فصلنامه مهندسی حمل و نقل
سال پانزدهم شماره 1 (پیاپی 58، پاییز 1402)

Porous asphalt pavement (PA) material is a special open-graded asphalt mixture which has a large proportion of interconnected air voids with a void content of 18% or more. Traffic noise, splash, spatter, aquaplaning and skid resistance are public problems in dense asphalts. Porous asphalt pavement is an alternative that believed to be meeting above mentioned requirements. The aim of this study is to evaluate the performance of rubber powder and styrene-butadiene-styrene (SBS) on mechanical properties, surface friction, macrotexture and sound absorption of these PA mixtures. For this purpose, Porous asphalt mixtures with two different types of gradation were studied and compared. SBS polymer and recycled rubber powder were used to modify 60/70 bitumen. In order to evaluate the desired characteristics, ITS moisture sensitivity, Cantabro abrasion, Drain down, pavement macrotexture depth, British pendulum and sound absorption tests were carried out on the specimens. Test results showed that rubber powder had a positive effect on surface friction and a negative effect on sound absorption of polymeric PA mixtures. However, the gradation containing more fine aggregates had a better surface friction than the other one. In addition, adding crumb rubber into polymeric porous asphalt mixtures, decreased sound absorption in all frequencies from 125 to 1250 Hz.

Berth planning Problems are one the problem related to port management that the answer to these problems can affect the cost of the port and therefore have been studied in many papers and studies. Berth planning includes two subproblems; Berth Allocation Problem and Quay Crane Assignment Problem. This paper intends to merge these two subproblems and develop an integrated mathematical model. An ant colony optimization algorithm is performed to solve this integrated model, which leads to an optimal berth plan that reduces port costs. The initial berth plan is planed assuming that all input data is certain, but this is not true. Unforeseen events may occur during the execution of the initial plan, which may challenge the execution of the initial plan. These types of events, which are generally unpredictable, are called disruptions. The process of recovering the initial berth plan in a way that results in the lowest cost and penalty for the port is called disruption management. So far, two general methods have been introduced for the recovery and management of berth allocation disruption; the Global recovery method and the local recovery method. This paper introduces a local recovery method by eliminating the weaknesses of the previous local recovery method and developing it. In order to validate the methods of solving mathematical models, the data of ships' entry and exit in both container terminals of Shahid Rajaei port are used. The numerical results of this paper show that local recovery methods perform much better than the global recovery method (the optimal response in local methods in the first terminal is about 120% and in the second terminal is about 300% better than the response in the global recovery method). The two methods of local recovery and the developed local recovery method have almost similar answers. However, reaching the final answer in the developed local recovery method is approximately 2 to 3 times faster.

The expansion of air, sea, and land transport in terms of facilities, passengers, cargo transportation, speed, accessibility, and network development are increasing daily, which can have disadvantages beside the benefits of its development. The most prevalent of which in recent years are outbreak, intensification or causing disease by the transport system. The restrictions imposed by diseases such as Covid-19 on the transport system during recent years have led the study to investigate the structural dimensions of transport and disease studies in a larger dimention and to identify the types of common diseases that are closely related to the transport system. In fact, this scientometric review provides an overview of transportation and disease studies at the core of the Web of Science (WoS) search engine using the Vosviewer and Bibliometric software. In this study, descriptive analyzes have been used to examine the temporal development trend of the number of studies, identify effective journals and articles. Conceptual analysis is organized with keyword co-occurrence analysis of research fronts to identify and classify them. Based on co-occurrence analysis, the research fronts of transportation and disease studies are classified into 4 main categories: "Infectious Diseases", "pandemic diseases", "vector-borne Diseases", and "Chronic Diseases".

Pedestrians are one of the most vulnerable users of urban roads that are directly exposed to accidents. Traffic crashes tend to be spatially dependent and is a phenomenon known as spatial dependence. The objectives of this study include identifying alternative variables for pedestrians in urban roads and identifying accident-prone areas using pedestrian exposure variables, as well as demonstrating the efficiency of spatial models in predicting pedestrian crashes. In this study, in the first step, which is the identification of exposure variables, several statistical methods have been used to identify these variables. Also, the frequency of pedestrian crashes has been predicted using six widely used models of spatial statistics, which have been evaluated based on pedestrian crash data in Tehran for the years 1396-1398. The results of this study show that the prediction of the frequency of pedestrian crashes using Poisson regression models with zero-inflated geographical weight and distribution of zero-inflated negative geographical binomials has better results based on model selection criteria than other models. This study has shown the dispersion and density of pedestrian accidents without having the volume of pedestrians and thus can be done by taking safety measures in places prone to pedestrian crashes, social costs and casualties. In this study, the use of various geographical weighted regression models to evaluate the relationship between sociological variables and crash frequencies at the area level was appropriate. Comparison of the performance of geographic weighted regression models shows the existence of significant spatial heterogeneity in the analysis.

The need to move from the most fundamental prerequisites for human societies. Disabled people and elderly people will not be able to use transportation facilities provided for other people. This will reduce the participation of these individuals and their productivity in the community. According to the global health institution of disabilities, there are many: inability to move, visual problems, hearing problems and wheelchairs. These people do not have the ability to use personal cars independently and are dependent on a person or other transportation system. In this research, first examines the motor characteristics and the human needs of these individuals according to their type of disability. Subsequently, according to a questionnaire for the survey of people with disabilities, the minimum parameters needed and effective for the use of disabled use of the transportation facility have been identified. Then, the parameters of the index using a hierarchical multi-criteria decision-making method are prioritized. The results of the research show that the highest share of the community consists of physical and motor disabilities, if urban spaces and transportation devices are suitable for passing and reviewing these people, with minor changes, the background of the rest of the disabled can be created at the city level.

The purpose of this research is to increase public transportation based on the goals of urban and suburban communities and, among the numerous modes of suburban transportation, to produce an acceptable alternative by emphasizing the competitive advantages. This research seeks to introduce a scientific method and model for grounding theorists. The current research highlights the subject of suburban transportation's benefits via a detailed examination of the many important and useful areas of transportation. The whole procedure defers expert opinion in a cogent way, overlapping several community management aims. A model is created to illustrate the benefits, the internal connection, and their relevance to meet the study goals..In examining the benefits, it seems that contemporary societies' objectives have shifted toward encouraging sustainable growth in the coordination of urban structure and transportation infrastructure, and transportation issues have become a key concern. As a result, society's macro-management policies are pushed toward reducing the costs of this business's environmental and social tolls. A questionnaire is used to obtain preliminary data. The multi-criteria network analysis decision-making approach is utilized to examine the data, as is the DEMATEL method for determining the model linkages.

The acceleration of urbanization has created many problems in cities, such as congestion of vehicles and environmental pollution. One way to solve these problems is to use bicycles as a means of transportation. For this reason, in many developed countries, the bicycle sharing system has been launched. The purpose of this research is to determine the selection criteria for the location of cycling stations and optimal shared bike routes. Initially, first, by using the Delphi technique in two stages, effective criteria were identified in choosing suitable location for the construction of stations. Then the importance of the criteria was obtained by the best-worst (BWM) method. As a result, the factors of proximity to public transportation stations, proximity to cycling infrastructure and distance of stations from each other were recognized as the most important criteria for choosing the optimal locations. Then, by using Geographic Information System (GIS), suitable locations for building shared bicycle stations were identified. In the end, according to the available usable routes, the best route was suggested.

In Iran, there are many internet taxis such as Tapsi, Snap, etc., which are an important element in urban transportation. More use of this transportation system can reduce the use of private vehicles and effectively use the city transportation system. Therefore, identifying the mental patterns of passengers and their expectations from internet services can be helpful in using this type of transportation system. In this respect, in this research, Q factor analysis has been used to identify the mental patterns of passengers, and a sample of transportation passengers in the country was selected as the target population, and based on the Q method, the propositions were prioritized by the participants, which was finally based on factor analysis. Q (the mental patterns of passengers regarding the factors affecting the country's road transportation were identified and useful information was obtained in this field. The results showed that seven mental images regarding the factors affecting the country's road transportation were presented by the passengers based on The most important mental image, the factors of multiple increase of the fare during the peak travel hours, the long distance of the driver from the origin after accepting the passenger, the sudden increase in prices and the large difference in the price of night and day have the greatest impact on internet transportation, and the factors of not monitoring the drivers' receipts for time Stopping, not using disinfectant solutions, not easily applying discount codes have had the least impact on the country's internet transportation. Also, in this research, the factors affecting the use of internet taxis from the drivers' point of view, using AHP method was investigated. In general, from the point of view of drivers, driving factors of drivers in nervous and difficult conditions have the greatest impact on the service procedure, and in the next stages, the location is not the same for the driver and passenger, misuse of passenger numbers by drivers, smoking in the presence of passengers, multiple increases in fares in Peak hours of travel, not finding a car on short routes or low prices, the long distance of the driver from the origin after accepting the passenger, the sudden increase in prices and the large difference in night and day prices were recognized as important in the country's internet transportation. But from the point of view of the drivers, according to the results of the AHP table, the high commission fee and the lack of service to the driver caused the driver to drive in nervous and difficult conditions, and on the other hand, the drivers, contrary to the opinion of the passengers, chose short routes to reach the number of services intended by the company. Include rewards.

The analysis of transportation demand is one of the most important pillars of transportation planning and management. Therefore, choosing effective decisions in the field of travel demand requires knowledge of price and income elasticity of different vehicles. In this regard, the purpose of this study is to estimate the direct and cross elasticities of price and calculate the income elasticity of urban transportation demand, including buses, subways, taxis and private cars in the area of Shohada Square- Imam Hossein Square in Isfahan by the method of system of the almost ideal demand. The variable considered in this study include household expenses, monetary, and time costs of urban transportation mode which was extracted through random distribution of a questionnaire among passengers. The result of this study shows that the amount of self- price elasticity of buses, subways, taxis and private cars is negative, which indicates that it is in line with the law of demand. Also, the values obtained for elasticity indicate the inelasticity of demand for these vehicles and insensitivity of passengers to changes in the price of these vehicles. The result of income elasticities show that buses and subways with income elasticities of less than one are essential good while taxis and private cars with income elasticities of more than one are luxury good. The results related to the substitution elasticity indicate that taxi and subway both substitute for bus but the intensity of metro substitution is more than taxi, while the amount of the substitution elasticity of metro with bus, taxi and personal car indicate that all three kinds of transportation are substitute for metro. But the substitution elasticity of taxi is significantly higher than other two kinds of transportation. So, with the increase in the price of metro, the taxi demand will increase more. The substitution elasticity of taxi with bus, metro and personal car expresses that there is no significant difference in the amount of substituting elasticity of these three kinds of mod, but Substitution elasticity of personal car with other vehicles indicate between three kinds of transportation which substitute with personal car, the substitution elasticity of taxi is more than others.

Traffic forecasting, as an important part of intelligent transportation systems, plays an important role in monitoring traffic conditions. Given that many studies have done traffic prediction work with deep learning models, no research has been done on traffic speed prediction in different chapters. Considering the important effects of spatio-temporal factors and the excellent performance of recursive neural networks in the field of time series analysis, in this paper, one of the deep learning neural networks that combine the characteristics of Giti recursive neural networks in consecutive time data is proposed. The data used in this study are obtained from the active visualization and evaluation network in Seattle, USA. This research has compared three seasons of spring, summer and autumn and four deep learning models including LSTM, GRU, ConvLSTM and BiLSTM and a shallow SVM model in three time steps of 5 minutes, 10 minutes and 15 minutes. The results of this research show that the results of the proposed model There was no significant difference in different seasons, and also the four deep learning models and the SVM model did not have significant differences in different seasons and these differences can be ignored. The other results show that as the interval of time steps increases, the errors increase and the accuracy of the model decreases. According to the obtained results, the accuracy of the FI-GRU model is 0.52% higher than the lowest accuracy of the deep learning model (BiLSTM), and the accuracy of the proposed model(SVM) is 1.24% higher than the shallow learning model, as well as the proposed model for predicting traffic speed. It has performed 44.1% better in the time step of 5 minutes.

The decrease in airports' revenue in recent years and the small share of non-aeronautical revenues in Iran's airports show the necessity to pay attention to increasing revenue through the non-aeronautical sector. On the other hand, there needs to be a specific framework for the pricing of the non-aeronautical sector to aid airport management in control and increase revenue, as well as increase the desirability of private sector investment. Accordingly, this research aims to present econometrics models of pricing for some non-aeronautical services using available data on airports. For this purpose, after identifying the factors affecting the cost of services, data on 60 airports from different countries was collected in 2022. After that, the models of the pricing of the services were explained using the econometrics model of multivariate linear regression. The results show that the variables of the number of annual passengers, the average air distance of flights, and the monthly cost of welfare facilities (water, electricity, and gas) in the city affect the cost of Commercially Important Person (CIP) services. In the evaluation of baggage wrapping models, the variables of the number of flight destinations, the amount of cargo moved annually, and the monthly cost of welfare facilities have a significant effect on the cost of baggage wrapping. According to the results, the models estimated for the pricing of non-aeronautical services can be the basis for pricing the studied services in Iran's airports and help increase the revenue of the non-aeronautical sector in the coming years.

Driving behavior refers to a set of relatively stable, observable, evaluation and prediction actions and reactions of humans in the field of traffic and the use of transportation devices, which, due to external stimuli, affect how they and other road users travel and can have positive or destructive effects on have traffic flow. In this research, attention has been turned from a linear view of the problem to a non-linear and multifactorial view. This research seeks to investigate driving behavior using simulation based on system dynamics approach. The main goal of the article is to present the practical model resulting from the design of driving behavior in Tehran. In order to achieve this goal, by reviewing the literature and reviewing the documents, the key variables of driving behavior were identified and based on this, the dynamic hypotheses of the model were formulated. In the following, the cause and effect and state-flow diagrams were drawn and the equations related to the relationships of the model variables were extracted. In this research, the opinions of experts in different executive departments were used in the form of semi-open or closed interviews. The main part of the required data has been collected and analyzed from the existing data banks in the traffic-related units of Tehran. In the end, after testing the dynamic model using software and wind, different scenarios were proposed to apply the model. The results of the selected scenarios show improvement in variables related to driving behavior.