Cost analysis of road traffic crashes in China

Abstract

Fatalities and injuries resulting from road traffic crashes is always a serious problem. The overall economic losses caused by road traffic crashes are beyond imagination. Including the economic cost of property damage, productivity loss, medical cost, travel delay time cost, legal cost and insurance cost, the total economic cost of traffic crashes in China in 2017 is calculated as 490.1 billion yuan (72.6 billion USD 2017), which is equivalent to 0.60% of the GDP. The cost of productivity loss accounts for the highest proportion of total economic cost, which is 72%. The second is the travel delay cost, accounting for 12% insurance cost, property damage cost and medical cost are followed. The more serious the injury, the higher the unit economic cost. The unit cost of a crash that caused only property damage is 11,274 yuan. The unit cost of a minor injured crash is 20,223 yuan. The highest unit economic cost is the unit cost of a fatal crash, which is 3,181,394 yuan. This study provides important insights into the cost-benefit analysis of China's road safety policies.     

数据挖掘在高校图书馆中的应用概述

本文从目前我国高校图书馆各类数据资源的现状,分析了数据挖掘在高校图书馆的应用.     

数据挖掘及其在企业管理中的应用

企业在管理过程中产生了大量的数据,这些数据的背后隐藏着与企业密切相关的极其重要的知识。聚类、关联规则、序列模式、统计分析、特征规则等数据挖掘方法能从这些海量数据中发现有用的知识,使数据真正成为企业的财富,为企业的决策和发展服务。目前数据挖掘已被广泛应用于银行、电信等行业,用来对客户数据进行正确的分析,挖掘消费模式,预测客户未来的行为,针对客户的需求提供个性化的服务。     

Partitioning for Profit: An Empirical Study of Methods for Handling Unequal Costs of Error in Predictive Data Mining

This paper expands prior work on the Sequential Binary Programming (SBP) algorithm as a framework for cost-sensitive classification. The field of cost-sensitive learning has provided a number of methods to adapt predictive data mining from engineering and hard science applications to those in commerce. This discussion will test theoretical limitations of classical cost-sensitive algorithms empirically and outline the appropriate conditions under which various methods (specifically SBP) should be implemented in favor of others.     

A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo,Egypt

Abstract

The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequently lead to biased conclusions and poorly designed countermeasures. This is particularly true for imbalanced data where the number of samples in one class far outnumber the other. To improve the classification performance of the injury severity, the paper presents a robust noise filtering technique to deal with the mislabels in the imbalanced crash dataset using the advanced machine learning algorithms. We examine the state-of-the-art filtering algorithms, including Iterative Noise Filtering based on the Fusion of Classifiers (INFFC), Iterative Partitioning Filter (IPF), and Saturation Filter (SatF). In the case study of Cairo (Egypt), the empirical results show that: (1) the mislabels in crash data significantly influence the injury severity predictions, and (2) the proposed M-IPF filter outperforms its counterparts in terms of the effectiveness and efficiency in eliminating the mislabels in crash data. The test results demonstrate the efficacy of the M-IPF in handling the data noise and mitigating the impacts thereof.     

Customer relationship management in retailing: A content analysis of retail trade journals

The purpose of this research was to increase knowledge and understanding of how retailers use business intelligence and data mining tools to implement customer relationship management (CRM) in retailing. Specific objectives were to (1) identify organization and infrastructure requirements for CRM effectiveness, (2) identify CRM objectives and goals of retail companies, (3) identify data mining tools utilized by retailers to perform CRM functions, and (4) identify CRM strategies used by retail companies. A keyword search within business databases using CRM and CRM identified publications with CRM content. Content analysis was used on articles (N=149) drawn from Stores, Chain Store Age, Harvard Business Review, and Retail Forward over a 5 year period (2000–2005). Selected articles were stored as text files in QDA Miner, a computerized qualitative analysis tool. Key organization/infrastructure needs emerged focusing on data structure, organizational systems, technology structure, and data accessibility. Retailers goals/objectives and strategies focused on marketing, customer service, understanding customers through data analysis and increasing acquisition and retention through customer loyalty programs. Data mining tools identified supported marketing and customer analysis efforts. Findings provide insight into the challenges retailers face as they implement a more customer-centric business strategy.     

数据挖掘技术在物流企业管理的应用研究

本文针对物流领域的实际业务流程、业务需要以及实际拥有的数据进行了研究,论述了物流企业管理中数据挖掘的目标、算法和操作。阐述了数据挖掘技术及其在物流领域管理中的应用。     

Analyzing Group Data in MIS Research: Including the Effect of the Group

A review of recent group experimental research in three major MIS research journals indicates a lack of understanding of how to analyze group data measured at the individual level. This paper draws on statistical analysis literature from various disciplines to explain why current analysis of group data in MIS research is often incorrect and demonstrates how to analyze such data correctly.     

Application of the Information Bottleneck method to discover user profiles in a Web store

ABSTRACT

The paper deals with the problem of discovering groups of Web users with similar behavioral patterns on an e-commerce site. We introduce a novel approach to the unsupervised classification of user sessions, based on session attributes related to the user click-stream behavior, to gain insight into characteristics of various user profiles. The approach uses the agglomerative Information Bottleneck (IB) algorithm. Based on log data for a real online store, efficiency of the approach in terms of its ability to differentiate between buying and non-buying sessions was validated, indicating some possible practical applications of the our method. Experiments performed for a number of session samples showed that the method is capable of separating both types of sessions to a large extent. A detailed analysis was performed for the number of clusters ranging from two to seven, and the results were compared to those achieved by applying the most common clustering algorithm, k-means. Increasing the number of clusters generally leads to better results for both algorithms. However, IB demonstrated much higher average efficiency than k-means for the corresponding number of clusters, and this superiority was especially clear for lower number of clusters. The IB-based division of user sessions into seven clusters gives the mean entropy value of 0.28, which means the 95% separation of sessions of both types. Furthermore, a big advantage of our approach is that it gives a possibility to analyze the probability distribution of session attributes in individual clusters, which allows one to discover hidden knowledge about common characteristics of various user profiles and use this knowledge to support managerial decisions.     

Corporate environmental sustainability reporting in the context of national cultures: A quantile regression approach

This work, based on previous evidence within international business management research, aims to test the influence of communities’ culture features on corporate environmental sustainability reporting (CESR) practices. To overcome some limitations of conventional statistical approaches applied by previous research, a quantile regression (QR) model is implemented which allows setting a framework to test the working hypotheses in different scenarios that cover divergent firms’ commitment levels to stakeholder engagement and CESR practices development. Our central results addresses that different national culture dimensions present a non-monotonic influence on CESR practices. This result, which is analyzed through the Stakeholder Theory proposals, can be explained because corporate sustainability behaviors are highly sensitive to stakeholders’ pressures and demands which are ultimately conditioned by the cultural environment. Some interesting recommendations for companies’ strategic management and governmental policy-making processes are reported.     

All That Glitters is Not Gold: Digging Beneath the Surface of Data Mining

This article develops a more comprehensive understanding of data mining by examining the application of this technology in the marketplace. In addition to exploring the technological issues that arise from the use of these applications, we address some of the social concerns that are too often ignored.As more firms shift more of their business activities to the Web, increasingly more information about consumers and potential customers is being captured in Web server logs. Sophisticated analytic and data mining software tools enable firms to use the data contained in these logs to develop and implement a complex relationship management strategy. Although this new trend in marketing strategy is based on the old idea of relating to customers as individuals, customer relationship management actually rests on segmenting consumers into groups based on profiles developed through a firm's data mining activities. Individuals whose profiles suggest that they are likely to provide a high lifetime value to the firm are served content that will vary from that which is served to consumers with less attractive profiles.Social costs may be imposed on society when objectively rational business decisions involving data mining and consumer profiles are made. The ensuing discussion examines the ways in which data mining and the use of consumer profiles may exclude classes of consumers from full participation in the marketplace, and may limit their access to information essential to their full participation as citizens in the public sphere. We suggest more ethically sensitive alternatives to the unfettered use of data mining.     

e-banking culture: A comparison of EU 27 countries and Portuguese case in the EU 27 retail banking context

The aim of this research is two-fold. Firstly we conducted a wise comparative analysis of EU 27 countries and secondly we identified distinct e-banking user segments of Portuguese citizens. We used a questionnaire for data collection (n=2358) and Latent Class Models (LCM) for data analysis. LCM revealed three segments in EU 27 (Portugal is in the poorest cluster, concerning the indicators used, with Greece, Spain and Cyprus) and two e-banking user segments in Portuguese citizens: Those who do not risk (49%), and Those who risk (51%). Our findings enable e-banking managers for taking appropriate strategic decisions.     

长江三角洲地区人口入迁及城市化的区域经济因素分析：基于面板数据的研究

以“人口迁移和人口就地非农化是中国城市化两种并行不悖的实现形式”为立论,运用面板数据模型方法,从人口入迁和人口非农化两个角度对影响中国长江三角洲地区城市化的区域经济因素做了分析。第二和第三产业产值比重对推动长三角地区城市化进程的作用显著;工资水平是影响长三角地区人口入迁的最重要因素;人均GDP虽然对长三角地区人口非农化的作用显著,但对人口入迁的作用是不确定的;外商直接投资和对外贸易依存度等开放经济因素对长三角地区城市化的作用非常有限。     

大数据背景下小微物流企业公共信息平台的设计构想

大数据时代给小微物流企业既带来了机遇也带来了挑战。网络化经营是小微物流企业基本的运作模式,因此组建物流服务供应链是今后小微物流企业的发展趋势。从物流服务供应链的组建角度,提出了小微物流企业面临的多源信息融合和大数据驱动的决策优化问题。为政府提供构建公共信息平台整合企业供需信息的对策。在政府公共信息平台基础上给出了物流服务供应链的运作模型。     

Click Here for a Data Scientist: Big Data,Predictive Analytics,and Theory Development in the Era of a Maker Movement Supply Chain

Predictive analytics is impacting many diverse areas, ranging from baseball and epidemiology to forecasting and customer relationship management. Manufacturers, retailers, software companies, and consultants are creatively discovering new applications of big data using predictive analytics in supply chain management and logistics. In practice, predictive analytics is generally atheoretical; however, we develop a 2 × 2 model to explain the role of predictive analytics in the theory development process. This 2 × 2 model shows that in our discipline we have traditionally taken one path to theory development, but that predictive analytics can be a salient component of a comprehensive theory development process. The model points to a number of research questions that need to be addressed by our research community. These questions are not just highly relevant to the academic community but also in urgent need of answers to help practitioners execute the right strategies with greater precision and efficiency. We also discuss how one disruptive trend, the maker movement, changes the nature of who the producers are in the supply chain, making big data even more valuable. As we engage in higher levels of dialogue we will be able to make meaningful progress addressing these vital research topics.     

Effect of the Number of Banking Relationships on Credit Availability: Evidence from Panel Data of Spanish Small Firms

This paper investigates how the choice between single or multiple banking relationships affects credit availability for a complete panel of small and medium-sized Spanish firms. The results seem to indicate the existence of rationing, since a substitution relation has been found between trade and bank credit. We also analyse the relationship between the level of indebtedness and the interest rate for each group of firms. The results show that those SMEs that work with fewer financial intermediaries obtain fewer funds for the same increase in the interest rate, which indicates that these companies have more financial restraints.     

金融支持与区域经济增长:基于西部地区省际面板数据的经验分析

基于2000-2009年我国西部地区省际面板数据,使用TSLS工具变量法,运用面板数据单位根检验,协整检验和误差修正模型,对西部大开发战略实施十年以来西部地区金融发展与经济增长的长期关系和短期关系进行了实证研究。实证结果表明,西部各省份经济增长的金融贡献度存在差距;西部地区金融发展、控制变量与区域经济增长具有长期稳定的均衡关系,而金融支持的短期效应并不明显。为此,在推进新一轮西部大开发的过程中,须进一步深化金融体制改革,加快转变经济增长方式,提高区域经济增长质量。     

Trade Credit in Small and Medium Size Firms: An Application of the System Estimator With Panel Data

Financing through suppliers is a subject that has been little studied in the economic literature in general and in corporate finance in particular. Although several hypotheses have been put forward to explain the different reasons behind this phenomenon, trade credit is not based on a general theory. This study provides empirical evidence about factors determining the use of trade credit for a sample of small and medium size firms, which are potentially the firms that would follow this financing route, since they are more rationed in credit markets. Using a panel of Canary-Island firms from 1990 to 1996, and by means of specifications with the system estimator, results reveal that trade credit leads to a reduction in asymmetric information between firms and their financial backers, as well as in transaction costs. Furthermore, we confirm the theory that companies with easier access to institutional finance act as a credit channel for those with greater difficulties to obtain external funds.     

我国环渤海地区外国直接投资与对外贸易效应的实证研究

本文运用面板数据方法对环渤海地区的对外贸易效应进行了分析,结果表明外国直接投资对整个环渤海地区的对外贸易起到了正向推动作用,环渤海地区的外国直接投资与对外贸易之间存在互补关系。同时分别对该地区7个省市区外国直接投资与对外贸易的关系进行了研究。     

长江三角洲地区外商直接投资的对外贸易效应

本文由长三角地区(16城市)FDI与对外贸易的简单数量关系,构筑了地区层面的对外贸易模型,运用面板数据(PanelData)方法对长三角地区FDI的对外贸易效应及FDI在上海双边贸易中的作用进行了分析。结果表明,FDI对长三角地区的对外贸易及上海的双边贸易均起到了显著的正向推动作用,长三角地区FDI与对外贸易之间存在互补关系。