Automated data-driven prediction on aircraft Estimated Time of Arrival

4D trajectory prediction is the core element of the future air transportation system. It aims to improve the operational ability and the predictability of air traffic. In this paper, a novel automated data-driven framework to deal with the prediction of Estimated Time of Arrival (ETA) on the runway at the entry point of Terminal Manoeuvring Area (TMA) is introduced. The proposed framework mainly consists of data preprocessing and machine learning models. Firstly, the dataset is divided, analyzed, cleaned, and estimated. Then, the flights are clustered into partitions according to different runway-in-use (QFU). Several candidate machine learning models are trained and selected on the corresponding dataset of each QFU. Feature engineering is conducted to transform raw data into features. After that, the experiments are performed on real ADS-B data in Beijing TMA with nested cross validation. By comparing the prediction performance on the preprocessed and un-preprocessed datasets, the results demonstrate that the proposed data preprocessing is able to improve the data quality. It is also robust to outliers, missing data, and noise. Finally, an ensemble learning strategy named stacking is introduced. Compared to other individual models, the stacked model has a more complex structure and performs best in ETA prediction. This fact reveals that the framework proposed in this study could make accurate and reliable ETA predictions.     

A comprehensive evacuation planning model and genetic solution algorithm

We consider the problem of evacuating an urban area. Several planning aspects need to be considered in such a scenario, which are usually considered separately. We propose a macroscopic multi-criteria optimization model that includes several such questions simultaneously, and develop a genetic algorithm to solve the problem heuristically. Its applicability is extended by also considering how to aggregate instance data, and how to generate solutions for the original instance starting from a reduced solution. In computational experiments using real-world data, we demonstrate the effectiveness of our approach and compare different levels of data aggregation.     

Collecting samples from online services: How to use screeners to improve data quality

Increasingly, marketing and consumer researchers rely on online data collection services. While actively-managed data collection services directly assist with the sampling process, minimally-managed data collection services, such as Amazon’s Mechanical Turk (MTurk), leave researchers solely responsible for recruiting, screening, cleaning, and evaluating responses. The research reported here proposes a 2 × 2 framework based on sampling goal and methodology for screening and evaluating the quality of online samples. By sampling goals, screeners can be categorized as selection, which involves matching the sample with the targeted population; or as accuracy, which involves ensuring that participants are appropriately attentive. By methodology, screeners can be categorized as direct, which screens individual responses; and as statistical, which provides quantitative signals of low quality. Multiple screeners for each of the four categories are compared across three MTurk samples, two actively-managed data collection samples (Qualtrics and Dynata), and a student sample. The results suggest the need for screening in every online sample, particularly for the MTurk samples, with the fewest supplier-provided filters. Recommendations are provided for researchers and journal reviewers that provide greater transparency with respect to sample practices.     

To disclose or to falsify: The effects of cognitive trust and affective trust on customer cooperation in contact tracing

Contact tracing involves collecting people’s information to track the spread of COVID-19 and to warn people who have been in the proximity of infected individuals. This measure is important to public health and safety during the pandemic. However, customers’ concerns about the violation of their privacy might inhibit their cooperation in the contact tracing process, which poses a risk to public safety. This research investigates how to facilitate customers’ cooperative behavior in contact tracing based on cognitive trust and affective trust. The findings show that cognitive trust increases people’s willingness to disclose information and reduces their willingness to falsify it, whereas affective trust increases the willingness for both disclosure and falsification. This research contributes to the literature on customer data privacy by illuminating how cognitive and affective trust distinctly influence cooperative behavior, which has important implications for hospitality businesses.     

Pursuing consumer empowerment in the age of big data: A comprehensive regulatory framework for data brokers

Data brokers play a central role in the big data ecosystem, compiling personal data from numerous sources and subsequently selling them to businesses worldwide. Such practices, however, generally occur clandestinely without consumers' knowledge or consent and can harm consumers in a discriminatory manner. In recent years, US policymakers have been urged to regulate data brokers by promoting individual choice but have failed, whereas the European Union has introduced broad data privacy guarantees for consumers and a data protection regime that limits data brokers' activities if consumers' consent has not been obtained for specific purposes. Because of the power imbalance between consumers and data brokers, the author argues that the European Union's coherent data protection framework serves as an effective model for regulating data brokers.     

制造业能源效率测算与影响因素分析

要素使用结构是效率变动的起始点,以工业为对象的研究不可忽略不同行业间要素配置结构的区别。本文选取不同要素密集度制造业作为研究对象,基于DEA方法测算了1995～2008年中国制造业能源效率,并利用Tobit模型探索不同要素密集型制造业能源效率的影响因素。研究表明,中国制造业能源效率的总体水平较低,并随要素密集度的不同而不同。劳动密集型制造业能源效率最高,技术密集型行业次之,资本密集型行业最低。三大类型制造业能效提高仍有50％以上的空间,“十二五”期间节能降耗的政策导向应由目前的以行政区域为对象分解节能降耗约束指标的治理方式向以行业或企业为约束对象并分解节能指标的治理方式转变。本文通过Tobit模型还发现,进一步促进技术创新、保持适度合理的行业竞争、调整能源消费结构以及扩大企业规模将有利于提高制造业能源效率。     

技术创新能力与FDI区域分布非均衡

从新经济地理学的角度,考察了技术创新能力、地区市场规模和出口开放度等因素对FDI在中国各省区的集聚,以及由此而导致的FDI区域分布非均衡性。运用30个省(直辖市、自治区)1999—2007年的面板数据进行实证。研究发现,在吸引FDI方面,国内地区市场和国外市场具有一定的相互替代性;同时技术创新能力可以显著地吸引FDI进入。     

大课堂观视野下高职思想政治理论课教学改革探析

高职院校思想政治理论课教学应从狭义的课堂向校园、向更为广阔的社会实践空间拓展,以大课堂观的视域研究思想政治理论课教学改革。这种改革的主要措施有：以实践教学作为思想政治理论课教育教学改革的方向;根据建设中国特色社会主义的实践发展扎实开展案例教学,拓宽“大课堂观”视野下教学方法;针对大学生成长成才的实践条件,思想政治理论课教师应加强自身建设,发挥“大课堂观”视野下教学主体的作用。     

Utilizing and teaching data tools in Excel for exploratory analysis

In this article we offer Excel as an introductory tool to high end business intelligence (BI) and decision support system (DSS) applications. Because it is ubiquitous, Excel can be used by all managers and business students for exploratory data analysis. We provide three key points in this utilization of Microsoft^® Excel 2003: (1) manipulating records using Excel as a database, (2) creating PivotTables^® and PivotCharts^® using Excel for analysis, and (3) importing data using Excel as an automation container. The basic skill set defined by the above three items allows users to begin to use Excel to its full potential in finding information in business data, and it offers a key tool for future research in improving the utilization of information across organizations.     

我国二氧化碳排放的影响因素：基于省级面板数据的研究

本文较为精确地估算了1995—2007年我国29个省的人均二氧化碳（CO2）排放量和排放总量,构建了省级CO2排放面板数据库,并分别在静态和动态面板数据模型框架下,考察了我国CO2排放的影响因素。研究结果显示,重工业比重、城市化水平和煤炭消费比重都对我国的CO2排放都具有显著正的影响;经济发展水平和人均CO2排放量之间则存在倒U型关系,环境库兹涅兹曲线假说成立;上一期人均CO2排放量的大小对本期的排放量具有显著正的影响。