فصلنامه مهندسی حمل و نقل
سال سیزدهم شماره 4 (پیاپی 53، تابستان 1401)

Given the competitive advantage of transportation systems، increasing customer satisfaction is an important factor to attract them to use these systems.  On the other hand، a useful way to increase the attractiveness of rail travel is to build new stations، although locating stations on rail networks is a complex and time-consuming task. Therefore، a mathematical model can be a very useful way to find the best location of constructing the stations. On the other hand، our real-world has uncertainty and locating stations in fuzzy environments by considering the uncertainty will increase the reliability of the results. In this study، we modeled the location of stations in fuzzy environment by considering the uncertainty of data by using the optimization robust model with a realistic approach. A case study has been applied on the real data of Mashhad city and the results of solving the model in fuzzy environment were compared with those in crisp situations، which indicates a significant improvement.

Failure detection of complex industrial equipment such as locomotives is a challenging tack in maintenance process. Failure to timely diagnose the cause of locomotive failure, in addition to reducing access time, will cause disruption to the rail network, increase excess train stops and other adverse events. With the advances made in recent years, the amount of data stored in new locomotives is increasing. In different ways, the knowledge contained in the data can be extracted and used to increase the productivity of the organization. By pattern analysis of failure detection of locomotives, the cause of many failures can be discovered and repair time can be reduced. Among the available methods, data mining techniques can be mentioned. The present study uses data mining and Apriori algorithm to discover meaningful rules from the data in locomotives, with the aim of improving the efficiency of the failure detection process. The result of this study is the discovery of 20 frequent occurrence in passenger locomotives, 18 two-component laws and 2 three-component laws, and its key achievement is the improvement of locomotives maintenance in a short period of time in the Railway of the northeastern region of Iran. Use of the discovered laws in the future can eliminate many out-of-schedule stops of passenger trains and also significantly reduce the cost of locomotive maintenance. It is estimated that using only one of the mentioned laws alone can reduce the cost of Iran's railways by about ten billion rials per year.

Predicting the safety of the rail transport system is a fundamental problem in modeling and managing rail transport. In this paper, a profound learning-based safety prediction model for rail transport safety is proposed. Data were collected from the subway operating company and decisions were made on the factors of the forecasting model. These factors were used as DBN input. The structure of the input data determines the number of nodes in each layer. Sample data collected include types of accident-prone events, basic train information, and company operating information. Predictive factors were selected by analyzing these collected data. The DBN was then created based on the processed data. To show the accuracy of this model, a data set (Tabriz subway) has been investigated as a case study. Experiments on the data set show good predictive performance of the present model. These results show that deep learning is useful in learning the patterns of a rail system. Prediction and reality error between 0.08 and -0.08 are introduced. This is an acceptable error and cannot cause an incorrect level of security in the system. The dense safety range, both in the current situation and in the forecast model, is at the second level, i.e. relatively safety. This range is in the range of 0.8 to 0.89. Of course, the tendency of density after the second level is towards the first level to the third level. It is noteworthy that the forecast model (at high levels) shows higher values ​​than the existing safety.

In the oscillation traffic, various factors could affect a driver’s decision to choose a different speed and decrease or increase acceleration based on different driver’s patterns of behavior. In the present study, different behavioral patterns (aggressive and cautious) is analyzed in the oscillation traffic, with regard to three mental behavior of driver (safety, comfort, and travel time). In this regard, a structured methodology was proposed to quantify the indices’ weights including a multi-objective optimization problem in which the indices were the model’s objective functions. Then, a driver’s behavior was evaluated and analyzed using a neural network. The results of evaluating the driver's behavioral performance showed that the aggressive behavioral performance in the deceleration (acceleration) phase with respect to comfort index based on changes, increasing (increasing) acceleration and safety index based on changes, a decreasing-increasing (decreasing) acceleration and travel time index based on changes is determined by decreasing (increasing) acceleration. Finding also revealed The results of evaluating the timid behavioral performance show that the timid driver behavior in the deceleration (acceleration) phase with respect to comfort index based on changes, increasing - decreasing (increasing) acceleration and safety index based on changes, increasing ( decreasing-increasing) acceleration and travel time index based on changes is determined by decreasing (increasing) acceleration.

Today, pedestrian streets are important elements in urban space that can speed up the non-motorized movement. Development of pedestrian streets is one of the basic strategies to reduce the harmful environmental consequences of motor vehicles, increase the safety of pedestrians, and improve the urban environment. Recently, City of Qom has faced up many economic, social and environmental degradation problems that need to be rehabilitated and recreated. Regarding the issues, one of the ways to increase the presence of pedestrians in cities is to select pedestrian locations appropriately that optimize the routes between the trip origin-destination. In order to facilitate this importance, identifying and prioritizing the factors affecting the choice of pedestrian location based on trip purposes in the City of Qom is vital. Thus, the goal of this study is to promote walking using the factors influencing the location of pedestrian streets. In order to reach the goal, the present study firstly employs the transport and landuse data and information of the city of Qom as a cultural and religious center of the country based on the trip attraction data in 2018. Secondly, it identifies the relationship between factors affecting location of pedestrian streets using the structural equation modeling (SEM) based partial least squares (PLS) method. Therefore, the results of PLS method revealed that factors such car ownership, household, population, income, and area have a positive effect on pilgrimage, recreational, and shopping trips. Finally, variables such as car ownership and students have the most and the least effects on trips and the choice of pedestrian location, respectively.

In this study, the strength of hot and warm asphalt mixtures containing 3% Sasobite in cold regions has been investigated using the principles of fracture mechanics. The effect of temperature and loading modes (tensile and shear) on the fracture toughness of the mixture was investigated using a semicircular samples with an edge crack under three-point bending. The coefficient of critical stress intensity was determined from the results of the fracture test and the shape coefficients were determined from finite element analysis in ABACUS software. In hot and warm asphalt mixtures containing 3% Sasobite, with decreasing temperature, the critical stress intensity coefficient increased in tensile and shear modes. The results showed that the hot asphalt mixture at temperatures of 0, -10 and -20 °C presents significant fracture resistance compared to the warm asphalt mixture in all loading modes. As the temperature decreases, the fracture toughness of both types of both asphalt mixtures increases.

This study presents a new form of the problem of location, allocation, routing and inventory in the municipal waste management system by considering three economic, social and environmental issues. In the economic sector, various types of costs related to collection Municipal waste is examined in such a way that at the same time the total system costs and the number of machines required for waste collection in the whole network are minimized. Also in the social and environmental sector, it seeks to provide a model for finding the shortest path between waste collection tanks in the city to the final collection and recycling site. The research problem is formulated in the form of a multi-objective nonlinear programming model of complex integer. Also, to control the degree of uncertainty of some parameters in the research model, the fuzzy robust method has been used. Since the problem of this research is np-hard type and also the optimal amount of variables in these problems interact with each other, the multi-objective colonial competition algorithm and multi-objective genetic algorithm have been used for the final solution. The results of solving different scenarios as well as validating the results of the proposed algorithms show that the average error of the multi-objective metaphorical competition algorithm is lower than the multi-objective genetic algorithm in solving different scenarios, which indicates the high efficiency of the algorithm for a wide range of problems. It comes in different sizes.

In this research, a new model for long-haul vehicle routing problem considering driver requirements and preventive maintenance activities is presented. The aim of this study is routing optimizaton with delays in servicing minimization and scheduling by allocating a number of drivers to the routes and paying attention to driving requirements and long-distance drivers' rest and meeting the operational limitations of the transportation system. Due to the NP-Hard nature of the problem, the hybrid meta-heuristic algorithm is proposed to solve the model in large scale. The proposed development algorithm is an adaptive large neighborhood search algorithm that is reinforced using simulated annealing algorithm and optimized routes by considering the limitations of driving requirements and by allocating one or two drivers to the route as well as considering times for preventive maintenance activities to reduce distribution costs.

This paper aims to investigate and compare the properties of highly plastic clay soil stabilized by geopolymers synthesized from various industrial residues and rice husk ash. To this end, steel slag, fly-ash and diatomite were used as the base material, and NaOH solutions of 8.68 M and Na2SiO3 solutions extracted from rice husk ash were used as an alkaline activator for stabilization. Stabilization was performed using 10, 20 and 30 percent of base materials by dry weight of soil. Samples were compacted at optimum moisture content and were cured for 7, 28 and 90 days. The results of the experiments show that the application of geopolymer as a stabilizer improve the compressive and tensile strength, increase Young's modulus and reduce the failure strain of the stabilized clay soil. The highest increase in the unconfined compressive strength (UCS) was related to stabilized clay with geopolymer synthesized using 30% of steel slag, its UCS was 25.2 times higher than the untreated clay soil. Also, the samples stabilized with geopolymer synthesized using 30% of fly-ash and 30% of diatomite showed the UCS of 4.05 and 12.72 times more than the untreated clay soil, respectively. The results of this study also showed that the stabilization of clay using geopolymer significantly increases the indirect tensile strength (ITS) of the samples, so that the samples stabilized with geopolymers based on the 30% of steel slag, 30% of fly-ash and 30% of diatomite, had an ITS of  23.14, 7.58 and 12.5 times more than untreated clay soil, respectively.

Urban transportation networks and infrastructures design, in the traditional way was only car-based and almost all urban road network design parameters were extracted solely according to vehicle specifications and convenience. With the introduction and development of sidewalks as a human- oriented infrastructure in the urban road networks, the design and even conversion of roads that were previously designed exclusively for vehicles was developed into this type of urban infrastructure. However, the conversion of parts of the urban road network into roads for the exclusive use of pedestrians, or in the other words, pedestrianization, due to some restrictions at the network level, and especially in the adjacent roads of the project, always can cause various problems. However, the using some scientific transportation methods can prevent such problems. The main aim of this study is to evaluate the effects of creating pedestrian-oriented infrastructure at the micro and macro levels in the urban network and reduce its potential negative effects in order to increase the chances of success of this type of projects. In this regard, by simulating at both macro and micro levels, the conversion of riding passages to pedestrian infrastructure was examined both at the network level and at the project level. According to the research results, pedestrianization at the network level by converting riding passage to pedestrian infrastructure will increase the amount of trip by 2% and also increase the total produced pollutants at the network level by 1.5% and reduce the total network delay by about 1%. In addition, it reduces the speed and increases the delay at the project level, which with some proposed corrective measures can balance the values of slowing down and increasing the delay in order to succeed in this type of project.