Hierarchical clustering analysis framework of mutually exclusive crash causation parameters for regional road safety strategies

Hierarchical clustering analysis framework is developed to identify benchmark and critical regions for effective road safety strategies. The regions are grouped based on agglomeration coefficient of mutually exclusive crash causation parameters. Subsequently, regions from groups with lower than a threshold index value are selected as benchmark for the poorly performing critical counterparts. Euclidean distance-based Ward's, median and centroid clustering techniques are explored through a case study of Indian states and Union Territories. As per data between 2006 and 2015, fatal crash percentages of driving under influence of drug and alcohol, excessive speeding, vehicle malfunction and road conditions related crash causation parameters, severity index and its growth rate are assessed based on respective threshold values of 6.35%, 43.28%, 2.42%, 1.79%, 26.7 and 3.1%. These are the national average of respective indices. It demonstrated the unique application of hierarchical clustering analysis in benchmark and critical region identification.     

An analysis of risk factors associated with road crash severities in Namibia

Abstract

Road crash is a leading cause of death and disabilities in Namibia and other developing countries. Based on recent trends, the World Health Organization indicated that progress to realize Sustainable Development Goal (SDG) target 3.6 – which calls for a 50% reduction in the number of road traffic deaths by 2020 – remains far from sufficient. To contribute to efforts in reducing road fatalities in Namibia, this study examined risk factors associated with the severity of crashes recorded in the country. Mixed logit modelling methodology was adopted to address the problem of unobserved heterogeneity in injury severity analysis. Model estimation results reveal that collision with pedestrians, head-on collisions, ran-off road collisions and crashes involving high occupancy passenger vehicles were more likely to result in fatalities and severe injuries. The findings and recommendations of this study are expected to enhance countermeasure implementation to reduce road crashes in Namibia.     

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.     

Fatality rate of pedestrians and fatal crash involvement rate of drivers in pedestrian crashes: a case study of Iran

The aim of this study was to uncover patterns of pedestrian crashes. In the first stage, 34,178 pedestrian-involved crashes occurred in Iran during a four-year period were grouped into homogeneous clusters using a clustering analysis. Next, some in-cluster and inter-cluster crash patterns were analysed. The clustering analysis yielded six pedestrian crash groups. Car/van/pickup crashes on rural roads as well as heavy vehicle crashes were found to be less frequent but more likely to be fatal compared to other crash clusters. In addition, after controlling for crash frequency in each cluster, it was found that the fatality rate of each pedestrian age group as well as the fatal crash involvement rate of each driver age group varies across the six clusters. Results of present study has some policy implications including, promoting pedestrian safety training sessions for heavy vehicle drivers, imposing limitations over elderly heavy vehicle drivers, reinforcing penalties toward under 19 drivers and motorcyclists. In addition, road safety campaigns in rural areas may be promoted to inform people about the higher fatality rate of pedestrians on rural roads. The crash patterns uncovered in this study might also be useful for prioritizing future pedestrian safety research areas.     

The developing countries in the new gatt round: Safeguards revisited

The article reviews the case for a new safeguard code in GATT. Article XIX is at present widely circumvented, especially vis-a-vis developing countries. A new safeguard code would preferably not allow for selectivity, emphasize domestic adjustment, and in general not provide for compensation and/ or retaliation. Special treatment for developing countries might be incorporated by prescribing compensation in case safeguards were to be used against their exports.     

Open trade and skilled and unskilled labor productivity in developing countries: A panel data analysis

Abstract

This paper examines the effect of trade openness on the productivity of skilled and unskilled labor in a group of 36 developing countries using panel data and fixed effect approach. We have developed and utilized an empirical model that readily lends itself to testing the hypothesis posed. Our results support the hypothesis that trade openness has a positive and significant impact on labor productivity for both skilled and unskilled labor in the sample countries. We also observe that the beneficial effect of trade openness is relatively stronger for the skilled labor than the unskilled labor. We conclude that contrary to the claim made by Mayda and Rodrik (2001 Mayda, A. M. and Rodrik, D. 2001. “Why are some people (and countries) more protectionist than others? A cross country analysis”. Mimeo: Harvard University.  [Google Scholar]), skilled workers in developing countries may oppose protectionism. When adjusting for the purchasing power parity, the impact of trade openness on labor productivity, although positive and significant, is not as pronounced as it is for other definitions of openness.     

Estimating the impact of trade specialization and trade policy on poverty in developing countries

This paper investigates the impact of trade specialization on poverty. The empirical findings show that in developing countries manufacturing exports contribute to poverty reduction. But agricultural exports have a more significant effect on poverty in low-income countries. The analysis also confirms that trade specialization reduces poverty but under specific trade specialization patterns and policy conditions.     

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。     

数据挖掘中有序资料间距差异的量化与聚类分析

本文针对数据挖掘中的多分类有序资料,提出了用秩分析的方法对其间距差异作出界定。在秩分析的基础上,将有序定性资料定量化,并用于聚类分析。最后,利用该方法对大学生隐性教育调查资料进行了剖析。     

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.     

Shipping cost and development of the landlocked developing countries: Panel evidence from the common correlated effects approach

We estimate the impact of shipping cost on development for landlocked developing countries (LLDCs). Since container trade is important to them, we construct a country-specific measure of shipping cost, called HarpexCost, which combines the global cost of container shipping with information on how exposed to container shipping each LLDC is. We employ the common correlated effects (CCE) estimator of Pesaran (Econometrica, 2006; 55: 967) to first estimate the impact of HarpexCost on the LLDCs’ development, and then recover the actual impact of shipping cost from these estimates. Overall, we observe that shipping cost has large negative effects on the LLDCs. Building upon these results, we provide new estimates on the cost of landlockedness and how trade benefits their development.     

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.     

User-generated content in the era of digital well-being: A netnographic analysis in a healthcare marketing context

The disruptive strength of digital technologies in healthcare has led to valuable changes in the search for information and the application of these technologies. The introduction of these technologies has altered, sometimes even inverting, the doctor–patient relationship. User-generated content on social media and online communities represents a form of disruptive cooperation that has huge potential benefits for the future growth of society. Using netnographic analysis of smart patients and data from an online survey of Italian oncologists and health professionals, this paper shows the impact of user-generated content on the pursuit of personal well-being. The linkages between new forms of knowledge, business, and innovation are highlighted. Based on an analysis of the emerging concept of systems medicine, this paper focuses on the dimension of participation. A data-driven approach is adopted, and the data, information, knowledge, and wisdom framework is used to explain how people employ user-generated content in their pursuit of well-being. Managerial perspectives and suggestions for future research developments are discussed.     

On the “Invisible Inventory Conundrum” in RFID‐Equipped Supply Chains: A Data Science Approach to Assessing Tag Performance

Recent trade reports suggest that RFID implementation continues to lag lofty projections. A primary concern is that, despite the high cost of implementing RFID systems, realized read‐rates fall short of expectations. This results in the invisible inventory conundrum whereby tagged merchandise may still not be accurately represented in inventory records. Drawing from data science to address this issue, we ask: How can directed data mining models be used to identify laboratory test performance criteria for RFID tags that operate reliably across the idiosyncratic facilities (i.e., unique DCs, warehouses, and stores) that comprise apparel retailers’ supply chains? We investigate this question by advancing a methodology that integrates laboratory test performance data, field tests of RFID tags fixed to apparel items and scanned under normal operating conditions, and the application of five directed data mining models to the integrated data set of laboratory and field test results. Our analyses of 45,416 observations show that two directed data mining models may identify—with near‐100% accuracy—laboratory test criteria that discriminate tags having 99% or greater read‐rates in the field. Accordingly, our study validates a generalizable methodology for identifying technical performance standards for tags that operate reliably within apparel retailers’ supply chains.     

Financial globalisation and the labour share in developing countries: The type of capital matters

In this paper, we investigate how de facto financial globalisation has influenced the labour share in developing countries. Our main argument is the need to distinguish between different types of capital in this context as they differ in their effect on the host countries' production process and vary concerning their bargaining power vis-à-vis labour. Our econometric analysis of the impact of foreign direct versus portfolio investment in a sample of about 40 developing and transition countries after 1992 supports this claim. Using different panel data techniques to address potential endogeneity problems, we find that foreign direct investment has a positive effect on the labour share in developing countries, while the impact of portfolio investment is significantly smaller and potentially negative. Our results also highlight that de facto foreign investment cannot explain the decline of the labour share in developing countries over the investigated period.