Effectiveness of maritime safety control in different navigation zones using a spatial sequential DEA model: Yangtze River case

This paper aims to analyze the effectiveness of maritime safety control from the perspective of safety level along the Yangtze River with special considerations for navigational environments. The influencing variables of maritime safety are reviewed, including ship condition, maritime regulatory system, human reliability and navigational environment. Because the former three variables are generally assumed to be of the same level of safety, this paper focuses on studying the impact of navigational environments on the level of safety in different waterways. An improved data envelopment analysis (DEA) model is proposed by treating the navigational environment factors as inputs and ship accident data as outputs. Moreover, because the traditional DEA model cannot provide an overall ranking of different decision making units (DMUs), the spatial sequential frontiers and grey relational analysis are incorporated into the DEA model to facilitate a refined assessment. Based on the empirical study results, the proposed model is able to solve the problem of information missing in the prior models and evaluate the level of safety with a better accuracy. The results of the proposed DEA model are further compared with an evidential reasoning (ER) method, which has been widely used for level of safety evaluations. A sensitivity analysis is also conducted to better understand the relationship between the variation of navigational environments and level of safety. The sensitivity analysis shows that the level of safety varies in terms of traffic flow. It indicates that appropriate traffic control measures should be adopted for different waterways to improve their safety. This paper presents a practical method of conducting maritime level of safety assessments under dynamic navigational environment.     

Consistent and robust ranking in imprecise data envelopment analysis under perturbations of random subsets of data

Data envelopment analysis (DEA) is a non-parametric method for measuring the relative efficiency of a set of decision making units using multiple precise inputs to produce multiple precise outputs. Several extensions to DEA have been made for the case of imprecise data, as well as to improve the robustness of the assessment for these cases. Prevailing robust DEA (RDEA) models are based on mirrored interval DEA models, including two distinct production possibility sets (PPS). However, this approach renders the distance measures incommensurate and violates the standard assumptions for the interpretation of distance measures as efficiency scores. We propose a modified RDEA (MRDEA) model with a unified PPS to overcome the present problem in RDEA. Based on a flexible formulation for the number of variables perturbed, MRDEA calculates the empirical distribution for the interval efficiency for the case of a random number of variables affected. The MRDEA approach also decreases the computational complexity of the RDEA model, as well as significantly increases the discriminatory power of the model without additional information requirements. The properties of the method are demonstrated for four different numerical instances.     

Determinants of scale efficiency in the Brazilian 3PL industry: a 10-year analysis

This paper focuses on the Brazilian third-party logistics (3PL) sector which, facing increasing competition, offers clients a wide variety of services/IT technologies in the quest to bolster efficiency. The main research objective is to determine which variables significantly impact the 3PLs' scale efficiency by applying a two-stage DEA model. Based on an unbalanced panel model, secondary data from the annual study published by Revista Tecnologística (years 2001–2010) were collected and analysed. The required inputs and outputs for the analysis were identified, as were the contextual variables that can affect the scale efficiency of the 3PLs. The results support findings in the literature regarding the role coordination processes play in logistics performance. Managerial implications for both shippers and 3PLs are discussed.     

Data envelopment analysis as a tool for constructing scientometric indicators

It is shown that Data Envelopment Analysis (DEA) ca be used to construct relative scientific and technological indicators. The method is explained and illustrated using countries as objects of study; GDP, active population and R&D expenditure as inputs, and publications and patents as outputs. Using these parameters the efficiency of countries is assessed.     

How to assess sustainability of countries via inverse data envelopment analysis?

In this paper, two input-oriented and output-oriented inverse semi-oriented radial measures are presented. Such models are applied to determine resource allocation and investment strategies for assessing sustainability of countries. Our proposed models can deal with both positive and negative data. In our proposed inverse input-oriented data envelopment analysis (DEA) model, optimal inputs are suggested while outputs and efficiency score of decision-making unit (DMU) under evaluation are unchanged. Similarly, in our proposed inverse output-oriented DEA model, optimal outputs are proposed while inputs and efficiency score of DMU under evaluation are kept unchanged. For the first time, we propose two new inverse DEA models to handle resource allocation and investment analysis problems given sustainable development aspects in the presence of negative data. A case study is given for assessing sustainability of countries.     

The scientific wealth of European nations: Taking effectiveness into account

In this study we continue the application of Data Envelopment Analysis (DEA) to assess the efficiency and effectiveness of the R&D effort of European countries. We use GDP, active population and R&D expenditure as inputs, and publications and patents as outputs. Being effective means that, in order to obtain a maximum efficiency score countries are forced to perform on every output goal. A discussion of each country's performance and a comparison with May'sScience results concludes our analysis.     

Highway accident severities and the mixed logit model: an exploratory empirical analysis

Many transportation agencies use accident frequencies, and statistical models of accidents frequencies, as a basis for prioritizing highway safety improvements. However, the use of accident severities in safety programming has been often been limited to the locational assessment of accident fatalities, with little or no emphasis being placed on the full severity distribution of accidents (property damage only, possible injury, injury)-which is needed to fully assess the benefits of competing safety-improvement projects. In this paper we demonstrate a modeling approach that can be used to better understand the injury-severity distributions of accidents on highway segments, and the effect that traffic, highway and weather characteristics have on these distributions. The approach we use allows for the possibility that estimated model parameters can vary randomly across roadway segments to account for unobserved effects potentially relating to roadway characteristics, environmental factors, and driver behavior. Using highway-injury data from Washington State, a mixed (random parameters) logit model is estimated. Estimation findings indicate that volume-related variables such as average daily traffic per lane, average daily truck traffic, truck percentage, interchanges per mile and weather effects such as snowfall are best modeled as random-parameters-while roadway characteristics such as the number of horizontal curves, number of grade breaks per mile and pavement friction are best modeled as fixed parameters. Our results show that the mixed logit model has considerable promise as a methodological tool in highway safety programming.     

Data envelopment analysis with multiple modes of functioning. Application to reconfigurable manufacturing systems

In principle, data envelopment analysis (DEA) does not consider the possibility, which can occur in practice, of a production system being able to operate in different modes of functioning. In this paper, a new DEA modelling approach is proposed in which the different modes of functioning are taken into account and included in the analysis. The observed input consumption and output production in each mode of functioning is used to derive a mode-specific technology. The overall DEA technology aggregates these mode-specific technologies according to their respective time allocations. The proposed model computes a target operating point for each mode of functioning so that the operation of the overall system is efficient. The proposed approach is applied to assess the technical, cost and allocative efficiency of a reconfigurable manufacturing system. The inputs considered are modules/tools usage, labour and energy consumption. The outputs are the number of units produced of each part type. The production possibility set is determined by previous observations of the system functioning, from which the best practices can be identified. Technical, cost and allocative efficiency scores can be computed. The proposed approach not only generates input cost savings but also lead time reductions.     

模糊权重约束的DEA模型及其应用

基于Zadeh模糊优越集定义,应用模糊权重约束DEA模型对医院服务效率进行评价.通过最大化模糊隶属函数来确定投入与产出权重的上限和下限,以避免传统DEA模型中权重为0的缺陷.实例选取期末实有床位数和医护人员数为两个投入变量,总诊疗人次和总出院人次为两个产出变量,评价广州市15所医院的相对服务效率.评价结果对医院管理决策更具客观性和实用性.     

Multiple-vehicle traffic accidents in Hong Kong

‘Multiple-vehicle traffic accident’ refers to a crash between two or more moving objects. Unlike single-vehicle accidents, not all drivers involving in a multiple-vehicle accident are responsible for the occurrence of the event. Accordingly, variables such as road type, speed limit and number of vehicles involved in the accident are expected to play a much more important role in association with injury severity in multiple-vehicle accidents. To study the factors influencing injury severity of multiple-vehicle traffic accidents, a population-based study was conducted. The traffic accident data was obtained from the Traffic Accident Data System (TRADS), which was developed by the Transport Department, Police Force and Information Technology Services Department, Hong Kong. Multiple-vehicle traffic accidents (N = 10,630) occurring during the 2-year period 1999/2000 were considered. Potential risk factors such as district, human, vehicle, safety, environmental and site factors were examined. Categorizing injury severity into “fatal/serious” and “slight”, a stepwise logistic regression model was applied to the population data set. The district board, time of the accident, driver's gender, vehicle type, road type, speed limit and the number of vehicles involved are significant factors influencing the injury severity. Identification of risk factors for severe traffic accidents provides valuable information to help with new and improved road safety control measures.     

Optimization of Degree of Conformance in Multiresponse–Multistage Systems with a Simulation‐based Metaheuristic

In today's manufacturing and service systems, entities are progressed across the several stages of operations wherein one or more quality characteristic may be formed. The quality of final system outputs depends on the quality of intermediate characteristics as well as design parameters in each stage. This paper presents a new mathematical program to simultaneously optimize multiple quality characteristics in multiple stage systems. Multivariate form response surface methodology is applied with iterative seemingly unrelated regression as the estimation method to extract the relationships between the outputs and inputs in each stage. Because the intermediate response variables may act as covariates in the next stages, the probabilistic patterns of the response surfaces are considered by association with the quality of the previous stages. The objective function in the proposed model is the acceptance probability of the outputs based on predefined specification limits. A combination of Monte Carlo simulation and the genetic algorithm is also proposed to solve the final stochastic optimization model. At the end, the applicability of the proposed approach is illustrated by a numerical example. Copyright © 2014 John Wiley & Sons, Ltd.     

Evaluation and interpretation of knowledge production efficiency

We propose an improved Data Envelopment Analysis (DEA) model to evaluate the efficiency of research groups in the area of information science in PR China. By taking the research groups as Decision Making Units (DMUs), the budget of the projects and size of the groups as inputs and the quantity and quality of publications produced by the groups as outputs of the model, the relative efficiencies of 21 research projects are evaluated. Then, we move to focus on the issues of knowledge management of the organizations that undertook these projects and attempt to explore the underlying reasons of high research efficiency. Through integrating the evaluation outcomes into research process, three indicators of knowledge management are identified for the best practice groups with highest research efficiency. The findings verify that the proposed model is valid and practical to assess research performances on the basis of bibliometric indicators.     

Sentence severity and the drinking driver: Relationships with traffic safety outcome

While a major response to the drinking-driving problem has been to increase penalties for drinking-driving offences, the impact of sentence severity on the driving behaviour of offenders (specific deterrence) remains unclear. In this research, relationships between aspects of sentence severity (licensedeterrence) suspension, fine, jail term, assignment to probation, or temporary absence programmes) and postconviction accidents and drinking-driving convictions were examined, while controlling statistically for demographic and previous driving record factors. License suspensions were consistently associated with traffic safety benefits. However, increasing severity of other aspects of punishment seemed unrelated to outcome or was associated with increased traffic safety problems. As well, important differences between first, second, and multiple offenders were observed, which may be related to the impact of different aspects of sentence severity on driving behaviour.     

Efficiency evaluation of basic research in China

Summary Following the increasing investment on basic research in China, the outputs of basic research have been greatly enhanced. In this paper, the relative efficiency of investments in basic research is analyzed by adopting statistical regressions and Data Envelopment Analysis (DEA) method. Preliminary results show that injected investment seems to be the main driving force for the increased basic research outputs in China. It is found that there were significant improvements on overall efficiency from 1991 to 1996, although this trend has noticeably slowed down since 1996. Possible causes of this slow-down are discussed.     

Suggestions for an improved system for statistical data about road accidents and road traffic

Data information systems for road accidents and road traffic must satisfy high standards of relevance and quality. The general outline of an improved system for collecting road accident data is given. The system is characterized by the use of statistical sampling methods. The police, the insurance companies and the hospitals are recommended as sources of information about the total accident population. A statistical sample of all identified accidents is then investigated in more detail by special local investigation groups. A hypothetical numerical example is given to show how the suggested system would work in practise. Road accident data should not be isolated from road traffic data. An improved system for collecting information on road traffic is also discussed. This consists of a basic system (founded on statistical sampling methods) for estimation of the total volume of traffic and a few other essential variables such as the volume divided into speed and vehicle types.     

Previous convictions or accidents and the risk of subsequent accidents of older drivers

The over-involvement of elderly drivers in collisions has a potentially adverse effect on highway safety. The question for most experts in traffic research is whether we can predict the individual risk of accidents and which variables are the best predictors, especially for this population. For a better understanding of the elderly drivers' problems, this study aimed to describe the most common types of accidents in the elderly population of drivers living in Quebec (> or = 65 years of age). The second objective of the study was to analyse the relationship between previous accidents or convictions and the risk of subsequent accidents. The results show that: (1) elderly drivers are characterised by error accidents involving more than one car, especially at intersections, (2) prior accidents are a better predictor for accident risk than prior convictions and (3) these trends steadily increase with each age group (drivers 65 years old to 80 years or more). The results are discussed in relation to the literature on risk behaviour of the elderly drivers.     

Area-wide urban traffic calming schemes: a meta-analysis of safety effects

This paper presents a meta-analysis of 33 studies that have evaluated the effects on road safety of area-wide urban traffic calming schemes. Area-wide urban traffic calming schemes are typically implemented in residential areas in towns in order to reduce the environmental and safety problems caused by road traffic. A hierarchical road system is established and through traffic is removed from residential streets by means of, for example, street closures or one-way systems. Speed reducing devices are often installed in residential streets. Main roads are improved in order to carry a larger traffic volume without additional delays or more accidents. The meta-analysis shows that area-wide urban traffic calming schemes on the average reduce the number of injury accidents by about 15%. The largest reduction in the number of accidents is found for residential streets (about 25%), a somewhat smaller reduction is found for main roads (about 10%). Similar reductions are found in the number of property damage only accidents. The results of evaluation studies are robust with respect to study design. There is no evidence of publication bias in evaluation studies. Study findings are found to have high external validity.     

Traffic fatalities and injuries: the effect of changes in infrastructure and other trends

An analysis of how various road infrastructure improvements affect traffic-related fatalities and injuries is conducted while controlling for other factors known to affect overall safety. The road infrastructure elements analysed include total lane miles, the fraction of lane miles in different road categories (interstates, arterial, and collector roads), the average number of lanes for each road category, and lane widths for arterials and collector roads. Other variables that are controlled for in the study include total population, population age cohorts, per capita income, per capita alcohol consumption, seat-belt legislation (and seat-belt usage), and a proxy variable that represents underlying changes in medical technology. The data used is a cross-sectional time-series database of US states and is analysed using a fixed effects negative binomial regression that accounts for heterogeneity in the data. Data from all 50 states over 14 years is used. Results strongly refute the hypothesis that infrastructure improvements have been effective at reducing total fatalities and injuries. While controlling for other effects, it is found that demographic changes in age cohorts, increased seat-belt use, reduced alcohol consumption and increases in medical technology have accounted for a large share of overall reductions in fatalities.     

Cycling to work in Brazil: Users profile, risk behaviors, and traffic accident occurrence

In 2006, we carried out a cross-sectional study in the urban area of Pelotas, Southern Brazil, with the aim of outlining the profile of bicycle commuters, analyzing their use of safety equipment and risk behaviors and the association between these variables and involvement in traffic accidents in the previous 12 months. This study was based on the baseline survey carried out prior to an educational intervention aimed at reducing accidents among cyclists. The sample included 1133 male subjects aged 20 years or more, and who used a bicycle for commuting. Crude and adjusted analyses were carried out using Poisson regression. We recorded a total of 152 reported traffic accidents in the 12 months preceding the interview, involving 10.8% of subjects. Most risk behaviors studied and the use of safety equipment showed no significant association with accidents. Only commuting by bicycle seven days per week, as opposed to five or six, and a combination of extremely imprudent behaviors such as zigzagging through traffic, riding after ingesting alcohol, and high-speed riding were found to be risk factors for accidents. Our findings suggest that in the context where the study was done (poor road signaling, limited policing, aggressive driving) changing cyclist behavior may not have substantial impact in terms of accident reduction before other road traffic interventions are implemented.     

A new data envelopment analysis (DEA) model to select eco-efficient technologies in the presence of undesirable outputs

Recent global attention to the challenges of environmental protection is forcing firms and governments to evaluate, rank, and select eco-efficient technologies. Technologies may consume inputs to produce both desirable and undesirable outputs. It seems that the data envelopment analysis (DEA) is a proper method to evaluate eco-efficient technologies. There are some DEA extensions for dealing with undesirable output, and sometimes it is difficult to choose a suitable model to evaluate the technologies. The challenge becomes much more complex when the outcomes of models are not similar. In such a condition, subjective selection of alternative DEA models may lead to deviation from an optimal decision. Accordingly, the objective of this paper is to develop a combined model to include all characters of the previous DEA techniques in a flexible model to select optimum eco-efficient technology in the presence of undesirable outputs. A case study demonstrates the application of proposed procedure.