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关于我国高校英文科技期刊国际影响力提升的思考 被引量:1
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作者 沈怡欣 《今传媒》 2022年第9期72-74,共3页
本文从办刊体制、编辑人员专业水平、期刊评价体制、学术界唯SCI论四个方面对高校英文科技期刊的发展弊端进行了调研,总结出Journal of Traffic and Transportation Engineering (English Edition)(JTTE,交通运输工程学报(英文版))创刊... 本文从办刊体制、编辑人员专业水平、期刊评价体制、学术界唯SCI论四个方面对高校英文科技期刊的发展弊端进行了调研,总结出Journal of Traffic and Transportation Engineering (English Edition)(JTTE,交通运输工程学报(英文版))创刊以来所采取的提升期刊国际影响力的措施和方法:在“中国科技期刊卓越行动计划”的支持下,JTTE充分发挥自身优势,依靠长安大学在道路工程、交通运输领域强大的学术影响力,团结作者、学者、编委三方力量,将期刊介绍给全世界,从而提高了期刊的国际显示度;在新媒体背景下,期刊利用跨平台推荐、社交媒体推广等方式宣传期刊内容,加快了期刊内容传播。 展开更多
关键词 Journal of traffic and transportation Engineering(English Edition)(JTTE 交通运输工程学报(英文版)) 英文科技期刊 国际影响力 “中国科技期刊卓越行动计划”
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Special issue: Management of road and railway traffic and transportation engineering
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作者 Yongfu SUN Ziyou GAO 《Frontiers of Engineering Management》 2017年第4期385-387,共3页
In recent years,with the development of road and railway transportation industries,a variety of complicated decisionmaking problems have emerged in real-world applications.It is urgent to analyze these problems from t... In recent years,with the development of road and railway transportation industries,a variety of complicated decisionmaking problems have emerged in real-world applications.It is urgent to analyze these problems from the perspective of theoretical and methodological innovations,and provide methods in management,decision-making and application so as to achieve efficient operations of traffic and transportation systems.These problems have 展开更多
关键词 Management of road and railway traffic and transportation engineering Special issue PPP
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Estimating traffic volume on Wyoming low volume roads using linear and logistic regression methods 被引量:1
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作者 Dick Apronti Khaled Ksaibati +1 位作者 Kenneth Oerow Jaime Jo Hepner 《Journal of Traffic and Transportation Engineering(English Edition)》 2016年第6期493-506,共14页
Traffic volume is an important parameter in most transportation planning applications. Low volume roads make up about 69% of road miles in the United States. Estimating traffic on the low volume roads is a cost-effect... Traffic volume is an important parameter in most transportation planning applications. Low volume roads make up about 69% of road miles in the United States. Estimating traffic on the low volume roads is a cost-effective alternative to taking traffic counts. This is because traditional traffic counts are expensive and impractical for low priority roads. The purpose of this paper is to present the development of two alternative means of cost- effectively estimating traffic volumes for low volume roads in Wyoming and to make recommendations for their implementation. The study methodology involves reviewing existing studies, identifying data sources, and carrying out the model development. The utility of the models developed were then verified by comparing actual traffic volumes to those predicted by the model. The study resulted in two regression models that are inexpensive and easy to implement. The first regression model was a linear regression model that utilized pavement type, access to highways, predominant land use types, and population to estimate traffic volume. In verifying the model, an R^2 value of 0.64 and a root mean square error of 73.4% were obtained. The second model was a logistic regression model that identified the level of traffic on roads using five thresholds or levels. The logistic regression model was verified by estimating traffic volume thresholds and determining the percentage of roads that were accurately classified as belonging to the given thresholds. For the five thresholds, the percentage of roads classified correctly ranged from 79% to 88%. In conclusion, the verification of the models indicated both model types to be useful for accurate and cost-effective estimation of traffic volumes for low volume Wyoming roads. The models developed were recommended for use in traffic volume estimations for low volume roads in pavement management and environmental impact assessment studies. 展开更多
关键词 traffic volume estimation Low volume road Wyoming county roads transportation planning Regression analysis
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