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This descriptive–analytic study identified the factors affecting the degree of fatigue and sleepiness of heavy-vehicle drivers, assessed their effects, and ranked them according to extent of influence by using statistical analysis and the technique for order of preference by similarity to ideal solution (TOPSIS). Data were collected through interviews guided by a questionnaire, through which three main categories of factors that contribute to crashes caused by fatigue and sleepiness were discussed. These categories are (I) human, (II) road and environmental conditions, and (III) vehicle-related factors. The results showed that human and road and environmental conditions exert the strongest and weakest effects, respectively. The statistical and TOPSIS results revealed that the first four factors that exert the strongest effects are inappropriate behaviors of passengers and goods owners, non-standard roads, inappropriate behaviors of police, and economic problems of heavy-vehicle drivers.

Electrophoretic Deposition (EPD) is one of the alternative methods to fabricate and enhance the performance of Li-ion batteries. It enables the fabrication of electrodes with outstanding qualities and different electrochemical properties by the great domination over various parameters. EPD facilitates the processing of electrodes by binder-free grafting of nanomaterials, such as graphene derivatives, carbon nanotube, and nanoparticles, into the battery electrodes. It also enables the assembly of the free-standing electrodes with 3D structure and provides possibilities, such as the fabrication of the electrodes with an oriented microstructure, even on 3D substrates to improve the energy or power density. In this review, after an introduction to EPD, the effect of EPD parameters on the properties of the prepared electrodes is reviewed. Then, EPD is compared with tape cast, and its advantages over the conventional method are evaluated. Also, employing the EPD method as an intermediate process is discussed. Finally, the application of EPD in the fabrication of separators is assessed, and the prospects for the future are described.

Identification of accident-prone road segments based on the results of safety inspection is a widely accepted method for prioritization of safety improvement efforts particularly for low volume traffic roadways. In the existing methods, the items listed in a risk factor checklist are given qualitative or exact scores and then risk items are aggregated without considering the uncertainty in the subjective judgment of the inspector. Other shortcomings include the failure to consider the relative importance of risk factors and the failure to provide a solution to deal with absence or incompleteness of data. This study introduces a method of road safety evaluation and a distributed hazard index (DHI) based on evidential reasoning approach. This approach allows the risk factor to be expressed by several degrees with different values of confidence instead of one definite figure. By adjustment for the extent of user exposure and the approach of road safety authority to improvement, the distributed index is turned into the Hazard Index (HI), which serves as measure for final prioritization of road sections. The proposed method is applied to a case study and the resulting sections ranking show good consistence with the results of empirical Bayesian method based on accident records.

This study evaluated the effects of electric arc furnace (EAF) and basic oxygen furnace (BOF) slags on the fatigue behavior of asphalt mixes. After the initial evaluation of the properties of steel slags, nine sets of laboratory asphalt mixes were prepared. For each set, different proportions of limestone coarse aggregates (≥2.36 mm) were replaced with steel slags. The main laboratory protocol involved determining the fatigue life of the samples at 20 °C. Four-point bending beam fatigue tests were performed under controlled strain mode loading at different strain levels to characterize the fatigue behavior of the asphalt mixtures. The adhesion between asphalt and aggregates were assessed using the free surface energy concept. Results revealed that the addition of slags in the mixtures considerably enhanced fatigue life but that the fatigue life of the specimens containing EAF slag was greater than that of the specimens containing BOF slag. The findings also indicated that fatigue life increased with rising slag content. The surface energy test revealed that the adhesion between asphalt and aggregate was stronger in the slags mixtures than in the limestone mixtures. Such adhesion can explain the increased fatigue life of mixtures containing slags.

The present study investigated the relationship between offenses of drivers in terms of number of tickets and their demographic, behavioral, and personality factors. The researchers had the...

The use of ranking methods in safety retrofit projects, in order to reduce uncertainty to an acceptable level, is a crucial problem. This paper presents a multidimensional method for prioritizing safety retrofit projects, in which uncertainty is taken into account in benefits estimation (accident reduction) and costs. Data Envelopment Analysis (DEA) with uncertainty assessment is described to help decision makers select the most cost effective projects. It is different from other ranking methods in that this approach adds standard errors of crash modification factor and crash costs in selecting process as well as the average values. Furthermore, this model is applied to a sample of intersections that are required to improve safety. Results have revealed that the proposed model is a suitable tool in selecting efficient projects when tolerances in accident reductions and project cost are incorporated. Comparative study between the proposed model and incremental benefit cost analysis and integer programming methods has also indicated that some changes in the list of selected projects considering the uncertainty impacts of data were observed. This analysis allows such safety projects to be identified. This also provides more complete information for safety analysts to allocate a limit budget to more efficient safety projects.

Safety experts have, in recent years, been attentive to roadside accident severity and occurrence. Hitherto, to prioritize road segment hazardousness, there have been little efforts to quantify a well defined indicator. In this regard, the existing indicators are usually very plain and the overall configuration of roadside is rated by experts with an exact number describing its condition. Hence, the uncertainties which come with the subjective judgments cannot be regarded as of any substance. This research contribution therefore presents a procedure to assess the road safety (roadside safety indicator) by means of the evidential reasoning (ER) approach. The betterment of ER as opposed to the available procedures for roadside safety assessment is that the proposed approach makes allowance for the uncertainties which may arise from individual judgments. Additionally, when there is a dearth of evidence concerning factors which affect roadside hazardousness severity to collate several roadside segments, this procedure will offer the benefits of utilizing the maximum/minimum utility function. With the aid of the drawn indicator, organizations and agencies responsible for ensuring road safety can reach more flexible decisions to set in-place uncertain planning and road segments priorities. This indicator can also be utilized as a variable to include roadside conditions in crash severity prediction models. A field case study has also been carried out in an attempt to follow and validate the proposed approach which is based on the run-off accident history for a sample road segments. The crash data confirm the suggested indicator.

The aim of the present study is the representation of a method to identify and prioritize accident-prone sections (APSs) based upon efficiency concept to emphasize accidents with regard to traffic, geometric and environmental circumstances of road which can consider the interaction of accidents as well as their casual factors. This study incorporates the segmentation procedure into data envelopment analysis (DEA) technique which has no requirement of distribution function and special assumptions, unlike the regression models. A case study has been done on 144.4km length of Iran roads to describe the approach. Eleven accident-prone sections were identified among 154 sections obtained from the segmentation process and their prioritization was made based on the inefficiency values coming from DEA method. The comparisons demonstrated that the frequency and severity of accidents would not be only considered as the main factors for black-spots identification but proper rating can be possible by obtaining inefficiency values from this method for the road sections. This approach could applicably offer decision-making units for identifying accident-prone sections and their prioritizations. Also, it can be used to prioritize intersections, roundabouts or the total roads of the safety organization domain.