Fuzzy Logic is one of the concepts that has created different scientific attitudes by entering into various professional fields nowadays and in some cases has made remarkable effects on the results of the practical researches. However, the existence of stochastic and uncertain situations in risk and accident field, affects the possibility of the forecasting and preventing the occurrence of the accident and the undesired results of it.In this paper, fuzzy approach is used for risk evaluating and forecasting, in accidents caused by working with vehicles such as lift truck. Basically, by using fuzzy rules in forecasting various accident scenarios, considering all input variables of research problem, the uncertainty space in the research subject is reduced to the possible minimum state and a better capability of accident forecasting is created in comparison to the classic two-valued situations. This new approach helps the senior managers make decisions in risk and accident management with stronger scientific support and more reliably.

Truck accidents are an issue of concern due to their severity. Logit modeling and Neural Network modeling are performed to investigate factors such as vehicle, roadway, environment and driver characteristics that can potentially contribute to the severity of truck accidents. The objective of this study is to present models that can predict the severity of truck accidents and to identify the important factors causing these accidents. Comparison between neural networks and logit modeling are made using vehicle crash data on two-lane rural highways in Iran. A variety of variables related to roadways, vehicles, environment and drivers, such as, driver fatigue, head-on collision and lack of vehicle control, are found to have a significant bearing on the severity of truck accidents. Also, investigating the marginal effects of variables showed the same variables to be significant. The results of the comparison between the logit and neural network model indicated that they both show similar patterns regarding the effects of different variables causing truck accidents, with the logit model providing better results.      

Uncertain and stochastic conditions of accidents could affect the risk and complexity of decisions for managers. Accident prediction methods could be helpful to confront these challenges. Fuzzy inference systems (FIS) have developed a new attitude in this field in recent years. As lift truck accidents are one of the main challenges that industries face worldwide, this paper focuses on predicting the possibility of these types of accidents. At first, the data collection is done by using interviews, questionnaires, and surveys. An FIS approach is proposed to predict the possibility of lift truck accidents in industrial plants. Furthermore, our approach is validated using data from many real cases. The results are approved by the multivariate logistic regression method. Finally, the output of the fuzzy and logit models is compared with each other. The re-validation of the fuzzy control model and high consistent of the output of these two models is presented.

Background: The association between the occurrence of road accidents and WMSD is a topic that has attracted much attention in the transportation industry. This study aimed to investigate the prevalence of musculoskeletal disorders in professional truck drivers and their impact on the occurrence of road accidents. Materials and Methods: In this descriptive study, 200 professional truck drivers were selected by a two-stage cluster sampling method from occupational medicine centers in Tehran province, Iran in 2017. The data were collected from the two groups of with and without accident. The three-part questionnaire on ergonomics and safety of drivers was used, which included items asking about demographic characteristics, stress, job satisfaction, details of accidents, and the severity of musculoskeletal disorders in nine areas of the body. The central indices and non-parametric statistical tests (Kruskal-Wallis, Spearman) were applied to analyze the data using spss Ver 24. Results: The highest frequency of pain was observed in drivers without and with accidents in the back, knees, and shoulders, respectively,however, the most severe pain was reported for the low back and neck. Drivers between 45 and 60 years old reported the highest number of accidents. The association correlation between the number of road accidents and stress score among drivers with accident experience was negatively significant (P > 0. 05). However, the association between musculoskeletal disorders and accidents was not statistically significant. Conclusion: Factors other than musculoskeletal disorders were involved in road accidents that require more investigations.

Introduction: A review of the existing literature shows the importance of mental health in preventing traffic accidents. Therefore, the current study aims to determine the mentioned mental disorders in relation with the history of accidents among truck drivers. Methods: This cross‑, sectional study was done with two groups of truck drivers (with and without a history of accidents) in Iran. 56 people with a history of accidents and 410 people without a history of accidents participated in this study. At first, using questionnaires, the demographic information of the participants including age, education level, cigarette use, and addiction to drug was collected. Then, the mental disorders of the participants were evaluated by a 71‑, question, short form of the multidimensional Minnesota Multiphasic Personality Inventory (MMPI). The evaluated disorders are the HS or hypochondria, D or depression, HY or hysteria, Pd or social mental deviation, Pa or paranoia, Pt or mental weakness, Sc or schizophrenia, and Ma or hypomania. The tests used for data analysis include descriptive tests and Chi square. Results: Man–, Whitney U test showed that status of mental disorders, as revealed by the MMPI questionnaire, had a significant difference between the two groups with and without history of driving accidents (P ˂,0. 001). The results showed that mental disorders of depression, hysteria, social mental deviation, paranoia, schizophrenia, and hypomania in individuals with history of driving accidents led to meaningful differences from individuals without history of driving accidents (P ˂,0. 048). Conclusion: The results of the current study showed that generally, individuals with history of driving accidents had significantly more mental disorders than people without history of driving accidents.