船舶减摇水舱试验台架横摇运动模拟

本文介绍了由微机控制的船舶减摇水舱试验台架，利用这套试验装置可以实时模拟在规则波和不规则波情况下装备有水舱的船舶的摇摆，这里着重介绍了对船舶影响最大的横摇的模拟，从而为研究减摇水舱对船舶的作用，评估其减摇效果，为研究和设计减摇水舱提供了可靠的依据。     

语境理论在大学英语阅读教学中的应用

语言的使用离不开语境,语境制约着语言单位的选择、意义的表达和理解。本文从语境的定义和分类入手,探讨了语境在篇章解读中的作用及在大学英语阅读教学中的应用。     

高校学报内文版面设计中的自我宣传

文章通过分析单篇文档与期刊信息页割裂带来的问题,阐述了内文版面自我宣传设计的原则及要素,并进行了实例解析,对高校学报的自我宣传具有一定的借鉴意义。     

沥青混合料空隙率影响因素分析

以抗滑表层AK-16级配混合料,采用不同的击实功成型试件,分别进行马歇尔试验、浸水残留稳定度试验、劈裂抗拉强度试验、冻融劈裂试验及动稳定度等试验,分析沥青混合料空隙率的影响因素。     

Extracting patterns from Twitter to promote biking

Emphasis on non-motorized travel modes (for example, biking) reduces motorized trips and provides positive effects on the environment and the quality of human life. Understanding factors that influence people to biking or bike commuting can help decision makers, transportation planners, and bike commuting networks. Historically, conventional methods like surveys and crash data analyses were conducted to understand relevant factors. Survey and crash data analysis are difficult to perform in broad scale due to data availability and efforts. An innovative approach to determining these factors is to conduct social media mining to understand sentiments or motivations of bike commuters. People use terms (with hashtag at the beginning of the term) in Twitter, a popular social media network, to express their thoughts, activities or information. This study developed a framework for using Twitter data in understating the sentiments of the bikers with minimal effort. In this study, Twitter data associated with bike commuting hashtags were obtained for eight years (2009–2016). This study provided a framework of data collection and application of various natural language processing (NLP) tools (for example, text mining, sentiment analysis) to extract knowledge from the unstructured text data. Findings show that biking is associated with weather and seasonal patterns. The general sentiment towards biking is positive. However, negative sentiments are associated with bad weather, crime, and other challenges. The polarity scores indicate somewhat positiveness in the recent few years. The developed framework and the findings of this study will help planners and decision makers to promote biking on a broader scale.     

Risk assessment of inland waterborne transportation using data mining

ABSTRACT

China has constructed a relatively complete inland waterborne transportation system. However, the frequent occurrence of inland water accidents with serious consequences, like the catastrophic Orient Star shipwreck, is an urgent unsolved problem. To reduce such accidents in the future and improve inland waterborne transportation safety, this study uses data mining, mainly containing text mining and association rule mining to risk assess China’s inland waterborne transportation, rather than the traditional quantitative risk assessment model. Text mining enables the risk factors to be objectively identified and distilled from accident reports. The potential relationships between risk variables are explored using association rule mining, based on the FP-Growth algorithm. The results reveal the essential problem facing China’s inland waterborne transportation system: frequent and varied ship accidents; key risk factors include overloading or improper loading, poor navigation visibility, inadequate sailor competence, and insufficient government supervision of shipowners and shipping companies. Combining the actual circumstances of inland waterborne transportation operations, this study proposes relevant recommendations for governments and relevant supervisory departments. The integrated application of text mining and association rule mining serves to avoid uncertainty and subjectivity, and achieve good results proving their scientific nature as a feasible method in water transportation risk research.     

How can the maritime industry meet Sustainable Development Goals? An analysis of sustainability reports from the social entrepreneurship perspective

Sustainability Development Goals (SDGs) are a comprehensive agenda agreed upon globally that aims to stimulate actions towards economic, environmental and social sustainability. Being one of the key stakeholders, the international maritime industry plays an important role in contributing to global sustainability. By applying the concept of social entrepreneurship (SE), this study aims to examine (1) the basic and extended responsibilities (SDG 1–SDG 16) and (2) the potential collaborations within the value chain (SDG 17) concerning SDG implementation in maritime industry. To achieve these, we conduct a content analysis of sustainability reports published by container shipping liners and terminal operators from 2016 to 2019. More specifically, manual text classification is adopted to categorise the text content of sustainability reports based on 17 SDGs, and automatic text mining is employed to further identify the key roles of maritime industry related to each SDG. A unified framework is proposed, which points to varied motives and levels of comprehensiveness of the sustainability efforts by the maritime industry. This framework reveals the theoretic process of maritime industry's transitional involvement in sustainability from the SE perspective. It also creates managerial implications regarding the resource allocation strategies by maritime industry in meeting SDGs.     

Online license plate matching procedures using license-plate recognition machines and new weighted edit distance

License-plate recognition (LPR) technology has been widely applied in many different transportation applications such as enforcement, vehicle monitoring, and access control. Recently, there has been effort to exploit an LPR database for vehicle tracking using popular template matching procedures. Existing template matching procedures assume that the true reference string is always available. However, under a two-point LPR survey, a vehicle could have its plate misread at both locations generating a pair of misread strings (or templates) with no reference for matching. To compensate for LPR misreading problem, we propose a new weight function based on a probability model to match the observed outcomes of a dual LPR setup. Also, considering that reversal errors are never made in LPR machines, new editing constraints as a function of the string lengths are proposed to avoid compensation for reversal errors. These editing constraints are incorporated into the constraint edit distance formulation to improve the performance of the matching procedure. Finally, considering that previous template matching procedures do not take advantage of passage time information available in LPR databases, we present an online tracking procedure that considers the properties of probability distribution of vehicle journey times in order to increase the probability of correct matches. Experimental results show that our proposed procedure can improve the accuracy of LPR systems and achieve up to 97% of positive matches with no false matches. Further research is needed to extend the ideas proposed herein to plate-matching with multiple, i.e., more than two, LPR units.     

通用机场政策文本量化分析

通用机场是综合交通基础设施网络的重要组成,阐释通用机场政策工具体系的类型与特征具有重要意义。本文构建了以政策工具为核心,以政策目标和政策执行为两翼的3*32分析框架。以2016年至2020年的40个政策文本为研究对象,剖析了政策工具的类型与特征,并对政策目标与政策工具、政策执行与政策工具展开了交叉分析。结果显示,环境型政策工具应用频率最高,供给型和需求型政策工具应用较少,政策工具结构存在明显失衡;政策工具主要服务于规范建设与运营和引导发展与创新政策目标,政策目标的均衡性不足;政策工具与政策目标的匹配度较低,政府购买、人才供给、资金支持等诸多行之有效的政策工具在具体目标中未得到充分应用。     

Automating a framework to extract and analyse transport related social media content: The potential and the challenges

Harnessing the potential of new generation transport data and increasing public participation are high on the agenda for transport stakeholders and the broader community. The initial phase in the program of research reported here proposed a framework for mining transport-related information from social media, demonstrated and evaluated it using transport-related tweets associated with three football matches as case studies. The goal of this paper is to extend and complement the previous published studies. It reports an extended analysis of the research results, highlighting and elaborating the challenges that need to be addressed before a large-scale application of the framework can take place. The focus is specifically on the automatic harvesting of relevant, valuable information from Twitter. The results from automatically mining transport related messages in two scenarios are presented i.e. with a small-scale labelled dataset and with a large-scale dataset of 3.7 m tweets. Tweets authored by individuals that mention a need for transport, express an opinion about transport services or report an event, with respect to different transport modes, were mined. The challenges faced in automatically analysing Twitter messages, written in Twitter’s specific language, are illustrated. The results presented show a strong degree of success in the identification of transport related tweets, with similar success in identifying tweets that expressed an opinion about transport services. The identification of tweets that expressed a need for transport services or reported an event was more challenging, a finding mirrored during the human based message annotation process. Overall, the results demonstrate the potential of automatic extraction of valuable information from tweets while pointing to areas where challenges were encountered and additional research is needed. The impact of a successful solution to these challenges (thereby creating efficient harvesting systems) would be to enable travellers to participate more effectively in the improvement of transport services.