Scientific Quarterly of Rahvar
Year:2020 | Volume:8 | Issue:31 |Papers Count:8

Identifying and analyzing crucial parameters that cause accidents in highways, can help improving traffic. This paper addresses a multi-parameteroptimization problem in order to identify the parameters affecting the severityof accidents on highways in Tehran city and uses a combination model ofNeural Network and Genetic Algorithm to perform the analysis. The methodof this research is descriptive-cross-sectional. The statistical population of thisstudy is the accidents data on the highways of Tehran during 2015-2016. Inthis research, it is attempted to identify and prioritize the parameters affectingthe severity of accidents in Tehran city using a combination model of neuralnetwork and genetic algorithm. For this purpose, in the hybrid model, theseverity of the accident is considered as the dependent variable and the fourgeneral categories of variables namely climate, road, vehicle and driver areconsidered as independent variables. Then, using artificial intelligence methodand data preprocessing, the optimal structure of the neural network model wasdetermined and finally, the result of the neural network model was consideredas the input of the genetic algorithm. The results not only determines andprioritizes the main parameters affecting the severity of accidents inTehran(including: 1-driver behavior, 2-how the vehicle is moving, 3-type ofvehicles and 4-highways safety status), but also Indicates that the combinationmodel of Neural Network and Genetic Algorithm has a good performance inidentifying the parameters affecting crash severity in Tehran, And couldprovide a new insight into designing a pattern to understand better and preventfuture accident-related accident injuries.

Traffic lights are considered as a way to improve traffic safety andperformance at intersections. Lots of crashes occur at intersections. There aremany accidents at intersections and these accidents cause financial losses, injuries, and even death. This research was carried out to predict the crashseverity of lighted intersections in Qazvin city, and examines the variables thataffect the incidence of severe accidents. Accident data were used base onQazvin police reports and data provided by Qazvin municipality traffictransportation department that includes 288 accidents at the 38 lightedintersections of the Qazvin city in the years 2014 to 2016. The accidents reportdescribes the circumstances in which an accident occurred. The accidents wereranked in three categories according to their severity including damage, injuryand fatal accidents that based on statistics (128 damages, 156 injury and 4fatalities). Sequential logit model was used in this research. The results showthat the presence of some variables such as accident time in the day, rate ofintersection volume and the existence of an island actually reduces thelikelihood of occurrence of a accident with a high severity. In contrast, thepresence of some variables such as accidents in summer, type of collision, howto collide, pedestrian traffic and the number of lanes, actually increases thelikelihood of accidents with a high severity.

Background and aim research of determining the effectiveness of theissuance of third-party insurance based on driver's characteristics in theprevention of accidents has been done in Iran country. Scientific researches ontraffic accidents and their causes indicate that the human factor has asignificant difference in the incidence of accidents, compared with otherfactors. In other words, human and his mistakes can be considered as the mostimportant reason for a driving accident. Thus, many years ago, in manycountries around the world, stringent regulations and regulations on theissuance of third-party insurance have been introduced based on driverattributes for applicant. The general objective of this study is the effectivenessof the issuance of third-party insurance on the basis of driver's characteristicsin preventing accidents. Method this research is practical in type of aim and descriptive-analytical intype of method in terms of survey. The statistical population of this study wasexperts and specialists in traffic and insurance and snowball method was usedfor statistical selection. For this purpose, three linear models for totalaccidents, high-risk driving violations and fatal accidents have been developedusing generalized linear regression modeling. Findings results of this study show that the history of driver accidents, historyof high-risk driving violations, driver's occupation and strict rules so thatmakes it difficult to renew insurance policy, variables related to the issuance ofinsurance policies are based on the driver's attributes, which have a seriouseffect in reducing accidents. Discussion and conclusion Studies have shown that in almost all cases, theimplementation and enforcement of such laws have had a positive impact onthe reduction of accidents. This article evaluates this claim.

Risky behaviors are potentially destructive behaviors that people do, voluntarily or unaware of the possible undesirable consequences. This conceptis influenced by many social and personality factors. This concept is itselfinfluenced by many social and personality factors and this article attempts toinvestigate the role of excitment seeking and impulsivity in high risk drivingbehaviors of intercity drivers of Tabriz-Ahar road. The research method was descriptive in type of correlation. The statisticalpopulation of the study was all drivers of Tabriz-Ahar Road with at least twoyears of driving experience in 2018. From 130 drivers, a sample of 100 peoplewas selected as available. The tool for measuring the research were GeneralHealth Questionnaire (GHQ), Zuckerman Excitement seeking Questionnaire(SSS), Barratt Impact Assessment Questionnaire, and Manchester DrivingBehavior Questionnaire. The results showed that there is a significantrelationship between emotion seeking and high risk driving behaviors andthere was also a significant relationship between impulsive components andhigh risk driving behaviors in Tabriz-Ahar road. So, we conclude that the excitement seeking and impulsivity variables are ableto predict the risky driving behaviors of the Tabriz-Ahar road. Given thataccording to the findings of the present study, excitement seeking andimpulsivity are components related to the tendency of high risk drivingbehaviors, it is suggested that informig in field of exciteme seeking, impulsivity and their effects should be considered as one of the effectivefactors in high risk driving behavior training program.

Traffic police Recently pay special attention to the education of childrenthrough the implementation of "Police Assistants" and "Youth Policemen"programs. The purpose of this study was to investigate the impact of the role ofpolice assistants on learning the rules of driving in children. This is a quasiexperimentalstudy and its design is pre-test and post-test. The statisticalpopulation of the study consisted of 10-12 year old boys in Tehran. Thirtychildren were selected by available and purposive sampling method and wererandomly assigned into two experimental and control groups (15 people ineach group). Then a valid and reliable questionnaire (Cronbach's alpha 0. 91)was designed on guidance rules for children. The experimental group was thenassigned the role of police assistant and three weeks later the samequestionnaire was again administered to both groups. The result of this studyshowed that the scores of the control group did not change significantly in thetwo stages of the driving test. whereas in the assistants group, this averageincreased significantly after assuming the police assistant role. but afterassigning the police assistant role to the experimental group, there was asignificant difference between the two groups. Thus, by assigning the role ofthe police assistant to the children, their learning will be significantlyimproved at three levels of attitude, knowledge and behavior.

Personality is a fixed and definite behavioral pattern that determine how werespond to events. . Behavior is generally influenced by personality and driver'sbehavior is not an exception. This study was conducted to compare thepersonality traits and driving behavior of high risk and safe drivers in Marivancity. The research method is causal-comparative method. The statisticalpopulation consisted of all certified drivers in 2015 that referring to insurancecenters and based on available sampling, 225 drivers were selected and dividedinto two groups of safe drivers (non-accident and use of car insurancecoupons) and drivers. High-risk (accident history and use of insurancecoupons). Neo personality Big Five-Factor Questionnaire and ManchesterDriving Behavior Questionnaire were used to measure researchvariables. results of the comparative analysis indicated that there is a significantdifferent in the factors of extraversion, flexibility, adaptability andaccountability between high risk drivers and safe drivers. Also, the amount ofmistakes, errors, intentional and unintentional offenses was higher in high riskdrivers was more than it's amount among safe drivers and this difference wasstatistically significant.

Traffic accidents are a social problem in the country that annually kills a largenumber of people and brings huge economic costs to society. In recent years, in Traffic Management Discussion, there has been a lot of attention toaccidents prediction methods. Accident prediction has a great impact onreducing the number of casualties, injuries and financial losses resulting fromaccidents. The main purpose of this study is to predict traffic accidents usingthe regional spatial model of Big Tehran in 2016. This research is descriptivecrosssectional in terms of data collection method. The statistical population ofthis study is accident statistics and data in 22 districts of Tehran in 2016 whichis registered in the database of traffic police station. In this study, whileanalyzing the factors affecting traffic accidents, regional spatial regressionmodel was used for data analysis and presenting prediction models. Thefindings of this study show that the three factors of human, road and vehicleswere the factors affecting traffic accidents in Tehran; In such a way that theauxiliary variables of the number of trips absorbed and trips produced areinfluenced by the severity of traffic accidents (total accidents involving suddenchange of direction, disregard forwards, disregard of right of way anddisregard of long distance); So that a one percent increase in absorbed andproduced travels leads to a 21. 7 percent and a 23. 2 percent increase in drivingviolations, respectively. The results also showed that by considering thecorrelation structure of the counting data, one can deal with traffic accidentprediction models in the coming years and dentified and controlled the factorsaffecting them.

Background and aim Ten percent of all accidents in the study area arerelated to accidents in urban squares. The purpose of this study was todetermine the effective parameters of accidents in urban squares and to presenta model of accident prediction. Method method of this research is descriptive-survey. In this study, 456accidents were studied that occurred in 26 squares of Ardabil city from 2014 to2016 and were collected the effective factors on each of those accidents. Factors were designed as a questionnaire to select the effective refining factorsand final parameters for modeling using Likert spectrum method and Delphimethod and polling of expert. Statistical sample was selected 102 persons. 40initial effective parameters of the accidents were selected. Questionnaires wereanalyzed by statistical tests, mean and differential power, which identified 16high-impact parameters for modeling. The analysis and comparison of themodeling results were done in two ways. The first method is using a linear-regression statistical model with MiniTab software 14 and the second methodusing artificial neural network model with matlab software. findings The results of statistical analysis show that among the presentedregression models, the best model of accident frequency prediction is consistedof three parameters of square traffic volume, number of passages leading to thesquare, existence of a production site and trip absorption. as well as the resultsof neural network analysis show that the above model in predicting the numberof accidents, is model with four main traffic volume input parameters, thepresence of the taxi station and bus, speed bump on the main pathways, thenumber of passages leading to the intersection.