生人力及床位预测方法探讨[J].华南国防医学杂志, 2009, 23(6): 60-62. DOI: 10.13730/j.1009-2595.2009.06.001. Chen JH, Huang L, Wu AP. Forecasting methods of medical human and bed resources in Fujian Province[J]. South China National Defense Medical Journal, 2009, 23(6): 60-62. DOI: 10.13730/j.1009-2595.2009.06.001.
[11]	刘守信.青海玉树"1·3"特大交通事故的教训与反思[J].道路交通管理, 2006, (3): 30-34. http://www.cnki.com.cn/Article/CJFDTotal-DLJG200603023.htm Liu SX. Lessons and reflections on the "1·3" extraordinary traffic accident in Yushu, Qinghai Province[J]. Road Traffic Management, 2006, (3): 30-34. http://www.cnki.com.cn/Article/CJFDTotal-DLJG200603023.htm
[12]	湖南省人民政府网. 2018年湖南道路交通事故万车死亡率降至2.14[EB/OL]. (2019-3-23)[2019-4-28]. http://www.hunan.gov.cn/hnyw/bmdt/201903/t20190323_5300150.html. Hunan Provincial People's Government Network.The number of deaths per 10, 000 vehicles from road accidents in Hunan dropped to 2.14 in 2018[EB/OL].(2019-3-23)[2019-4-28].http://www.hunan.gov.cn/hnyw/bmdt/201903/t20190323_5300150.html.