فصلنامه مهندسی حمل و نقل
سال چهاردهم شماره 3 (پیاپی 56، بهار 1402)

In railway signaling, the safety of the system refers to the protection of losses and economic losses. The connection of an interlocking system to wayside equipment such as signal, point machine, track circuit, etc. is carried out through the equipment’s control system. Since the data transmission and retrieval of wayside equipment through the system acts as a vital interface between the interlocking system and the wayside equipment; therefore, checking the status and accuracy of each function should be performed instantaneously; therefore, check of this system safety and reliability it is very important under all circumstances. Generally, these boards are made up of a controllable signal for tolerable breakdowns. Since reliability and safety calculations require field and laboratory tests, they should be done by modeling, simulating, and using numerical and analytical methods. In this paper, first, we introduce the OCS system and then the signal controller, and then introduce the reliability and quantity of the related. Then, the reliability of the signal-controller board is obtained by using a system block diagram and simulation with MATLAB software, In the next step, using hardware redundancy, a closer look at reliability is discussed; and finally, conclusions will be drawn and proposals will be made in the relevant field. The main innovation in this paper is the use of block diagrams and structural redundancy in calculating the reliability of signal-controller boards and simulation with MATLAB software.

As the share of transport in the production and emission of air pollutants is higher than other sources of emissions in cities, this makes it important for urban managers to identify these sources in detail. This paper deals with the combined effect of emission sources due to transport in the case study of part 9 and part 21 of Tehran metropolitan area. The Mehrabad Airport is considered as a volume source of emission, the passenger terminal as an area source of emission, and the roads in this region as a linear source of emission. For this study, the emission factors for Mehrabad Airport were first calculated using ICAO proposed approaches. To model the emission of pollutants in the streets and west terminals, the emission factors were first obtained using the IVE model, which was developed for Iran with the obtained driving cycle. In addition, with the emission factors obtained for the airport, terminals and routes concerned, the emission distribution model is obtained by combining the emission sources modeling system (ADMS). In total, this paper uses two combined methods of IVE-ADMS for routes and terminals, as well as the ICAO-ADMS model for airports. The results showed that SOX emissions were predominant at Mehrabad airport. Also, the emission of various pollutants in the study area and its 24-hour impact on the surrounding areas with its geographical coordinates are shown.

In the current study, the polypropylene fiber (PPF) and basalt fiber (BF) were used to enhance the shear strength of natural soil or cement stabilized soil specimens. The main objective of this research is investigating the effect of curing time, initial moisture content and PPF or BF with or without the addition of cement on the unconfined compressive strength (UCS) of clayey soil. It is found out that adding cement, PPF or BF to soil causes a remarkable increase in the UCS value. The UCS value of the PPF-reinforced specimens was observed more than that of BF-reinforced specimens. The UCS of specimens increases gradually as the cement content or curing time increases. However, the axial strain at failure for cement-stabilized specimens decreased with the increasing curing time. Furthermore, it is concluded that the increase in the UCS of combined PPF or BF and cement inclusions is much more than the increase caused by them, individually. The fiber-reinforced specimens show a more ductile behavior than the natural soil or cement-stabilized specimens. The inclusion of fibers within the cement-stabilized specimens reduces the brittleness of the response and increases the UCS and axial strain at failure, significantly. The increase in the UCS of combined fiber and cement specimens is much more than the increase caused by fiber or cement, individually.

An effective method of preventive maintenance is the use of slurry seal surface treatment. The present research is aimed at investigating the use of blast-furnace slag powder as an alternative to mixed mineral filler in slurry seal mix for assessing its performance. In this respect, we began by exploring the properties of rock materials and blast-furnace slag powder. Next, seeking to evaluate the performance of the developed slurry seal treatment, five different mixes were examined; the mixes contained the blast-furnace slag, as a replacement for mixed mineral filler, at 0, 25, 50, 75, and 100%. Sample evaluation was conducted through wet cohesion test, loaded wheel test, and wet track-abrasion test according to ASTM D3910 and ISSA A105 standard codes. The results showed that the mixes containing the blast-furnace slag as filler tended to exhibit superior performance of the slurry seal. Moreover, among the examined samples, the mix containing the slag at 10.0% could improve the cohesion by 25.0 and 33.3% after 30 and 60 min, respectively, with the bleeding level decreased by 51.9%, as compared to the control sample. Compared to the control, this sample could achieve 1.89% higher bitumen emulsion, providing for better cohesion in an appropriate amount of time.

One of the port planning problems that has been noticed in many papers and research is the berth planning problems. Berth planning includes two sub-problems; Berth Allocation Problem (BAP) and Quay Crane Assignment Problem (QCAP). This paper develops one mathematical model by integrating these two sub-problems. The berth allocation and quay crane assignment model (BAQCAP) is solved by two metaheuristic algorithms; Taboo Search (TS) and Ant Colony Optimization (ACO). On the other hand, the berth plan is located in a disturbed environment; unexpected events may occur during the execution of the plan, making it infeasible or challenging to do the initial berth plan. These unexpected events are known as disruptions, which can impose additional costs on the port or make the initial berth plan infeasible. For this reason, The primary purpose of this paper is on the berth plan recovery in the disrupted situation. the Berth plan is recovered with two methods; Global recovery and local recovery. This paper compares global and local recovery to identify the optimal method for berth plan recovery. The numerical results show the optimal performance in the local recovery method. In this paper, the data from Shahid Rajaei port is used.

Roads are one of the most critical assets of any country, and a large part of the country's budget is spent annually on repair and maintenance operations to eliminate cracks. One of the factors that can effectively increase the useful life of asphalt pavement is the self-healing potential of asphalt mixtures. This study considers the factors affecting the self-healing index (such as type of additive, percentage of additive, gradation of aggregate, type of bitumen, crack repair cycle, type of heating, and heating time), and using a neural network model to predict this index. For this purpose, multilayer perceptron neural network (MLP), m multilayer perceptron neural network with particle swarm optimized algorithm (PSO), radial basis function neural network (RBF), and statistical analysis with SPSS software were used, and the results of these methods were compared. The results showed that the multilayer perceptron neural network (MLP) with a correlation coefficient of 0.96 has a better performance in predicting the self-healing index than other methods and to evaluate the generalization power of the neural network using data that were not used during modelling, multilayer perceptron neural network (MLP) and radial basis function neural network (RBF) work best. Also, three samples were made in the laboratory by replacing 60% of steel slag with coarse aggregates (4.75-9.5 mm), and their self-healing results were evaluated with neural networks.

Air travel delays are inevitable and play a very important role in the profits and losses of airlines. By predicting or estimating delays, the benefits of agencies and customer satisfaction can be increased to some extent. Flight delays change flight schedules as well as increase organization costs. In this regard, a lot of work has been done, including scheduling and reallocation of gates, most of which tried to allocate the gate without considering flight delays and only due to limitations and the use of available resources. Since the variables affecting the gate allocation are uncertain and there is no known algorithm to find the optimal solution for it in a limited time, this issue is considered NP-hard problem and is subject to different conditions and policies of airports and agencies. In this paper, in case of delay, a hybrid system based on the Gray Wolf algorithm and fuzzy allocation logic is designed, which up to date the gate allocation list according to the uncertainties and using the data related to the delay prediction. The proposed model was implemented and tested using Boston data in a MATLAB environment. The results of the model evaluation showed that the proposed model has improved the gate allocation list so that some gates remain empty, which means that the number of flights can be increased and the organization's profit can be increased without changing the physical infrastructure of the airport.

Today, with the expansion of the road network and the increase in construction and maintenance costs of pavements, using additives in bitumen has become a popular method to improve asphalt performance. Various polymeric additives are widely used additives in bitumen and asphalt mixture. This paper investigates the effects of 2, 4, and 6 wt% of Styrene-Ethylene/Propylene-Styrene (SEPS) polymer on the bitumen's behavior. In this regard, linear amplitude sweep (LAS) and time sweep (TS) tests have been used to evaluate the fatigue characteristics of modified bitumen. In addition to the common method of estimating fatigue life in the LAS test, the pseudo-stress method was also used, and the results of three techniques were compared. As a result, using SEPS in bitumen reduces the fatigue life by 12% in the sample containing 2% of SEPS; however, fatigue life increased to 27.9 times greater than virgin bitumen by increasing the percentage of SEPS. The results of LAS and TS tests show a significant difference compared to each other. The LAS test is a suitable method to check the fatigue characteristics of bitumen due to the shorter test time. According to the pseudo-stress method results, the fatigue life of the samples containing 2, 4, and 6 wt% of SEPS are 0.75, 2.5, and 15.8 times greater than the virgin bitumen sample.

In this study, we utilized mobile phone call detail records (CDR) to extract individuals’ travel patterns in Tehran, trying to demonstrate the potential and opportunities of employing CDR for studying mobility patterns compared to traditional household surveys. In this regard, the raw CDR is firstly refined and preprocessed, and the initial dataset (containing about 1.6 billion records of 12 million people) is reduced to a sample of about 400,000 users with 500 million records. Then, using a series of heuristic and learning-based clustering algorithms, we identified each individual’s activity locations and inferred their trip purposes. By identifying the origin and destination of trips for individuals, the O-D matrices of the CDR sample are extracted. The sample O-D matrices are then multiplied by expansion factors to expand the sample to the population. We observed that these matrices are satisfactorily consistent with the travel patterns of people in Tehran extracted from Tehran’s Transportation Masterplan studies. Validating the results against the previous surveys shows that the proposed methodology is applicable in transportation and urban planning.

It is very important to pay attention to pedestrians as one of the key elements in the flow of traffic. In recent years, adequate and safe time for pedestrians to cross urban streets has been an important issue. In countries such as our country, Iran, personal characteristics, gender and type of clothing, especially in women as users of pedestrian facilities at intersections adjacent to holy places is one of the effective parameters in accepting the opportunity for pedestrians to cross. In this research, pedestrian crossing time with field studies and by Raff’s method and dual logistic modeling were analyzed on four important intersections of Rasht city; Including, two intersections adjacent to the holy places and two intersections in other places. The results showed that at intersections adjacent to holy places, men need 21% and women need 57% more critical time interval than other intersections to cross safely. The effect of gender also showed that women needed 18% more-time interval than men to cross the intersection of adjacent holy places. Also, women with tent cover at the intersection adjacent to holy places need 18% more critical otime interval to cross the intersection than women without tent cover. Dual logistic modeling showed that if the intersection is adjacent to holy places, pedestrians have an 80.6% probability of accepting a critical time interval compared to other intersections to cross. The probability of accepting the critical time interval for pedestrian women to cross the intersection is 82% of men. Also, the probability of accepting a critical time interval for pedestrian women with tent cover to cross the intersection adjacent to the holy places is 40.7% compared to other intersections.

Crash prediction models (CPMs) are a valuable tool in transportation planning studies that enable safety and its changes to be investigated in future decisions. The main goal of this study is to develop the CPMs based on the trip distribution step from the common four-step demand models, which is called the crash risk distribution in this study. In order to, the Negative Binomial formulation with a log-link function is used. The frequency of severe crash in the city of Qom, Iran, including the total number of fatal and injury crash among each origin-destination (OD) pair of traffic analysis zones (TAZs) as a dependent variable, and explanation variables, including the traffic-related and trip distribution results, have been used for model calibration. Traffic-related data include the vehicle kilometers traveled, experienced travel time with vehicle, and average vehicle speed between each OD pair of TAZs. The results of trip distribution include the trip distribution by purposes of work, education, shop, and personal. Model calibration and validation results show a significant relationship between the mentioned variables and the frequency of severe crashes. Hence, the developed model predicts the number of severe crashes in future years based on increased trips distributed among TAZs. Therefore, with the help of this model, it is possible to identify and prioritize trips with high-risk origin-destination in terms of safety, and the impact of different travel demand management scenarios on safety can be evaluated.

Sliding window is one of the road network screening methods to identify black spots. There are two parameters which are required to be defined by the user: the window length and the minimum number of crashes per black spot. Based on previous studies, changing the window length significantly affects the process of black spots identification. In other words, changing window length and increment length leads to different results. The reviews show that, window length and increment length are selected based on engineering judgment or using the previous experiences, and statistical techniques and scientific methods have not been used. In this research, a new method is proposed based on optimizing the framework of the sliding window method by examining its features. The new method allows the sliding window length to be estimated dynamically. The optimization methodology is as follows: First, the desired road is segmented and for each segment, several different scenarios of window length are selected using the density-based clustering algorithm. Then the safety performance function is developed and the number of predicted and expected accidents and the difference between them as the potential safety improvement is calculated for each window movement for all selected scenarios in the segment. Finally, by calculating average differences using the analysis of variance, the length that has the lowest dispersion of difference values compared to the mean is determined as the optimal length of the window in each segment. The results of this study showed that in all segments, at least in a single movement of the window with the estimated optimal length, the SPF-value of the window has met the desired level of accuracy. Also, by moving the window with optimal dynamic lengths, 122 prone black spots identified, which has more crash density and effective length compared to the results of windows with fixed lengths of 300, 500 and 1000 meters.

Today, one of the main challenges of traffic is affected by pedestrians, that the different behaviors of people have a direct effect on traffic parameters. The main goal of this research is to improve and improve traffic flow and improve traffic for vehicles and pedestrians.  For this purpose, a case study was conducted on the behavior of pedestrians at the level intersection First of all, the traffic statistics of pedestrian and vehicular traffic were collected in the eight crossings of this intersection in the mentioned time periods, and by using the traffic simulation for vehicular and pedestrian traffic in this intersection, as well as considering and applying behavioral parameters and calibration for vehicles and for pedestrians in  considered  In order to identify the effect of pedestrian behavior and match it with reality, three scenarios were considered in the case study, in which the first scenario is related to the simulation of the intersection without considering the pedestrian traffic, the second scenario is the simulation of the intersection with the consideration of the pedestrian traffic and  Finally, the third scenario is the real behavior of people by simulating it in viswalk, considering the pedestrian traffic.  The results showed that the quality of traffic parameters in scenario 2 and 3 compared to scenario 1 has decreased by 13.5 and 25% respectively for the total speed of vehicles and increased by 21 and 38% for density, in fact the effect of traffic interference and pedestrian behavior  Pedestrians and riders indicate traffic flow parameters.

The ever-increasing demand in the field of rail freight transportation and the competitiveness of the price of goods transportation and rail transportation fares compared to other transportation sectors in the country is one of the most important reasons for the development and investment as much as possible in this sector of transportation. Different criteria in the field of rail cargo transportation, both by the owners of the goods and by the rail transportation companies licensed to issue a rail bill of lading, have been discussed and investigated in recent years. The main purpose of this article is to apply the use of artificial intelligence in MAPNA multimodal transportation company as one of the important companies in the field of rail cargo transportation, in order to find the optimal load. In this article, the implementation of multi-layer artificial neural networks and the description of the implementation codes were done through the Python programming language and after evaluating and adjusting the model parameters, the weights of the network were stored by the pickle library that can be used to estimate new data. After checking 89275 bills of lading available in the railway network, the information of the mentioned bills of lading was entered into the simulated program, the main formula of which was formulated by industry experts and railway experts. The results of the optimal rail load from among the issued bills of lading show more income and lower costs.

The main causes of traffic accidents include human, road, environment and vehicle, among which the largest share is related to the human factor. So human factors should be prioritized during traffic safety studies. Despite the relatively large investment in two sectors that affect accidents, which are roads and vehicles, the efficiency corresponding to these costs is not achieved In Iran. One of the issues and problems of traffic safety studies is the behavioral evaluation of vehicle drivers, which has been replaced by the use of car driving simulators due to the dangers in the actual process. In this research, car driving simulators were investigated in order to identify the most important indicators that can evaluated by these devices. This process helps to improve and systematically control the use of existing simulators, and to build and provide suitable new simulations in accordance with the needs and priorities of the city of Tehran. The analysis of factors that can be evaluated by the simulator device through multi-criteria methods revealed that risk perception and speed selection are among the most important factors, and eye movements and visual search, as well as the driver's behavior when facing a traffic light, will be among the least important ones.