فهرست مطالب navid nadimi

Many individuals have been affected physically and socially as a result of road crashes. Many of these incidents have irreversible consequences. An immediate solution to improve road safety must be developed. This paper examines the issue of inadequate budget allocations by road departments for road safety. With a budget that does not meet ideal requirements, maintaining an extensive road network poses a challenge. This paper proposes an economic model for improving rural road safety, considering its importance. To minimize costs and reduce road casualties, the proposed model takes into account a number of factors, including the geometric conditions of the roads, relevant information, and the economic conditions of the community. In order to improve road safety, the study employs an integer programming model. In this approach, road geometry, relevant data, and economic considerations are examined in order to implement appropriate safety solutions. This paper discusses safety improvement strategies based on calculated probabilities of implementing them. These probabilities are determined using the Naive Bayes method. In addition to the geometric aspects of the roads, the best method for improving road safety is also considered in the economic model. Validation of the model is based on a case study conducted in Kerman province. The unique characteristics of each rural road in a country can be considered when implementing safety measures. The proposed method is used to accomplish this. As a result of the results, four safety solutions were identified as the most suitable in the Anar-Rafsanjan area, including road lighting, vibrating grooves, guardrails, and repairing damage and unevenness.

Because of its convenience and low cost, rail transportation has become one of the most attractive modes of transportation for passengers and business owners alike. Rail transportation has been reduced in popularity due to accidents. This study investigates the factors that affect the severity of accidents for freight, passenger, and other rail vehicles in three levels of death, injury, and damage. A multinomial logit model (MNL) regression was used to model the Iranian railway accident data from 2006 to 2021. The model quantifies the effects of each variable on the severity of events by quantifying the results. The results of this study indicate that type of accidents (with pedestrians), type of accidents (with road vehicles), speed exceeding the allowable speed (equal to or exceeding 30 km/h), speed during the accident (equal to or exceeding 30 km/h), multilane rails and moderate variables are among the variables that have a greater impact on the severity of accidents than other variables, so investing in these areas improves rail safety. Because of budget constraints, this research work will be effective in attracting the attention of policy makers and transportation managers in this area.

In road pavement design, soil resilient modulus is one of the factors that play a very important role in determining pavement thickness. In the past, determining the Resilient Modulus of soil directly from laboratory results has not always been practical due to its high costs, both in terms of equipment and labor. Therefore, it is possible to predict and determine this parameter based on past field data using artificial intelligence. Our goal in this paper is to develop a model for predicting the resilient modulus of stabilized clay soils using artificial neural networks. In this study, four different soil samples stabilized with additives, such as lime, fly ash, and cement kiln dust, were examined using the pavement design appendix of the AASHTO 2002 specification. By comparing the results to the laboratory data based on the statistical indicators such as regression coefficient (0.99), root mean square error (less than 6 percent), we found that the artificial neural network was highly accurate in predicting the resilient modulus..

Walking is the most traditional mode of transportation that still plays an important role in daily life and since the development of motor vehicles, the issue of pedestrian safety has become important. One of the most important factors affecting pedestrian safety is pedestrian characteristics and behaviors. Valid data are needed for investigating the effects of various factors. However, variables related to behavioral data such as mental conditions cannot be found in the crash database. Thus, in this study, instead of crash data, near-crash data was used. in this regard, using a questionnaire, the number of near-crash data and various aspects of 378 pedestrians' data were collected. Then, the effects of different factors on the pedestrian safety were investigated. In this regard, the effects of various personal characteristics (included age, education, average monthly income and driving years), mental health, walking experience, violations and the feeling of safety while walking on the pedestrian safety were investigated by considering near-crash events and using SEM technique and SmartPLS software. Results indicated that, among the study factors, the violations with a loading of 3.809 and the pedestrians' personal characteristics included age, education, average monthly income and driving years, with a loading of 2.127 have the most and the least impacts on the pedestrian safety. Besides, mental health with a 2.515, walking experience with a 2.446 and the feeling of pedestrian safety while walking with a 2.308 loading affect pedestrian safety.

In developing countries, the population of younger adults is relatively higher. In addition, the frequency of traffic violations, committed by young drivers, is considerable. Consequently, annually a large portion of road crashes is recorded among this age group. This paper aims to study the traffic rule violations of young drivers in Iran. Focusing on the behavior of young drivers and understanding the mechanisms that affect the occurrence of violations among this group of drivers can be helpful to promote traffic safety. For this purpose, 567 drivers in the range of 18 to 40 years old have been studied. Then, different data mining approaches such as descriptive analysis, correlation analysis, multinomial logistic regression (MLR), and Random Forest (RF) were used. The main contribution of this paper is using different analytic methods to gain insight into traffic violations of young drivers and to propose potential countermeasures to decrease this issue. Results indicated that driving over speed limits, red-light running, and angry driving are the most frequent violations. The frequency of using mobile phone while driving, as a source of distraction, has been found to be highly correlated with other violations. As the frequency of previous traffic fines, the number of days with access to private cars, and the frequency of previous crashes increase, more diverse types of violations with high frequencies are expected in the future. In addition, the frequency of risky violations was found to be higher among men and those with lower education levels.

Around the world, various activities were restricted following the outbreak of the Corona virus. Among these changes was the situation of university activities. In order to pursue educational and research affairs, electronic communication was considered. Using a survey form, this article evaluates the status of education and research in the field of civil engineering of Iran during the Corona outbreak based on the opinions of students. The results show that undergraduate students were not satisfied with the quality of learning in this period (about 66% below the average), but graduate students assessed the quality of learning in a virtual way as appropriate (about 21% below the average). In the coming years, there is expected to be a decline in civil engineering graduates from a practical standpoint due to the problems created for the practical course of the undergraduate course (less than 5% were classroom learning) and the collection of data for postgraduate dissertations during the period of Covid-19 pandemic (about 23% had serious problems). The spread of examination fraud (about 40%), as well as the difference in internet access and the systems needed to participate in virtual classes (about 42% were unsatisfied), challenged the issue of educational equity (about 50% were unsatisfied). Generally, virtual education and research in civil engineering cannot completely replace face-to-face methods, but some of its capabilities can complement conventional methods in the future.

Population growth, increased travel demand and, consequently, increased motor vehicle use has led to concerns about road safety in today’s society. Various factors such as driver characteristics, environmental conditions, road surface conditions, and type of vehicles affect the severity of accidents. In this paper, by using structural equation modeling (SEM) method and relying on crashes data, it has been tried to identify the factors that have the greatest effect on the severity of suburban accidents in Kerman province. Then, the results obtained from structural modeling are used to develop a fuzzy inference system (FIS) in order to prioritize solutions in relation to each factor. According to the results obtained from the structural equation modeling, human factor (estimate -0.47) has shown the greatest effect on occurence of accidents with high injury compared to other latent factors in the model. Among the parameters explaining the human factor, using the safety belt variable (estimate 0.85) was the stronger predictor for this factor. Finally, in order to determine the priority of implementing strategies to reduce the severity of crashes by human, environmental, and road factors, the fuzzy inference system has been used due to its acceptable accuracy in solving problems that face uncertainty. The results of the outputs of the fuzzy inference system have been shown that the priority of solutions related to human factors is far more than solutions related to road and environmental factors. Overall, the results of this study can help transportation professionals to prioritize countermeasures to reduce the severity of crashes on the suburban roads in an efficient and optimal manner.

Roads are one of the most important assets in each country. Road network are too extensive and the budget needed for reconstruction is too high. To maintain these properties planning for the maintenance and rehabilitation is an important issue. Aging, traffic (especially trucks) and weather condition in the region cause numerous tensions in the pavement. These tensions are the main reason of distress, reducing ride quality and finally deterioration in the pavement. Because of the increasing rates of the costs associated with maintenance and repair there is a concern about keeping roads in good condition with the lowest costs. The main purpose of this research is to apply an evolutionary model to minimize the costs of maintenance and repair operation and at the same time maximizing the benefits of the system for a long time with acceptable service quality. The models are assessed on the hypothetical networks and the results have been satisfactory..

This paper aims to develop a method to detect dangerous situations for each vehicle based on microscopic traffic data for intelligent vehicles. Here, surrogate safety measures (SSMs) would be applied. Each SSM has unique characteristics and if we could use the advantages of different SSM simultaneously, then the efficiency of intelligent vehicles might be increased. For this purpose, Fuzzy Inference System (FIS) is applied to develop a Combined Surrogate Safety Measure (CSSM). In order to avoid complicating the issue, only rear-end collisions are considered. Microscopic traffic data collected in Modares highway of Tehran is used to develop FIS. Finally, the CSSM results are compared by each SSM statistically. Based on the results, it can be declared that FIS can be helpful to calculate the rear-end collision probability by using different SSMs. This achievement can be useful in promoting the efficiency of autonomous vehicles.

Motorcyclists due to lack of protection at the scene of the crash and also being less exposed in the eyesight of other vehicles are more vulnerable for sever crashes. The main purpose of this study, is to identify the factors affecting the severity of motorcycle crashes.

Neural network, random forest and statistical analysis (comparison of the average severity of accidents in variables) have been used in this paper. To conduct this research, two series of data (accident data and behavioral data collected from motorcyclists in Tehran based on field interview) have been used.

The severity of crashes increases with the rise of the frequency of speeding, passing red lights, using mobile phones, performing dangerous movements, not usign helmet and crossing the pedestrain pathway. However, with the increase in the health status of motorcyclists and the technical appearance of the motorcycle the severity of accidents will decrease. Among the districts of Tehran, district 6 had the highest frequency of crahses. Neural network model, is able to predict fatalities, injuries and propoerty damage crashes with 24, 76 and 51 percent accuracy. Furthermore, in the random forest model, the accuracy of predicting fatal, injury and property damage crashes are 53, 69 and 32 percent.

It indicate that the traffic violations of the motorcyclists are the main reasons for crash severities. Thus, it is necessary to have an overlook about performing more restrict traffic fines for this group. In addition, becasuse of the importance of the individual health condition and technical appearance of the motorcycle it is necessary to determine rules for controling them with issuing cycling licence and other intervals after that. Finally, the high rate of motorcycle crashes in the district of 6 in Tehran can be related to high capacity rate of transit  in the zone.

Roads are considered as one of the most extensive civil infrastructures and national capitals of each country. Pavement maintenance and rehabilitation (M&R) management is one of the most important problems, which can help reducing further costs. Two issues are important in the field of road maintenance and rehabilitation management; first type of M&R option and then time of applying it. In this paper, we have attempted to combine prioritization, artificial intelligence and optimization methods to select the optimal option for maintenance and rehabilitation of pavement sections at any time interval. For this purpose, the analytical hierarchy process is used to prioritize the branches of the pavement network. In the next step, using the linear programming model, the probability of selecting maintenance and rehabilitation options for pavement sections is maximized considering several specific constraints. Fuzzy inference system is used to determine the probability of selecting each maintenance and rehabilitation option in pavement sections. A case study in Mahan (Kerman) is used to run the proposed model. Based on the results, it can be concluded that the proposed algorithm can consider different parameters and indices for pavement branches and sections. In addition, it offers different scenarios for selection of M&R options in a year. The model helps comparing various scenarios based on different budgets for each year. In all, the proposed algorithm facilitates the process of selecting M&R options in the different sections of a road network and provides a scientific approach to manage maintenance roads.

The purpose of this paper is to prepare a hybrid simulation model of system dynamics (SD) - agent-based modeling (ABM) to investigate the mobile service consumers’ behavior in the country. By using the suggested in this article, first the necessity to analysis consumers’ behavior is explained and then the effective factors on mobile ecosystem which influence the consumers’ behavior are explained.For each factor affecting on the ecosystem, related simulations were performed and then by combining system dynamics modeling - agent -based modeling, the behavior of mobile service consumers in the first operator of the country (Hamrahe Aval Company) was examined and finally the income affected by that behavior is analyzed.Results indicated that if no funds by the operator are allocated to the development of native applications and digital platform, after 2 years, the number of active customers of the operator will decrease because of the activities of cultural organizations. By the operator entering into the field of native application production and digital transformation, consumers tended to use more data services instead of voice calls, but because of different data and voice tariffs, the operator's income will not change much in the next 2 years. With the increase of marketing and advertising activities, despite the greater consumer inclination to use data, the main income of the operator decreases, which also needs more attention of policymakers in this area.

In Iran, despite all efforts have been devoted to reduce crash frequencies, statistics indicate that the crash fatalities have recently been increasing. In more than 90% of accidents, human errors and misjudgements have been reported to be the main contributing factor. This has drawn significant attention to problems relating to driving style, which are generally formed by early-stage driver's education and training programs (DETPs). In this regard, in Iran, like any other jurisdiction, beginner drivers must participate in a short-term DETP before taking the driving license exam. The programs consist of two parts: classroom theory education and in-car practical training. This paper seeks to evaluate the effect of DETP on dangerous driving behaviours of drivers using Structural Equation Modelling (SEM). Accordingly, data relating to 510 drivers were gathered, regarding their demographics and attitudes about DETPs specifications. Results indicated that training can be more effective than education in reducing unsafe behaviours. In addition, human characteristics have been identified as an important factor in decreasing risky driving and this can easily fade the impact of a proper DETP. The related administrates also must consider more seriousness for the final exams and assessments of DETPs. Finally, the establishment of strict rules and punishments for traffic violations can be a successful way of increasing the efficiency of DEPTs.

With the tremendous progress in communications in the world, the transformation and behavior of mobile operators and their digitalization, which in the past were only service providers, as well as the creation of different experiences for customers, is inevitable.

The purpose of this study is to create a hybrid simulation of system dynamics and agent based modeling in order to analyze the revenue of the first operator in the country to enter the field of digital platform and development of native applications. Using the model proposed in this article, first the need for operators to enter the digital arena and produce native applications was expressed. Then, the factors that affect the mobile ecosystem that affect the production of applications and the development of required platforms were described. Benefiting from hybrid simulation of system dynamics and agent based modeling, the income of mobile operator in entering and not entering the digital arena and producing native applications were examined.

The findings showed that with the entry of the operator into the field of production of native applications and the adoption of digital approach, consumers tended to use more data services, but due to different tariffs for data and voice, the operator's income up to 2 Next years will not change much.

Traffic safety is one of the main challenging issues in transportation engineering. Hhuman factor in most reports has also been announced as an effective factor in accidents. Various methods have been proposed for controlling and warning drivers in order to prevent violations. The methods can be classified into traditional and modern categories. Nowadays applying intelligent transportation systems are so popular, but deciding about their application is done often based on personal judgments. However, these systems have various benefits and disadvantages and each one must be applied in the proper case. In this paper we want to compare 4 scenarios containing 1- No control 2- Controlling by police 3- Applying cameras and 4- Intelligent transportation systems in the road. These scenarios are different in application, cost of application, precision and proportionality to the road classification. Here, a model for decision making based on 7 criteria and by Fuzzy Inference System would be developed. Then the proposed model would be tested for a case study in Kerman County for 5 rural roads. Results indicate that the proposed model can be a helpful method in order to decide about selecting the proper scenario to control the rural roads based on technical and economic criteria.

The main purpose of this paper is to investigate the effect of capacity on queue parameters, in order to improve the efficiency of the road by creating a proper change in the capacity of the highway and reducing the delay caused by queuing. As a system sends a parameter out of the output at a specified time interval, in other words, the system does not have the ability to service the entire input request, the queue is created and the vehicles must wait for service. In traffic queues, in addition to reducing the quality of the trip, Undesirable effect on traffic flow safety is also pronounced. The demand for the use of the road is variable throughout the day, and unfortunately there is no possibility of storing supplies in transportation. Consequently, in many periods of time, it is not possible to use capacity. It is possible to reduce the delay in the roadway by changing the capacity at different intervals. This involves sensitivity analysis on the effect of the capacity on the delay parameters. In this study, a definite queuing model is presented that can estimate the effect of changes in freeway bottleneck capacity on user delays. The formulation of the general problem is based on the definite queue theory and is solved by numerical analysis. A sample problem is investigated using a computer program. Finally, a sensitive analysis has been made on capacity changes. The results indicate that queue performance indicators improve with increasing capacity. By analyzing each case and performing such a sensitive analysis along with applying a suitable policy that changes the capacity of the way, the delay can be reduced and the route efficiency improved. Moreover, the proposed method helps to improve understanding the failure phenomenon in the upstream of traffic jams and large queuing models, and the sensitive analysis can be used to control traffic management. Using the proposed numerical method for each log entry and exit rate, one can calculate queuing parameters in each system (even non-traffic) and calculate the appropriate capacity with expected latency parameters.

Roads as the most extensive infrastructures are great assets in each country. Pavement rehabilitation and maintenance (R&M) based on the current budget is one of the most important issues to have proper roads. Two subjects are important in pavement management system, which are deciding about the proper alternatives for R&M and time to perform it. This paper aims to develop a model based on artificial intelligence to propose an algorithm for deciding about the proper option for pavement R&B and also the priority of its performance. For this purpose fuzzy inference system (FIS) would be applied. The present condition of the pavement based on the distresses, roughness and structural strength would be considered as the model inputs. Also the outputs are the proper option for R&B and priority of performance. To validate the model the real data of Fars County which relates to a PMS project would be applied. Results indicate that there is a good correlation between the results of the proposed model and real data.

Traffic safety evaluation based on traffic conflict is a new method noticeable for traffic safety researchers. Traffic conflict is an observable interaction between two or more vehicles and can be calculated in various methods. In this paper, time-to-collision (TTC) as a microscopic traffic safety index is used in traffic conflict technique. Microscopic traffic data of I-80 freeway in NGSIM project has been used for analytical calculations. For different groups of traffic, parameters such as headway, spacing, leading and following vehicle velocities and leading and following vehicle types, TTCs have been calculated and then compared. Results indicate that traffic safety in lane 1, as HOV lane, is the highest. Also, vehicle following heavy vehicles have the smallest TTCs in percentage. It seems that traffic conflict technique makes new opportunities for traffic safety engineers to evaluate freeway safety without the need for freeway crash data.

A large number of traffic accidents are due to the human errors. Advanced driver assistance systems can reduce human errors and thereby the number of accidents. An important issue in developing such systems is the use of a proper warning strategy, which warns the driver when the situation is critical and immediate reaction is required. Although time-to-collision (TTC) has been recently suggested and used, as a cue for decision-making in ADAS, but this indicator is unable to detect potential rear-end collisions. On the other hand, short time headway (THDW) during carfollowing period indicates that there is a high potential risk of collision, as soon as the lead vehicle starts braking. In this paper, a new safety indicator, urgent deceleration index (UDI), is developed. UDI can detect critical situations a few seconds sooner than TTC giving the driver more time for reaction planning, and hence providing a better margin of safety. To compare the sufficiency of UDI, TTC and THDW, as safety indicators, percent time spent unsafe, during following period is computed per lane using the detailed data gathered in the NGSIM project for I-80 freeway. Results indicate that the percentage frequency of time in rear-end conflict is 3, 63 and 76 percent, based on TTC, UDI and THDW, respectively. Also, the average correlation coefficient of critical situations based on UDI and THDW is about 0.644. In all, results indicate that UDI is more capable than TTC in detecting rear-end conflict.