A hybrid clustering and classification approach for predicting crash injury severity on rural roads

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011–2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.     

Hedging performance of multiscale hedge ratios

In this study, the wavelet multiscale model is applied to selected assets to hedge time-dependent exposure of an agent with a preference for a certain hedging horizon. Based on the in-sample and out-of-sample portfolio variances, the wavelet-based generalized autoregressive conditional heteroskedasticity (GARCH) model produces the lowest variances. From a utility standpoint, wavelet networks combined with GARCH have the highest utility. Finally, the wavelet-GARCH model has the lowest minimum capital risk requirements. Overall, the wavelet GARCH and wavelet networks offer improvements over traditional hedging models.     

Law‐abiding organizational climates in developing countries: The role of institutional factors and socially responsible organizational practices

The institutional environment of developing countries may lead firms to engage in unlawful firm conduct, which is a pervasive problem in this context. Our paper examines the effectiveness of organizational practices for ensuring that firms adhere to the law in the light of pressures from the institutional environment to be unlawful. Using the lens of anomie theory, we investigate: (a) the negative effect of aspects of the institutional context—regulatory burden and lack of industry munificence—on a law‐abiding climate, a type of organizational climate related to unlawful conduct, and (b) the role of socially responsible organizational practices in combating these negative effects. Survey data were collected from 118 firms and analysed using OLS moderated regression. Our results indicate that a manager's perceptions of regulatory burden and lack of industry munificence are negatively related to the extent to which the firm has a law‐abiding climate. Furthermore, our findings shed light on the ability of socially responsible practices to countervail this effect. While the negative effect of perceived regulatory burden on law‐abiding climate weakens when codes of ethics are used more extensively by a firm, it strengthens when firms hold a CSR certification. The latter finding may be due to the lack of enforcement associated with the specific certification considered in our study.     

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.     

A fuzzy-based roadmapping model for enhancing high-tech small firms’ technological entrepreneurship capabilities: cases of Iran

ABSTRACT

Technology-based firms need to survive via technological changes. Technological entrepreneurship is concentrated on identifying and exploiting the tech-opportunities for creating product innovation. This paper introduces a fuzzy-based tool to promote innovation throughout firms in order to enhance technological entrepreneurship capabilities (TEC). The effort involves identifying components of TEC to develop the inference system, specifying the fuzzy relationship among these components, assessing the TEC in firms, and designing a TEC roadmap after a fuzzy-based gap analysis. Designed roadmaps introduce technological change priorities as a growth path to firms to move up along the ladder of competitiveness. Based on the results, a method for categorizing firms into four distinct levels that involve passive, reactive, proactive and innovative is introduced. Hence, firms have an action plan to enhance TEC from passive to innovative level in a time-based transition pathway.     

Machine-Part Cell Formation Problem Using a Group Based League Championship Algorithm

This article proposes a new algorithm for grouping problems that is a grouping version of league championship algorithm (GLCA). We compare the performance of GLCA with several well-known algorithms published in the present literature and select a set of 20 most widely used benchmarks of cell formation sample problems posing as a grouping problem. We used a truncated geometric algorithm to find the number of initial cells. Our computations reveal that GLCA can reach the best-known solution for 17 of the 20 benchmark problems, and improve the solution of three others with a 1.4% average gap.     

Input-Orientated Data Envelopment Analysis Framework for Measuring and Decomposing Economic,Environmental and Ecological Efficiency: An Application to OECD Agriculture

This paper presents an input-orientated data envelopment analysis (DEA) framework which allows the measurement and decomposition of economic, environmental and ecological efficiency levels in agricultural production across different countries. Economic, environmental and ecological optimisations search for optimal input combinations that minimise total costs, total amount of nutrients, and total amount of cumulative exergy contained in inputs respectively. The application of the framework to an agricultural dataset of 30 OECD countries revealed that (i) there was significant scope to make their agricultural production systems more environmentally and ecologically sustainable; (ii) the improvement in the environmental and ecological sustainability could be achieved by being more technically efficient and, even more significantly, by changing the input combinations; (iii) the rankings of sustainability varied significantly across OECD countries within frontier-based environmental and ecological efficiency measures and between frontier-based measures and indicators.