پژوهشنامه حمل و نقل
سال بیست و دوم شماره 1 (پیاپی 82، بهار 1404)

The use of motorcycles as a common mode of transportation is prevalent in some developing countries, including Iran. This trend is particularly driven by advantages such as lower ownership costs compared to cars and the increasing availability of motorcycle delivery services in major cities. However, motorcycle accidents can result in fatalities and serious injuries. Research indicates that the most effective way to protect motorcycle riders is through the use of standard helmets. The lack of helmet use is recognized as a significant challenge in the realms of health and transportation. This study delves into the existing literature and incorporates expert opinions to compile a list of the most critical factors influencing the use of helmets. Subsequently, the factors affecting helmet use are ranked using the best-worst method. The findings revealed that socio-economic variables, such as age, significantly influence the relative importance of each factor. Heat was identified as the most significant factor, while embarrassment was deemed the least important. Additionally, a clear lack of enforcement of laws by the police, leading to non-compliance among motorcyclists, was observed. Finally, based on the obtained results, strategies and recommendations are proposed to encourage the use of safety helmets.

Rapid urban growth has led to an increase in travel demand and private vehicle ownership, and the existing infrastructure has not been able to match the effects of demand and traffic congestion. As traffic density increases, passenger delays increase and network reliability decreases. In the analysis of transportation systems, estimation and analysis of travel demand have particular importance to achieve an efficient and effective transportation system. Estimating and correcting demand matrices is very important, and more reliable and practical results can be achieved by using more accuracy and details in their estimation. In the problem of matrix estimation and modification, especially in dense networks, the accuracy of models significantly increases by using simulation models. In this research, the goal is to optimize the time distribution coefficients of the demand matrix in 15-minute steps for the entire study period. A bi-level approach has been used to estimate the travel demand matrix from previous matrices, which are the static matrices of comprehensive studies. At the upper level, it produces coefficients by Gaussian mixture models and uses Teaching–Learning-Based Optimization algorithm to optimize. The objective function is to minimize the error through the RMSE value. Unused stations are used to validate the model, and the detection coefficient, R^2, in all intervals and stations is 0.91 and in the 3 peak periods, the value is 0.88, according to the optimization approach using previous matrices, to change the time distribution of trips (trips start), the results show the proper functioning of the presented bi-level process.

Travel production in transportation management has led to extensive studies for different travel purposes. Trips due to the daily activities of citizens are divided with the purposes of work, shopping, education, etc., according to the studies conducted in Tehran, 12% of trips are shopping purposes, which after the trip with the purpose of work. Therefore, by identifying factors affecting the creation of shopping trips, we can play a significant role in reducing transportation cost, reducing pollutant emissions, increasing user satisfaction, and contributing to the growth of sustainable development. In this research, considering the frequency of data in areas 2 and 5 of Tehran metropolis and calculations based on Cochran's formula, 1500 questionnaires were provided to the people of these areas. Finally, 1,000 questionnaires were collected from users in the field of e-commerce who had successful orders in online and offline services in the last 20 days of 2021. The results of the descriptive statistics of the interviewees showed that the largest share of people in the statistical population were single men in the age range of 18-35 years without owning a car and having a bachelor's degree with an income level of 10-15. Also in this paper we used the indicators of age, gender, marital status, car ownership, shopping cost, shopping time, product price, household income, employment status, and education level. It has been used as indicators affecting the type of shopping trip. In the next step, the results were evaluated and the travel type estimated. In order to compare the proposed method, MLP neural network, DT and KNN algorithms were used. The results showed that the deep model has the best performance with an accuracy of 95.63%. After that, there are neural network with 90.12% accuracy, DT with 86.49% accuracy and KNN with 80.16% accuracy.

According to previous studies and investigations, various parameters influence the moisture sensitivity and stripping of asphalt mixtures. These parameters are associated with the aggregate, asphalt, mixture design and construction, environmental conditions, and traffic. The primary objective of the present research is to evaluate stripping under different environmental and traffic conditions and to assess its counter effect on the permanent deformation of asphalt mixtures. For this study, the dynamic creep test was conducted on both conditioned and unconditioned dense-graded mixtures to compare their behaviors in the presence and absence of moisture. In this method, an axial dynamic load was applied to the saturated samples to model the dynamic entry and exit of water into their pores. The theory posits that water penetrates the interface between the aggregate and bitumen, leading to poor adhesion and reduced cohesion within the bitumen structure. This phenomenon, known as stripping, accelerates the failure rate of the asphalt mixture, resulting in permanent deformation of specimens exposed to moisture. Through the examination of various temperature and loading combinations, it was concluded that at high temperatures, an increase in frequency results in a decrease in stripping potential, whereas at low temperatures, an increase in frequency leads to an increase in stripping potential. Analyzing the mechanical characteristics of the samples revealed that the ratio of creep modulus to resilient modulus serves as an appropriate criterion for evaluating permanent deformation in the samples. This ratio allows for the investigation of stripping potential and moisture effects at elevated temperatures. Furthermore, it was found that at high temperatures, asphalt mixtures are more sensitive to temperature changes than to moisture content. A comparison of the modulus of resilience between dry and saturated samples indicated that the reduction rate of the resilience modulus in wet samples is significantly higher than that in dry samples.

In urban streets, severe traffic crashes mostly relate to vulnerable users such as pedestrians, and cyclists. Because of not being protected like vehicles for their occupants, the severity of such crashes is high. Now, in the world sustainable transportation (walking and bicycle) is highlighted, thus it is important to regard the safety of this group of users more. For this purpose, it is necessary to gain an insight from previous crashes of vulnerable users. Therefore it is possible to propose countermeasures to reduce the crashes and prevent future incidents. In this paper, association rules s used as a machine learning method to find the frequent patterns and rules in the crashes of pedestrians, and cyclists. Tehran (capital of Iran) as a city with high frequency of fatal crashes in the period of 2020 to 2021 is selected as the case study. The results indicated that most fatal crashes among vulnerable road users relate to pedestrians and motorcycles (43 and 42 percent). Most of those who died in pedestrian crashes were foreigners, homeless, and municipal workers (support 0.926). Most of the injuries related to the crash of vehicles with motorcycles (support 0.661). In injury crashes of motorcycles, road and environment factors did not have a great role. Nevertheless, human factor in the form of non-necessary agility and acceleration was an effective variable (support 0.661). Engineering, education, regulation and enforcement can be the most effective countermeasures to reduce the severe crashes among vulnerable road users in Tehran.

In location-arc routing problems, unlike the well-known locating-routing problems, demand is on the arc and using deadheading arcs is permitted. Few studies have focused on this issue. In this research, a three-objective complex linear mathematical model for the multi-period location-arc routing problem under uncertainty with the time window is presented. The objectives of the model are included in the minimization of cost, cash-in-transit, and vehicle waiting time. The time window, minimizing cash-in-transit, and definition threshold for routes risk based on safety indicators, reduces the transportation risk indirectly. The proposed model is based on Bertsimas and Sim model and the ε-constraint method is used to solve 22 standard problems based on the proposed model. In addition, the robust model is validated with the realization model. The results show that the robust model versus deterministic model has better performs at higher conservatism levels and increases the uncertainty at each level of conservatism leading to higher costs.

The network design problem, functioning as a decision-making framework, embodies a policy-based orientation. This problem finds its conceptual footing in the realm of cost optimization, with a key emphasis on the travel time-related expenditures. However, the scope of costs in the network design problem extends beyond travel time. This study undertakes a meticulous exploration of the ancillary costs stemming from safety shortcomings and accidents and introduces the novel concept of the Safe Network Design Problem (SNDP). The investigation of this issue was carried out using the Sioux Falls network as a case study in LeBlanc's model, shedding light on the optimal network improvement strategy. The LeBlanc problem, comprising a selection among five projects within a budget constraint, resulted in the optimal scenario of 10110, indicating the execution of the first, third, and fourth projects. The introduction of safety as a key factor in the problem, particularly in the context of LeBlanc's example, yields intriguing results. In this scenario, the solution shifts to 01110. The combined approach that encompasses travel time costs and accidents, coupled with a comprehensive sensitivity analysis that accounts for accident severity and weighting, engenders a paradigm shift in the solution to the problem.

In recent years, much research has been conducted on using bio-renewable materials. For instance, agricultural residues are used as a bitumen modifier in asphalt pavements. Burning biomass to provide energy or to remove it is always associated with waste production in the form of ash. Ash disposal can become an environmental crisis over time. These natural materials exhibit outstanding characteristics for enhancing bitumen, while being cost-effective and environmentally friendly. The present research investigates the effect of adding barley stalk ash (BSA) on the mechanical properties of asphalt mixture. To this end, bitumen was mixed with 5%, 10%, 15%, and 20% modifier. After examining the chemical properties and morphology of bitumen and ash, conventional asphalt binder tests, multiple stress creep recovery test (MSCR), Marshall Resistance, repeated load axial test (RLA) at high temperatures, and semi-circular bend test at intermediates and low temperatures were conducted. The results showed that BSA reduces the degree of penetration and plasticity and increases the softening point and hardness of bitumen. MSCR results also showed that modified bitumen experiences fewer permanent deformations at high temperatures. Furthermore, the repeated axial load test results revealed that BSA improves the high-temperature performance of the asphalt mixture and makes it more suitable for use in tropical regions. The results of the semi-circular bending test showed that barley stalk ash lowers the fatigue resistance of asphalt mixture at intermediate temperatures and cracking at low temperatures.

Accidents in the transportation system, especially vehicles with natural gas fuel during refueling, are a serious problem. Accidents, especially the explosion of the tank of these cars, have caused concern at different levels of society. With a dynamic view of the mentioned issue in Iran and important surveys such as; the number of accidents, deaths and annual damages caused by it can be more understood. In general, CNG incidents have various and a multidimensional or multifaceted issue has its own complexities. The purpose of this study is to design a cause-cause model of natural gas fueled vehicle accidents in Iran with system dynamics. First, after stating the cause of the issue over time, according to the background and interviews conducted, four main categories as factors or important and influential factors are determined based on the factors that form each of the changes, the relationships of the causes. In this regard, the investigated system is divided into subsystems or main parts, and for each part or subsystem, a causal loop is provided. At the end of the research, the strategic policies of improving the main behaviors of the system to reduce accidents in this area of the transportation system are proposed to activate or set up a trustee organization called the CNG industry leader organization.

It is very common to modify rubber bitumen with warm asphalt waxes such as Sasobit, also the use of cellulose fibers for tensile strength and better strain energy absorption power than other additives has attracted the attention of researchers. This research used cellulose fibers coated with Fishertraps wax (Sasobit) in proportions of 1,3, and 5% as an additive in bitumen with 15% rubber powder. So that modified bitumen can be used to improve strain energy absorption and bitumen shedding in asphalt mixtures with open-graded and low density or to improve the index of resistance against rutting in higher-density mixtures. The results of the investigation of rheological properties indicate that the addition of the mentioned fibers to rubber bitumen, in addition to reducing stiffness, increases its resistance to rutting in both unaged and short-term aged samples. The results show that the upper-performance limit of unaged pure bitumen 22-64, with the presence of 15% rubber powder and improved fibers, for example, adding 5% fibers to bitumen with 15% rubber powder increases the upper limit of bitumen performance up to 94 degrees Celsius. It increases the degree. Also, the performance of short-term aged and non-aged rubber bitumen is similar, but short-term aging of bitumen reduces the effect of fibers. Also, the addition of fibers causes an increase in the modulus of complexity in the middle temperature and better performance in returning from the multi-stress level in the fracture temperature. This better performance makes the rubber powder bitumen-containing fibers perform better against rutting failure at the highest operating temperature.

In this study, evaluation of structural systems for pedestrian steel bridges will be considered. In this research, three common types of trusses, cable and arch bridges were examined and their capacity against static, dynamic and vibration loads was calculated with SAP software. The weight of the bridges is then compared to select the most economical option. The overall results showed that the structures of the conventional pedestrian bridges have sufficient capacity for static, dynamic and vibratory loads. In comparison, it was found that due to the length of the span and the constant height of the bridge, the truss system weighs less than other systems. The weight of the arch bridge was 1.7 times the weight of the truss bridge and the weight of the cable bridge was 2.1 times the weight of the truss bridge. Also, the frequency of the first mode of the cable bridge was 33% higher than the truss bridge and the frequency of the first mode of the arched bridge was 24% lower than the first frequency of the truss bridge. Comparing the common types of bridges, it was found that truss systems have lower cost and stability index than other systems. In the system of cable structures, the vibrations of the pedestrian bridge are further reduced. In a truss system with the same load, the total weight and therefore the cost of the bridge is minimal. Based on the results of this research, the most appropriate system can be identified and used according to the type of load applied to the bridge structure.

The study of the drivers of economic growth in transportation is of great importance. One of the factors affecting the economic growth of transportation is macroeconomic shocks. Given the importance and role of macroeconomic shocks in the economic growth of the transportation sector, this study examines the effect of macroeconomic shocks on the transportation sector. For this purpose, the additional variable test method was used to examine the existence of a long-term relationship or the existence of counteraction between the variables. In the following, the short-term and long-term relationship between the value added of the transportation sector and macroeconomic shocks was examined. The results showed that there is a long-term relationship between the variables of the value added of the transportation sector, total value added, consumer price index, liquidity, and exchange rate. The results of the short-term and long-term tests related to estimating the effect of macroeconomic shocks on the transportation sector showed that unexpected positive shocks to value added, unexpected negative shocks to the consumer price index, unexpected positive shocks to liquidity, and unexpected negative shocks to the exchange rate have a positive effect on the value added of the transportation sector. Also, unexpected negative shocks to value added, unexpected positive shocks to the consumer price index, unexpected negative shocks to liquidity, and unexpected positive shocks to the exchange rate have a negative effect on the value added of the transportation sector. In addition, the results of the test of the hypothesis of asymmetry of positive and negative shocks of variables showed that the variables have asymmetric effects on the value added of the transportation sector.

One of the most significant aspects of train timetable optimization is determining the arrival dwell time in the subway system. A good timetable improves the efficiency of trains and resources, reducing passenger wait times. This study provides a timetable optimization model to reduce passenger journey time. In this model, train movement between two stations is divided into three phases: accelerating, coasting, and breaking, with random numbers used for running duration in each phase due to stochastic delays in crowded stations. First, we develop a stochastic integer programming model that includes headway and dwell time. The Scenario-Based Uncertain Programming Approach is then utilized to simplify. Finally, numerical examples are provided for Tehran's metro system. The model's optimal solution is determined via a genetic algorithm. The results demonstrate that the model can lower the passenger waiting time when compared to the current timetable.

The aim of this study was to investigate the impact of new technologies in the field of logistics development and post-shipment transportation in the southern ports of Iran (Case study of Imam Khomeini port). In this study, it is of applied type and quantitative research method has been used. Library method and questionnaire were used to collect data. Also, a sample of 65 people was used to collect data. On the other hand, the method of structural equations has been used to test the hypotheses with the help of LISREL software. The results showed that all hypotheses are confirmed and all variables have a positive and significant relationship.

Traffic volume represents one of the most critical parameters in transportation analyses, influencing various aspects such as design, planning, policy-making, and model development. During holidays, characterized by a peak in out-of-town trips, significant changes occur in the traffic volume pattern across the country's rural road network. In Iran, holidays follow different patterns based on lunar and solar occasions, resulting in notable fluctuations in traffic volume on suburban roads. This study aims to investigate the pattern of changes in traffic volume during holidays and to provide a model for predicting traffic volume during such periods. For this purpose, an artificial neural network model was developed to forecast traffic volume on the suburban roads of the country's provinces, with sensitivity to holidays. The models are designed on a daily time scale and are developed separately for each province. The evaluation metric used for the models is the mean absolute percentage error. The average of this metric across all models was 9.28%, with an example from Mazandaran province showing 8.89%. The results of this research aid policymakers in predicting traffic volume in the transportation networks of the country at the province-day level during different times of the year and enable them to take proactive measures to address traffic congestion during special occasions and holidays. Moreover, the developed model allows for the examination of the effect of different holiday scenarios on the country's traffic volume.

By providing a transition between two straight road segments, horizontal curves play a significant role in the interurban road network. According to statistics, these locations are characterized by high incident and accident rates. The aim of this study is to introduce a geospatial based method for extracting the geometric parameters of horizontal curves on rural two-lane roads using Geospatial Information Systems (GIS) and satellite imagery, as well as identifying accident-prone curves through the utilization of spatial clustering functions and and investigating factors influencing the injury severity of accidents on the horizontal curves of rural two-lane roads based on familiar and unfamiliar drivers, using the Random Forest algorithm. The implementation results of the proposed method on the studied corridor (Rasht - Anzali) indicated that the most significant contributing factors to severity of crashes on accident-prone horizontal curves, which were identified using the kernel density estimation method were collision type and road light condition with coefficients of 0.51 and 0.41 for accidents involving familiar drivers and the factors of daily traffic volume and collision type with coefficients of 0.53 and 0.51 for accidents involving unfamiliar drivers. Factors such as daily traffic volume and driver age for familiar drivers, road surface conditions and road light condition for unfamiliar drivers were recognized as additional influential factors on the severity of crashes of studied corridor's horizontal curves.

Bridges are pivotal infrastructures in each country, and the maintenance and damage detection in these structures play constructive roles in enhancing road safety and service life. The indirect bridge health monitoring methods, in which sensors are installed on the moving vehicle, become considerably rapid and efficient since there is no need for sensor installation on the bridge elements. However, the responses recorded by the vehicle comprise combined responses of moving vehicle and bridge accelerations, causing further analyses and calculations for decomposing accelerations. The effects of dynamic parameters on acceleration at the vehicle-bridge contact points are considerably lower than that of the acceleration of moving vehicle’s components; therefore, the bridge health condition can be more easily evaluated according to the acceleration at contact points compared to the acceleration recorded by moving vehicle’s components. In this study, the Fourier transform of the vertical acceleration at the contact point of the moving vehicle and bridge was used to assess the bridge health condition. First, the vertical acceleration at the contact point was calculated using the backward procedure for moving vehicle with different speeds crossing over the undamaged bridge. Second, the Fourier transform was applied to acceleration results. Finally, the Fourier transform of the acceleration at the contact point for different speeds of the moving vehicle was predicted and compared against the corresponding accelerations of the undamaged bridge to assess the accuracy of the proposed method in determining damage levels in the bridge. The results indicate that the bridge damage level can be properly determined using the Fourier transform of the responses at the contact point of the bridge and moving vehicle at different speeds. Moreover, the suggested approach leads to accurate predictions of damage levels in the bridge even in the case of noisy data and substantial damage on the bridge pavement surface

Today, there are many social and environmental pressures to limit the emission of greenhouse gases, especially in the transportation sector. This paper investigates the two-echelon capacitated vehicle routing problem considering the simultaneous pickup and delivery approach. A multi-objective model is developed in order to minimize costs, environmental damage factors, and travel time balance of the transportation fleet to achieve sustainability. To solve this problem, two meta-heuristic algorithms including NSGA II and MOPSO were proposed. To evaluate the performance of the two proposed meta-heuristic algorithms, 15 instance problems were randomly generated. The results of the sample problems were compared in 8 indicators, such as the average of the first to third objective functions, the Number of Pareto Front (NFP), the Maximum Spread Index (MSI), the Spacing Metric (SM), the Mean Ideal Distance (MID), and the CPU run-time (CPU-time) values. The results showed that the NSGA II algorithm has achieved better results than the MOPSO algorithm in the Number of Pareto Front (NFP), the Maximum Spread Index (MSI) and the Spacing Metric (SM). While the MOPSO algorithm has been more efficient than the NSGA II algorithm in achieving the averages of the first to third objective functions, the Mean Ideal Distance (MID) and the CPU run-time (CPU-time). Finally, the MOPSO algorithm has chosen as the best solution method by using the TOPSIS method and obtaining a weight of 0.7061. This article can help managers to benefit by reducing operating costs, reducing harmful environmental effects and the need to pay attention to social criteria in order to gain a competitive advantage throughout the logistics network.

Direct and circular flows in the intersection by creating interference between vehicles and pedestrians have caused these points to have a high potential for pedestrian Crashes. Safety analysis of pedestrians at intersections with different methods such as statistical modeling is of great importance. The aim of the current research is to develop a statistical model for predicting pedestrian Crashes in urban intersections with stop control based on the most important factors affecting pedestrian Crashes. To achieve this goal, the Safety data of intersections with stop control in the urban environments of Ilam province have been used as a database for modeling. The modeling method in this research was negative binomial regression modeling. Based on previous studies, a complete list of influential variables was prepared and their coefficients were determined with the help of SPSS software, and then significant variables were selected for modeling. Significant variables included the presence of hump speed before the intersection, the presence of parking on the street, the number of left turn lanes, the volume of pedestrians and ADT. The performance evaluation results, verification and validation of the model were done with the help of SPSS software. The results of the statistical tests showed the full verification of the model.

There are many factors in the use of cars or citizens' reluctance to use a certain type of public transportation, but this paper want to budget public transportation and guide citizens to use public transportation. For this purpose, this research presents a dynamic model for the public transportation system of Mashhad using regression equations in order to achieve sustainable development. In this research, using the data related to public transportation and the opinion of experts in this field, the circular causal model of public transportation in Mashhad city was designed and simulated using Vansim software. After checking and adjusting the circular causal model, the flow diagram of the model was drawn. Then, the validity of model was formulated and tested. According to the results of the model, if the bus be free, the bus allocates about 18.06 percent of the trips, and if three thousand tomans are considered, this ratio decreases to about 11.22. It means every 1000 tomans increase in the fare rate, while keeping constant for the fare other modes, we have a 2.28% decrease in the share of bus from intra-city trips. Finally, according to the opinion of experts, 4 scenarios were presented, the results of each of which were analyzed on the development of the sustainability of the public transportation system in Mashhad.

The main objective of this study is to investigate and determine the share of the three factors of human, vehicle and road in crashes in Isfahan province. For this purpose, the causes of crashes and the causes of injuries resulting from crashes that lead to injury or death of drivers and passengers have been studied and the share of the three factors in this case have been calculated. Also, in this study, the difference between the nature before and during the crash (from the Haddon matrix) has been examined; that the share of human factor in 97.5 percent, road factor in 36.9 percent and vehicle factor in 8.9 percent of crashes (cause of crashes) in Isfahan province play a role; also, in-depth analyses showed that human factors such as lack of attention to the front, high speed and a set of inappropriate behaviors of drivers account for more than 85 percent of all crashes and if these factors are eliminated, we expect that 85 percent of crashes will not occur; also, in general, by eliminating each of the factors affecting the crash, it is expected that no crash will occur. In the following, the share of vehicle, human and road factors in injuries during the crash were calculated to be 83.4, 45.2 and 30.6 percent, respectively, which shows that vehicles play the most role in injuring drivers and passengers of vehicles during the crash and if each of these factors are eliminated in crashes, we expect that the severity of the crash will decrease. Due to the lack of volume of the database, in the future, the use of more sources, including insurance and repair shop reports, is suggested for more accurate analysis of the share of factors.

This research explores the use of machine learning to predict public transportation demand in Mashhad, Iran. Predicting demand is crucial for optimizing operational plans and ensuring efficient service delivery. The complex nature of travel patterns necessitates a model that can account for both spatial (geographic) and temporal (time-based) factors. The developed model utilizes various spatial and temporal data points, offering flexibility and adaptability. The study compares four models built with different datasets. The research employs Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to identify distinct travel patterns within each time period across the city's 253 traffic zones. Additionally, the random forest algorithm is used to identify and select the features that affect the demand variable. The most effective model leverages spatial data on an annual scale, resulting in highly accurate predictions (training error: 0.331, testing error: 1.095). This model allows planners to estimate public transportation demand across different traffic zones, both daily and annually, in response to potential changes in urban land use.

The location problems of repair centers in the railway network have been developed to quickly and cheaply solve the failure of signaling equipment. The signaling system in railway stations guarantees the safety and smooth running of passenger and freight trains. Failure of signaling equipment is inevitable, which leads to disruption of train movement and dissatisfaction in the customer community (passengers and goods owners). Hence, it is important to quickly fix equipment failure in priority stations. The signaling equipment of each station includes interlocking, track circuit, machine needle, signal light, and axle counter. This equipment has an average monthly failure frequency, average repair time, and different thresholds for fixing the failure. This study investigates the disturbance caused by the failure of signaling equipment with the importance of stations and the average frequency of equipment failure. Importance indices were extracted to prioritize the stations. The weighting of the indicators was done using the fuzzy hierarchical analysis method. Then, the stations were prioritized based on their importance using the TOPSIS multi-criteria decision-making method. In the following, a multi-objective optimization model of location and allocation is developed to maximize the coverage of the entire maintenance network and reduce the time and cost of troubleshooting. To solve the model, the method of genetic algorithm of non-dominant sorting is used. The data of signaling equipment failure in Mashhad-Tehran railway stations is analyzed as a case study and the importance of stations and demand points is calculated. Comparing the output results with the current status of the repair centers in this study shows that the new location of the repair centers and how the stations are allocated improves the coverage number of the priority stations.

The railway transportation system can be considered as a source for renewable energy production, whereby the use of thermoelectric generators in locomotive exhausts and rail structures, installation of solar panels and small wind turbines with regular geometric specifications on train roofs or in the vicinity of rail lines, and connection of piezoelectric absorbers of vibration and sound on rail lines and rail vehicles have had a significant impact on the production of renewable energies from railways. The results have shown that the maximum energy output produced by thermoelectric generators is 20 milliwatts per square millimeter. In simultaneous application, solar cells installed with an equivalent area of 34 square meters per wagon on the roof of a 15-wagon train and horizontal-axis wind turbines with an equivalent area of 0.785 square meters per wagon have produced a maximum energy output of 311 kilowatts per wagon, which is nearly 9 times the power output of wind turbines installed on the train roof. According to the results, the simultaneous use of wind turbines and solar cells is preferred in terms of energy production capacity.

Nowadays, the optimization of energy consumption in public transportation systems is a serious issue. Since a large part of energy in transportation systems is consumed by subways, a new approach has been proposed for optimal control of a train to reduce energy consumption. The proposed model is based on the Reinforcement Learning algorithm. It is assumed that a train moves between two stations along a line with non-constant gradient, curve, and speed limits. Moreover, the train should complete its journey within a given time interval. The Reinforcement Learning of States, Actions, and Rewards are based on the selected Actions. In the proposed method, the train States are the velocity and position of the train, and the Action is acceleration or coasting motion. Unlike the former techniques, most stages of optimization in this method are offline and implemented only once for any route. Following the formation of the reward matrix, we could use this method in an online form and then the speed profile could be produced at a minimum time. The simulations of the proposed method are implemented in MATLAB and finally compared with those of the Genetic Algorithm.

In the supply chain, factors such as political issues, demand fluctuations, technological changes, financial instability, and natural disasters lead to increased uncertainty and the emergence of various risks. One of the most important and complex stages of logistical support in organizations, particularly in military and law enforcement organizations, is the forecasting process. Due to its vital role in improving logistical functions, this process highlights the necessity of designing and establishing effective forecasting systems. This research examines the importance of forecasting and estimation in logistical functions and analyzes the requirements that must be followed when designing a forecasting system for the country’s 10-year horizon. The research method is a questionnaire survey evaluated by 120 participants. The case study of this research is the Amirabad Special Economic Zone (Mazandaran), from which all necessary information and data have been collected. This region plays a crucial role in trade and economic development in northern Iran, with various investments in logistics and transportation.

The purpose of this research is to evaluate the mechanical performance and durability of Roller Compacted Concrete (RCC) with fibers as an alternative to asphaltic pavements in industrial areas with heavy traffic loading. RCC is a hard and relatively dry mixture of aggregate (maximum nominal size 19 mm), cement and water which is spread by conventional asphalt pavement finishers and compacted by a vibrating roller. After completing the hydration reactions, the RCC has the same appearance as the concrete with zero slump. In this research the compressive and bending strength, durability and cycles of freezing and thawing were tested. The results showed that the performance of RCC containing fibers is better than RCC without fibers. Also, the resistance of RCC against fire is much higher than asphaltic mixture. The results showed that RCC pavement is suitable for roads with heavy vehicle traffic and low speed due to its engineering and mechanical characteristics. The main purpose of this research is to investigate the engineering and mechanical properties of RCC as an alternative to asphaltic pavement in industrial areas with heavy traffic loading. Also, by conducting a test, the fire resistance of RCC and asphaltic pavements was evaluated.

Dam building industry is considered as one of the first and most complex part of civil affairs and therefore it has always been one of the topics under the scrutiny of different societies. High dams have weaknesses against shear stresses. According to the results of the dynamic analysis, it is concluded that the seismic shocks caused by the earthquake cause the expansion of many large and small cracks in the upper areas of the core. Due to the fact that dams are usually built in mountainous areas, the existence of faults near and far from the axis of the dam is inevitable. Investigating the seismic potential of each fault in the far or near distance and the effect of the resulting earthquake on the stability of the dam is one of the main criteria for locating the axis of the dam. Therefore, the detailed investigation of the impact of earthquakes in far and near fields on the stability of dams is of special importance in dam design and stability evaluation of existing dams. Modeling in this research has been done using Geostudio (2018) finite element software. In order to perform dynamic analysis, the records of earthquakes in Borujerd, Khorramabad and Sarpul Zahab of Kermanshah have been used. According to the results of the quasi-static analysis of the Meruk dam, it was concluded that the increase in the core thickness has caused a decrease in the stability assurance factor of the dam; Also, the results of the dynamic analysis of the studied dam showed that the maximum settlement that occurred in the dam without increasing the core width under the effect of Borujerd, Khorramabad and Sarpol Zahab earthquakes was 0.18 meters, 2.35 meters, and 0.04 meters, respectively.

The appearance of new infectious diseases has become a serious global problem. Public transportation networks are an important factor in the rapid spread of such diseases. In this study, investigation of the spread of COVID-19 epidemic virus is done using dynamic SIR (Susceptible – Infected – Recovered) mathematical model with MATLAB. In addition, we studied accurate statistics of infected cases and deaths due to COVID-19 for the time intervals in various transportations in Iran, for instance, the transportation via rail, air and road transportation in specific time periods from Tehran city to Mashhad city also Tehran to Shiraz. Average of the Reproductive number (R0) COVID-19 in Farvardin and Ordibehesht months of 1399 also 1400 for Tehran to Mashhad is about 2.10 for rail transportation, 2.11 for air transportation and 2.16 for road transportation also for Tehran to Shiraz is about 2.09 for rail transportation, 2.10 for air transportation and 2.12 for road transportation. In each transportation type, the modelling is done and the comparison based on the statistics of deaths of passengers from Tehran to Mashhad and Shiraz shows the risks of outbreak and infection in road transportation are higher than air and rail transportation and the risks of outbreaks and infection in air transportation are higher than rail transportation. This results indicate that rail transport in safer in terms of the prevalence of COVID-19 and the contamination from air and road transportation.

This research is considered by considering the analysis of the road network after the crisis in Gorgan according to various influencing factors and access criteria for network analysis, after determining the effective criteria according to the opinions of experts using a questionnaire collected from 20 experts and After examining the reliability of the questionnaire by Cronbach's alpha method, factor analysis was performed to determine the important effective factors in providing services to people after the crisis. - Paying attention to the channels, observing the distance and privacy from rivers and streams, 3- Access to the communication network with the function of 2nd degree arterial roads, etc., were prioritized, in which the special effect of access and transportation networks. The result was. Furthermore, considering that the research method is spatial and network analysis in GIS environment, all effective factors in post-crisis conditions, travel time, route performance, distance related to Network criteria in Gorgan were analyzed in GIS environment. Finally, according to the knowledge of different layers in the network in the current situation and also access to vehicle-centered relief points, the situation of the road network was analyzed after the modern crisis. In the final part of the research analysis, in order to control the proper access of different relief areas of Gorgan to 11 relief bases using network analysis tools and its criteria such as travel time, road performance, distance, etc., the functional radius of these bases is analyzed. The output of the network analysis showed that all 11 stations are in good condition, with the difference that in some parts, due to the lack of service level in the city road network, it is necessary to locate several fire stations and the Red Crescent.