A Review of Current Situation and Hot Spots of Road Safety Research
-
摘要: 道路交通事故的产生对民众的生命安全和财产损失影响巨大，国内外学者在该方面进行了大量的研究。为了整体把握道路交通事故研究热点及发展趋势，从中国知网(CNKI)核心期刊数据库和Web of Science核心合集数据库选取了2000—2020年与道路交通事故相关的3 943篇文献为数据源，借助CiteSpace和VOSviewer文献计量软件平台从文献分布特征、关键词共现、关键词聚类、关键词突现等方面进行分析，并在此基础上从事故黑点鉴别与影响因素分析、事故安全评价与事故预测、事故伤害(RTI)的流行病学研究和预防、事故处理与安全管理、事故仿真与驾驶行为分析这5个研究方向分析道路交通安全的研究趋势与热点问题。研究表明：①从作者合作方面分析发现道路交通事故研究具有多学科交叉性质；②对关键词共现分析发现国内外期刊关键词共现类别基本一致，说明国内外对道路交通事故方面的研究具有较强的一致性；③数据分析发现当前研究还存在实时交通事故评价手段欠缺、道路交通伤害数据结构不统一、事故仿真模型的通用性与有效性有待于进一步提高等问题；④从研究趋势的演进来看，未来的研究趋势主要集中在道路交通事故侵权责任研究、道路交通事故对道路通行能力的影响等方面。Abstract: Due to its great impacts on people's life and property loss, road safety research has been gained more and more attention in China and abroad. Inorder to grasp state of the art and the practice of road safety research, 3 943 papers related to road accidents from 2000 to 2020 are selected from the core periodical database in China National Knowledge Infrastructure(CNKI)and the core collection database of Web of Science.These papers are analyzed based on their publication year, distribution of journals, research institutions, scholars, and keywords, by using the CiteSpace and VOSviewer software. The research trends and hotspots of road safety have been reviewed from the following five aspects: identification of black spots and analysis of influencing factors, safety evaluation and prediction, epidemiological study and prevention of road traffic injury(RTI), response to accidents and safety management, accident simulation and driving behavior analysis. The results show that: ①road safety research has multi-disciplinary nature from the perspective of co-authorship analysis. ② Co-occurrence analysis of keywords shows that the categories of co-occurrence keywords in domestic and foreign journals are basically similar, which indicates that studies on road safetycarried out in Chinaare consistent with those abroad. ③Data analysis shows that there are still issues within the current research, such as the lack of real-time road safety evaluation methods, inconsistent data structure for accident-related injury data, and the effectiveness and applicability of accident simulation model need to be further improved. ④In terms of the evolution of road safety research, future research could mainly focus on tort liability and the impactsof accidents on road capacity.
-
Key words:
- road safety /
- state of the art /
- research topics /
- CiteSpace /
- VOSviewer
-
图 1 2000—2020年道路交通事故研究载文量分布图
Figure 1. Distribution of research papers on road safety from 2000 to 2020
图 3 Web of Science主要研究群体共现图谱
Figure 3. Web of Science cooccurrence map of main research groups
图 5 Web of Science关键词共现知识图谱
Figure 5. Web of Science keywords co-occurrence knowledge map
表 1 CNKI从事道路交通事故研究的代表性学者
Table 1. CNKI representative scholars engaged in road safety research
序号 中心性 作者 序号 被引频次 作者 1 15 周继红 1 19 王正国 2 14 王正国 2 9 周继红 3 10 李桢 3 8 刘锐 4 9 邱俊 4 7 付锐 5 9 李立 5 7 尹志勇 6 9 段腾龙 6 7 刘小明 7 9 张良 7 6 高建刚 8 9 蒋志全 8 5 赵玲 9 9 何永旺 9 5 邵春福 10 2 代维 10 5 刘浩学 
下载: 导出CSV
表 2 Web of Science从事道路交通事故研究的代表性学者
Table 2. Web of Science representative scholars engaged in road safety research
序号 中心性 作者 序号 被引频次 作者 1 7 Laumon Bernard 1 6 Park Kee B 2 6 Negishi Kazuno 2 5 Laumon Bernard 3 6 Yamagata Bun 3 5 Hitosugi Masahito 4 6 Yamamoto Yasuharu 4 5 Morland Jorg 5 6 Lauwaert Door 5 4 Charnay Pierrette 6 6 Hotta Ryo 6 4 Hours Martine 7 6 Tsutsumimoto Kota 7 4 Rehman Lal 8 6 Deynse Helena Van 8 4 Xing Yingying 9 6 Makizako Hyuma 9 4 Pereznuez Ricardo 10 6 Belleghem Griet Van 10 4 Lu Jian
下载: 导出CSV
表 3 CNKI期刊载文量分布
Table 3. CNKI distribution of journal articles
单位: 篇 期刊 数据库 2000—2005年 2006—2010年 2011—2015年 2016—2020年 合计 《公路》 北大核心 14 25 31 12 82 《公路交通科技》 CSCD 28 14 12 4 58 《中国安全科学学报》 CSCD 0 27 18 13 58 《中华创伤杂志》 CSCD 17 12 10 4 43 《中国公路学报》 EI 8 2 5 15 30 《中国法医学杂志》 CSCD 0 6 12 10 28 《长安大学学报(自然科学版)》 CSCD 9 9 2 6 26 《交通运输工程学报》 EI 1 17 6 1 25 《保险研究》 CSSCI 1 7 11 5 24 《重庆交通大学学报(自然科学版)》 CSCD 0 8 9 7 24 《中国安全生产科学技术》 CSCD 0 5 5 10 20 《中外公路》 北大核心 2 9 8 1 20 合计 80 141 129 88 438
下载: 导出CSV
表 4 Web of Science期刊共被引分析
Table 4. Web of Science analysis of journal co-citation
序号 被引频次 期刊 序号 中心性 期刊 1 924 Accident Analysis & Prevention 1 11 Acta Otorhinolaryngologica 2 526 Journal of Orthopaedic Trauma 2 10 American Journal of Psychiatry 3 396 International Journal of the Care of Injured 3 10 Journal of Neurotrauma 4 365 The Lancet 4 9 Addiction 5 363 Traffic Injury Prevention 5 8 BMCPublicHealth 6 283 Injury Prevention 6 8 Journal of Bone and Joint Surgery-American Volume 7 281 Journal of Safety Research 7 8 Spine 8 256 Transportation Research Record 8 8 Sleep 9 246 British Medical Joural 9 8 Journal of Oral and Maxillofacial Surgery Medicine and Pathology 10 222 Jama-journal of the American Medical Association 10 7 Journal of Cranio-maxillofacial Surgery
下载: 导出CSV
表 5 CNKI与Web of Science关键词共现中心性表
Table 5. CNKI and Web of Science keywords co-occurrence frequency and centrality table
序号 CNKI 序号 Web of science 关键词 中心性 关键词 中心性 1 道路交通事故 41 1 risk factor 9 2 交通安全 27 2 safety 9 3 交通工程 27 3 motor vehicle accident 9 4 高速公路 23 4 crash 8 5 影响因素 12 5 pattern 6 6 交强险 10 6 fracture 5 7 安全评价 9 7 driver 5 8 自动驾驶汽车 8 8 trend 48 9 运行速度 8 9 management 38 10 交通安全法 7 10 impact factor 27
下载: 导出CSV
-
[1] HE Y, YANG S, CHAN C Y, et al. Visualization analysis of intelligent vehicles research field based on mapping knowledge domain[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(9): 5721-5736. [2] ZOU X, YUE W L, VU H L. Visualization and analysis of mapping knowledge domain of road safety studies[J]. Accident Analysis & Prevention, 2018, 118(6): 131-145. [3] 李杰, 曾叙砜, 李平, 等. 道路交通安全文献的知识可视化综述[J]. 交通信息与安全, 2020, 38(1): 13-19+26. doi: 10.3963/j.jssn.1674-4861.2020.01.002LI J, ZENG X F, LI P, et al. Visualization review of road traffic safety literature[J]. Journal of Transport Information and Safety, 2021, 38(1): 13-19+26. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2020.01.002 [4] 段腾龙, 何永旺, 李桢, 等. 基于PC-Crash软件的人-车碰撞道路交通事故重建[J]. 法医学杂志, 2019, 35(4): 440-443. https://www.cnki.com.cn/Article/CJFDTOTAL-FYXZ201904013.htmDUAN T L, HE Y W, LI Z, et al. Reconstruction of vehicle-pedestrian collision road traffic accidents based on PC-Crash software[J]. Journal of Forensic Medicine, 2019, 39 (4): 440-443. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-FYXZ201904013.htm [5] 张良, 周继红, 邱俊, 等. 2000—2006年重庆地区老年人道路交通伤害特点[J]. 重庆医学, 2011, 40(15): 1457-1459. doi: 10.3969/j.issn.1671-8348.2011.15.001ZHANG L, ZHOU J H, QIU J, et al. Characteristics of traffic injuries among elderly people in Chongqing between 2000 and 2006[J]. Chongqing Medicine, 2011, 40(15): 1457-1459. (in Chinese) doi: 10.3969/j.issn.1671-8348.2011.15.001 [6] 刘小明, 李颖宏, 陈昱靦, 等. 基于改进BML模型的交通事故下路网交通运行状态分析[J]. 交通运输系统工程与信息, 2010, 10(2): 122-129. doi: 10.3969/j.issn.1009-6744.2010.02.020LIU X M, LI Y H, CHEN Y S, et al. Road net traffic status analysis under traffic accident based on improved BML model[J]. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(2): 122-129. (in Chinese) doi: 10.3969/j.issn.1009-6744.2010.02.020 [7] 高建刚, 陈宏云, 许诺, 等. 国外乡村公路交通安全保障措施介绍[J]. 公路交通科技, 2010, 27(8): 120-126+142. doi: 10.3969/j.issn.1002-0268.2010.08.023GAO J G, CHEN H Y, XU N, et al. Road safety guarantee measures of foreign rural roads[J]. Journal of Highway and Transportation Research and Development, 2010, 27(8): 120-126+142. (in Chinese) doi: 10.3969/j.issn.1002-0268.2010.08.023 [8] 赵玲, 许宏科, 程鸿亮. 基于最优加权组合模型的道路交通事故预测[J]. 计算机工程与应用, 2013, 49(24): 11-15. doi: 10.3778/j.issn.1002-8331.1305-0324ZHAO L, XU H K, CHENG H L. Road traffic accidents prediction based on optimal weighted combined model[J]. Computer Engineering and Applications, 2013, 49(24): 11-15. (in Chinese) doi: 10.3778/j.issn.1002-8331.1305-0324 [9] YAMAMOTO Y, HIRANO J, YOSHITAKE H, et al. Machine-learning approach to predict on-road driving ability in healthy older people[J]. Psychiatry and Clinical Neurosciences, 2020, 74(9): 488-495. doi: 10.1111/pcn.13084 [10] HOTTA R, MAKIZAKO H, DOIT, et al. Cognitive function and unsafe driving acts during an on-road test among community-dwelling older adults with cognitive impairments[J]. Geriatrics & Gerontology International, 2018, 18(6): 847-852. [11] HOURS M, KHATI I, CHARNAY P, et al. One year after mild injury: Comparison of health status and quality of life between patients with whiplash versus other injuries[J]. The Journal of Rheumatology, 2014, 41(3): 528-538. doi: 10.3899/jrheum.130406 [12] XING Y, CHEN S, ZHU S, et al. Exploring risk factors contributing to the severity of hazardous material transportation accidents in China[J]. International Journal of Environmental Research and Public Health, 2020, 17(4): 1344-1363. doi: 10.3390/ijerph17041344 [13] ZHOU X, ZHAO G. Global liposome research in the period of 1995—2014: A bibliometric analysis[J]. Scientometrics, 2015, 105(1): 231-248. doi: 10.1007/s11192-015-1659-6 [14] ZHANG X D, WANG C X, JIANG H H, et al. Trends in research related to high myopia from 2010 to 2019: A bibliometric and knowledge mapping analysis[J]. International Journal of Ophthalmology, 2021, 14(4): 589-599. doi: 10.18240/ijo.2021.04.17 [15] BORSOS A, CAFISO S, D'AGOSTINO C, et al. Comparison of Italian and Hungarian black spot ranking[J]. Transportation Research Procedia, 2016, 14(5): 2148-2157. [16] 耿超, 彭余华. 基于动态分段和DBSCAN算法的交通事故黑点路段鉴别方法[J]. 长安大学学报(自然科学版), 2018, 38(5): 131-138. doi: 10.3969/j.issn.1671-8879.2018.05.016GENG C, PENG Y H. Identification method of traffic accident black spots based on dynamic segmentation and DBSCAN algorithm[J]. Journal of Chang'an University(Natural Science Edition), 2018, 38(5): 131-138. (in Chinese) doi: 10.3969/j.issn.1671-8879.2018.05.016 [17] 刘志强, 王玲, 张爱红, 等. 基于贝叶斯模型的雾霾天高速公路交通事故发生机理研究[J]. 重庆理工大学学报(自然科学), 2018, 32(1): 43-49. https://www.cnki.com.cn/Article/CJFDTOTAL-CGGL201801007.htmLIU Z Q, WANG L, ZHANG A H, et al. Study on traffic accidents occurrence mechanism in haze weather on the highway[J]. Journal of Chongqing University of Technology (Natural Science), 2018, 32(1): 43-49. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CGGL201801007.htm [18] 赵丹, 马社强, 张雨萌, 等. 农村公路交叉口交通事故特征关联性与风险因素分析[J]. 中国安全科学学报, 2020, 30 (7): 146-151. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202007023.htmZHAO D, MA S Q, ZHANG Y M, et al. Correlation and risk factors analysis of traffic crash at intersections on rural highways[J]. China Safety Science Journal, 2020, 30(7): 146-151. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202007023.htm [19] TANG J, LIANG J, HAN C, et al. Crash injury severity analysis using a two-layer Stacking framework[J]. Accident Analysis & Prevention, 2019, 122(10): 226-238. [20] WANG C, XU C, XIA J, et al. A combined use of microscopic traffic simulation and extreme value methods for traffic safety evaluation[J]. Transportation Research Part C: Emerging Technologies, 2018, 90(3): 281-291. [21] 梁心雨, 郭彤, 孟祥海. 基于三角模糊数权重算法的宏观交通安全评价方法[J]. 交通信息与安全, 2017, 35(4): 20-28+35. doi: 10.3963/j.issn.1674-4861.2017.04.003LIANG X Y, GUO T, MENG X H. A method on macroscopic traffic safety evaluation based on weighting triangular fuzzy number algorithm[J]. Journal of Transport Information and Safety, 2017, 35(4): 20-28+35. (in Chinese) doi: 10.3963/j.issn.1674-4861.2017.04.003 [22] TIAN Z, ZHANG S. Application of multi-attribute group decision-making methods in urban road traffic safety evaluation with interval-valued intuitionistic fuzzy information[J]. Journal of Intelligent & Fuzzy Systems, 2021, 40(3): 5337-5346. [23] USMAN M, CARIE A, MARAPELLI B, et al. A human-in-the-loop probabilistic CNN-Fuzzy Logic framework for accident prediction in vehicular networks[J]. IEEE Sensors Journal, 2021, 21(14): 15496-15503. doi: 10.1109/JSEN.2020.3023661 [24] TANG J, ZHENG L, HAN C, et al. Statistical and machine-learning methods for clearance time prediction of road incidents: a methodology review[J]. Analytic Methods in Accident Research, 2020, 27(2): 100123-100138. [25] 戢小辉. 基于灰色关联的LS-SVM道路交通事故预测[J]. 计算机应用研究, 2016, 33(3): 806-809. doi: 10.3969/j.issn.1001-3695.2016.03.037JI X H. Forecast model of road traffic accidents based on LS-SVM with grey correlation analysis[J]. Application Research of Computers, 2016, 33(3): 806-809. (in Chinese) doi: 10.3969/j.issn.1001-3695.2016.03.037 [26] 高珍, 高屹, 余荣杰, 等. 连续数据环境下的道路交通事故风险预测模型[J]. 中国公路学报, 2018, 31(4): 280-287. doi: 10.3969/j.issn.1001-7372.2018.04.032GAO Z, GAO Y, YU R J, et al. Road crash risk prediction model for continuous streaming data environment[J]. China Journal of Highway and Transport, 2018, 31(4): 280-287. (in Chinese) doi: 10.3969/j.issn.1001-7372.2018.04.032 [27] 黄合来, 罗启章, 彭韵颖, 等. 山区高速公路隧道群路段危化品运输风险评价体系研究[J]. 中南大学学报(自然科学版), 2018, 49(8): 2107-2114. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201808034.htmHUANG H L, LUO Q Z, PENG Y Y, et al. Risk evaluation for hazardous chemicals transportation at mountainous freeway with tunnels groups[J]. Journal of Central South University(Science and Technology), 2018, 49(8): 2107-2114. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201808034.htm [28] 梁明明, 张允, 汪媛, 等. 气象因素与交通事故伤害关联性的系统评价[J]. 中华疾病控制杂志, 2020, 24(2): 222-227. https://www.cnki.com.cn/Article/CJFDTOTAL-JBKZ202002020.htmLIANG M M, ZHANG Y, WANG Y, et al. A systematic review on the association between meteorological factors with traffic accident injury[J]. Chinese Journal of Disease Control & Prevention, 2020, 24(2): 222-227. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JBKZ202002020.htm [29] 杨嘉璐, 钟艺琪, 梅海卿, 等. 共享单车道路交通伤害的流行特征及危险因素研究[J]. 中华疾病控制杂志, 2018, 22 (10): 1012-1015. https://www.cnki.com.cn/Article/CJFDTOTAL-JBKZ201810011.htmYANG J L, ZHONG Y Q, MEI H Q, et al. Study on epidemic characteristics and risk factors of road traffic injury on shared bicycles[J]. Chinese Journal of Disease Control & Prevention, 2018, 22(10): 1012-1015. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JBKZ201810011.htm [30] WU Y, LIU C, LAN S, et al. Real-time 3D road scene based on virtual-real fusion method[J]. IEEE Sensors Journal, 2015, 15(2): 750-756. doi: 10.1109/JSEN.2014.2354331 [31] 程啸. 民法典侵权责任编中机动车交通事故责任的完善[J]. 法学杂志, 2019, 40(1): 64-74.CHENG X. Advices on improving regulations of auto liability in the tort law of China civil code[J]. Law Science Magazine, 2019, 40(1): 64-74. (in Chinese) [32] 马宁. 中国交强险立法的完善: 保险模式选择与规范调适[J]. 收藏, 2019, 13(5): 149-167. https://www.cnki.com.cn/Article/CJFDTOTAL-QHFX201905009.htmMA N. The improvement of China's compulsory traffic accident liability insurance: Selection of insurance model and regulatory adjustment[J]. Tsinghua University Law Journal, 2019, 13(5): 149-167. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QHFX201905009.htm [33] 庞明宝, 蔡章辉. 一个山区高速公路下纵坡弯道可能事故的CAM[J]. 系统仿真学报, 2018, 30(4): 1414-1422. https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201804026.htmPANG M B, CAI Z H. CAM of possible accident for longitudinal slope curve of mountain freeway[J]. Journal of System Simulation, 2018, 30(4): 1414-1422. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201804026.htm [34] 李静, 王占永, 蔡铭. 信号交叉口左转待行区综合效益评估[J]. 中山大学学报(自然科学版), 2019, 58(3): 110-117. https://www.cnki.com.cn/Article/CJFDTOTAL-ZSDZ201903014.htmLI J, WANG Z Y, CAI M. Comprehensive benefit evaluation of the left-turn waiting zone on signalized intersection[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2019, 58 (3): 110-117. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZSDZ201903014.htm [35] 龚鹏飞, 常正辉, 徐雨. 城市道路应急交通组织措施及仿真评价[J]. 中国安全生产科学技术, 2020, 16(10): 139-145. https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK202010028.htmGONG P F, CHANG Z H, XU Y. Emergency traffic organization measures and simulated evaluation of urban roads[J]. Journal of Safety Science and Technology, 2020, 16(10): 139-145. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK202010028.htm [36] 范双双, 张梦洁, 漆书林. 驾驶员异常驾驶行为与人格类型调查研究[J]. 交通信息与安全, 2018, 36(3): 99-104. doi: 10.3963/j.issn.1674-4861.2018.03.014FAN S S, ZHANG M J, QI S L. The relationship between abnormal driving behaviors and personality types of drivers[J]. Journal of Transport Information and Safety, 2018, 36(3): 99-104. (in Chinese) doi: 10.3963/j.issn.1674-4861.2018.03.014 [37] ZAHID M, CHEN Y Z, KHAN S, et al. Predicting risky and aggressive driving behavior among taxi drivers: do spatio-temporal attributes matter?[J]. International Journal of Environmental Research and Public Health, 2020, 17(11): 3937-3958. doi: 10.3390/ijerph17113937 [38] 蔡晓禹, 雷财林, 彭博, 等. 基于驾驶行为和信息熵的道路交通安全风险预估[J]. 中国公路学报, 2020, 33(6): 190-201. doi: 10.3969/j.issn.1001-7372.2020.06.018CAI X Y, LEI C L, PENG B, et al. Road traffic safety risk estimation based on driving behavior and information entropy[J]. China Journal of Highway and Transport, 2020, 33 (6): 190-201. (in Chinese) doi: 10.3969/j.issn.1001-7372.2020.06.018 [39] HU J, ZHANG X, MAYBANK S. Abnormal driving detection with normalized driving behavior data: A deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2020, 69(7): 6943-6951. doi: 10.1109/TVT.2020.2993247 [40] 林垚, 张丽, 张晗. 基于Cite Space的我国路面工程领域发展状况分析[J]. 交通运输研究, 2021, 7(1): 104-114. https://www.cnki.com.cn/Article/CJFDTOTAL-JTBH202101013.htmLIN Y, ZHANG L, ZHANG H. Domestic development status of pavement engineering based on CiteSpace[J]. Transport Research, 2021, 7(1): 104-114. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTBH202101013.htm [41] 杨昆鹏. 无人驾驶汽车碰撞程序的刑法正当性——基于行为功利主义理论[J]. 新疆大学学报(哲学·人文社会科学版), 2021, 49(5): 19-27. https://www.cnki.com.cn/Article/CJFDTOTAL-XJDB202105003.htmYANG K P. Legitimacy of the criminal law on the procedure of driverless car crashes-based on the theory of act utilitarianism[J]. Journal of Xinjiang University(Philosophy, Humanities & Social Sciences), 2021, 49(5): 19-27. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XJDB202105003.htm [42] KUANG L, YAN H, ZHU Y, et al. Predicting duration of traffic accidents based on cost-sensitive Bayesian network and weighted K-nearest neighbor[J]. Journal of Intelligent Transportation Systems, 2019, 23(2): 161-174. doi: 10.1080/15472450.2018.1536978 [43] JIA W, PENG H, RUAN N, et al. WiFind: driver fatigue detection with fine-grained Wi-Fi signal features[J]. IEEE Transactions on Big Data, 2020, 6(2): 269-282. doi: 10.1109/TBDATA.2018.2848969 [44] CHANG H, PARK D. Potentialities of vehicle trajectory big data for monitoring potentially fatigued drivers and explaining vehicle crashes on motorway sections[J]. Sustainability, 2020, 12(15): 5877-5893. doi: 10.3390/su12155877 -
点击查看大图
图(10) / 表(5)
计量
- 文章访问数: 2592
- HTML全文浏览量: 908
- PDF下载量: 330
- 被引次数: 0