shahriar afandizadeh

Control of traffic performance just via right of way regulations, causes the forming of long queues along the entry approaches of roundabout while results in decreasing the capacity of roundabout and also increasing the delay time of network. The idea of signalization of roundabouts can be useful as a counteraction for this problem, especially during peak hours. In this case, determining the type of control, phasing and timing of the traffic signals and also defining a pattern in signalization of roundabouts in order to select the best alternative of traffic signals operation in roundabout and reach to optimal results, is the one of the problems that seems to be facing traffic engineers when signalizing roundabouts. In this paper, to evaluate the performance of traffic signals at roundabouts with high flow rates and in order to find the suitable scenario for signalization of roundabout to improve the traffic condition, Valiasr-square which is one of the densest roundabouts in Tehran, as main case study model and Ferdosi-square, as validation case study model, are modeled and simulated in traffic simulator software AIMSUN. According to the results obtained for valiasr-Square includes a 32% reduction in delay time, 17% reduction in density, 13% increase in flow, 48% increase in speed and 17% reduction in total travel time while installation of traffic signal in its optimal scenario, it can be said that the use of traffic signals at roundabouts with high traffic volumes is a good choice to get more traffic flow and fewer delays.

Metro has become a necessity as a solution to traffic and a heavy and important means of public transportation in today's world. The need for traffic management and planning is inevitable. Therefore, the purpose of train scheduling is to minimize the time of the trains from the origin to the destination, to satisfy the passengers and stakeholders, to minimize the delays in the stations and to maximize the use of the capacity of the lines, stations, and fleet. An approach by considering all the factors that affect the time of trains in the network can lead us to produce a plan to achieve the mentioned goals. In this research, which is a case study of an urban train between Tehran and Karaj using queuing theory with the M/G/C/C pattern, a schedule for the train head based on three scenarios of the maximum capacity of the train in which some passengers are standing on the low track, the capacity In the mode of all passengers sitting, and the other mode was considered in the mode between these two modes. It was investigated using the hourly pattern in the case study of line 5, 32% of the distribution is in the middle stations, currently the current schedule of the metro causes delays and Dissatisfaction is for a group of passengers, which the proposed timing reduces to some extent. The proposed time is between 6-30 minutes in different scenarios for different hours. The proposed time reduces the waiting time by 21%.

Collecting tolls is one of the solutions favored by transportation planners in dealing with traffic congestion. In this paper, urban tolls for internal freeways are presented in the form of a two-level optimization model. At the top, the decision level of the freeway toll is placed, and at the bottom, the issue of travel characteristics in the road network is solved to increase accuracy and efficiency. Allocation of travel from two aspects of network performance and social justice has been done dynamically, considering different population groups in terms of value. Also, this article considers a novel dynamic allocation model that uses the accuracy of micro-view and the speed of macro-view. To solve the problem in this multi-population metaheuristic algorithm has been developed. The combined multi-population method strengthens the local and global search operations to divide the population into different sub-populations. The teaching learning-based optimization (TLBO) is used in the presented method. The results showed that the speed and accuracy of the developed model are superior to the three competing algorithms. This algorithm reduced the travel time of the entire network from 8205 hours to 6683 hours, equivalent to 18% of the total travel time, and also, the speed of the presented algorithm has increased six times compared to the genetic algorithm.

The development of roads to meet the demand of cities has lost its efficiency in the modern world. Experts have found a solution to traffic problems, managing travel demand and increasing the efficiency of the transportation system. One of the ways to increase the efficiency of the transportation system is to use intelligent transportation systems. In this research, the causes of dissatisfaction of bus passengers are determined by a questionnaire, so that appropriate smart solutions can be selected based on that; Then, information is collected through Tehran Bus Company and field observations, and the current state of the network is calibrated using the information available in the pedestrian simulator software and according to the field observations. Finally, scenarios are defined to reduce delays and, as a result, reduce bus travel time. The proposed scenarios are simulated in the software. In this study, the impact of ITS as a tool to reduce bus travel time has been investigated. By intelligently removing stops at undemanding stations, using variable message signs (VMS) for drivers to increase speed, and eliminating cash fare payment, travel time can be saved by 9.8, 4.4 and 4.1 percent, respectively. The results obtained from this research show that by combining the two scenarios of intelligent elimination of stops at stations with no demand and the use of VMS signs to increase the speed, the bus travel time on the desired line from 317 seconds per kilometer to 261 seconds per kilometer. (17.7 percent) decreases.

Transportation is known as an important energy consumer, and achieving sustainable transportation implies controlling and improving the amount of energy consumption. So, a system for evaluating the urban transportation system based on the principles of sustainability can help to improve the economic, social and environmental indicators. This study was conducted with the aim of clarifying the place of energy consumption in the transportation evaluation model based on sustainable development indicators. This research is applied-developmental in terms of purpose, and in terms of the method and time period of data collection, it is considered a survey-cross-sectional research. Library studies and questionnaires were considered as data collection tools. In the qualitative section by searching, in the period from 2000 to 2023, 41 articles were selected and analyzed. Meta-composite analysis was used to identify the research categories and present the initial model, and 9 components and 53 indicators were identified. Validation and screening of indicators was done with Delphi technique. The participants of this section are 28 experts who were selected using the snowball pattern. Finally, 9 components and 49 indicators were confirmed. In the end, using the process of network analysis, the priority and weight of dimensions, components and indicators were determined. The findings showed that the environmental dimension has 45% and the economic dimension has 36% of the weight of the model. The social dimension is ranked third with 19%. The energy consumption component has the most weight with 17% and its indicators have 3 positions out of the top 10.

Millions of vehicles are moving on the roads worldwide, and billions of dollars are spent each year to meet transportation needs as the main artery of the world economy today. The role of the road and, consequently, asphalt as a factor in accelerating movement and reducing depreciation costs is undeniable. The investigation of various methods used to determine the passenger car equivalent (PCE) indicates that most of these methods are focused on traffic characteristics affected by the presence of heavy vehicles. The most important of these parameters are traffic speed and the formation of a group of vehicles along the route, the speed of heavy vehicles, especially on steep slopes, the dimensions of vehicles, and the maneuvering capabilities of these vehicles in reducing and increasing slope, which are among the parameters used in determining the PCE. This study aims to analyze effective indicators and determine the PCE of various vehicles on the main roads of the country. The results show that the PCE for two-axle trucks and minibusses have the lowest coefficients among heavy vehicles. After that, the coefficients increase in the order of buses, two- and three-axle trucks, some vehicles, two-part heavy vehicles, and four-axle trucks and above. This order has been observed in different topographic conditions, different slopes, and different volumes.

Travel time reliability affects the behavior of passengers in private or public transportation and can be seen as an important factor in the context of freight transportation. The main cause of travel time oscillation, known as travel time reliability, is congestion. Congestion is classified into two categories: recurring and nonrecurring. Recurring congestion, which is the topic of this study, is formed when supply surpasses capacity. Peak periods are good examples of recurring congestion. In this paper, by utilizing different bagging regressor methods, the effect of speed flow reduction, compared to Free Flow Speed (FFS) in terms of congestion was studied on the Planning Time Index (PTI) on a section of Interstate 64 in the United States (US). Then, by analyzing PTI changes based on congestion variation, it was revealed that when speed reduction surpasses 10%, travel time leaves its reliability. Also, when the congestion is somewhere around 0.7 to 0.75, the unreliability becomes severe. These findings were directly extracted from scatter plots drawn by bagging and bootstrapping samples which were used to improve the accuracy of PTI prediction.

The postal network is a network in which the flow of shipments is collected by vehicles from several sources and transferred to hubs; In the last step, the parcels are sent to the final destination by the vehicles. In order to provide this service, the hubs are located to process the parcels and the local routes are planned to pick up and delivery the packages in the hubs. This problem is known as location-routing problem. The current research seeks to locate the hub and the routing of freight carriers on an intercity scale and at the level of Iran. For this purpose, first, the mathematical model of the problem of locating the hubs and routing the cargo carriers is presented, and since the problem of locating-routing is in the category of complex problems with large dimensions, meta-heuristic algorithms have been used to solve it. In previous studies, meta-heuristic algorithms or a combination of them have been used to solve the location-routing problem, but in the study, the location-routing problem has been solved by integrating two genetic algorithms (GA) and the particle swarm optimization algorithm (PSO). The results of this research show that the integration of two algorithms, PSO and GA, provides a better answer than if only one meta-heuristic algorithm is used to solve the problem. It is worth mentioning that in the current research, the data of Tipax Company was used to locate the hubs and local tours routing.

The purpose of this research is to investigate the impact of self-driving cars on changes in travel. To do the work, first, the data related to persons, households, land use, and skim matrices between the studied areas of Washington DC were entered into Activitysim software. The information obtained from the models without the presence of self-driving cars was examined, then the way the self-driving car traveled was added to the software, and the information resulting from the effects of these cars on the changes in trips, which is derived from the changes in the variables of the specific age group, the travel time in the accessibility, choosing more distant destinations for non-mandatory tours and the frequency of non-mandatory tours, in considered. The results in the one-dimensional analysis of the variables show that among the selected variables, the variable of non- mandatory activities of people in the frequency model of non- mandatory tours with 11.7% and the variable of travel time in accessibility model with 5.5% have the greatest effect on the changes in the number of trips. Also, the presence of self-driving cars has the greatest impact on shopping and leisure trips. Another goal of this research is to build a quick response model. After examining the interactive effect of the variables, it was found that the travel time variables and the activities of the working people, the age group under 18 years, and the retired are the most important variables influencing the changes in the number of trips.

The Complexities of Operating DAR Services Mean That Computerized Planning and Scheduling Is Necessary for Systems of Realistic Size. This Research Studies an Electric Autonomous Fleet Size with Mix Dial-A-Ride Problem. The Goal of The Problem Is to Minimize a Weighted Objective Function Consisting of The Total Travelling Costs of All Vehicles, Users' Excess Ride Time Costs and Vehicles' Acquisition Costs While Satisfying Customer Service Level Constraints Along with Battery Level Management and Recharge Times Management Constraints. In This Variant of The Dial-A-Ride Problem, Recharging at Any of The Available Charging Stations Is Allowed. A Cluster-First, Route-Second Genetic Algorithm Is Proposed to Solve the Problem, Where the Clustering Is Performed by Choosing the Fleet Size and Assigning the Customers to The Fleet Using a Genetic Algorithm (GA), Then the Primary Routes Are Developed by A Routing Heuristic, Finally the Charging Stations Will Be Inserted to The Algorithm Using an Insertion Technique. The Performance of The Proposed Method Is Tested by Using Benchmark Instances of a Related Problem from The Recent Literature. The Proposed Method Has Achieved Solutions Comparable with The Current State-Of-Art Methods. The Computational Results Show That the Proposed Method Is Effective in Finding Comparable Solutions with The Current State-Of-Art Method. New Instances, Some of Which Include First-Mile Feeder Services, Are Generated Based on The Data from Uber Technologies Inc. Tests Performed on New Instances Demonstrate That the Minimum Possible Fleet Size Does Not Always Result in Minimum Costs. Moreover, The Tests Show That Integration of The Feeder Services into Dial-A-Ride Services Increases Ride-Sharing Ridership.
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Introduction One of the important issues that can determine the status of a city and make changes in the quality indicators of its space is the urban transportation system. Roads and transportation systems are important elements in every city that shape the overall structure of the city. Today, with the ever-increasing population growth and development caused by cars, transportation and traffic is one of the problems of cities, especially in metropolitan areas that consist of a mother city and several satellite cities and towns, this problem is greater, because a significant population They live in satellite cities and because of their employment in the mother city, they travel to it every day, and in the absence of a reliable and planned transportation system, they increase the problems of the mother city. As long as city and metropolitan area programs and transportation system plans are prepared with the car-oriented approach, it will not be possible to achieve a sustainable transportation system. Therefore, a system for evaluating the urban transportation system based on the principles of sustainability and sustainable development implies the improvement of economic, social, and environmental indicators of cities. The present study was carried out as a part of more comprehensive research to provide an evaluation model of the urban transportation system based on sustainable development indicators in the metropolitan area of Tehran, and it seeks to examine the independence or dependency of the components of the model.
Materials and MethodsThe current research can be considered applied-developmental in terms of its purpose, and in terms of the method and period of data collection, it can be considered survey-cross-sectional research that was conducted with a mixed approach. For this purpose, in the qualitative section, by searching the databases, 43 articles were identified in related fields between 2000 and 2023, and finally, 41 articles were selected in a targeted way and entered the analysis stage. To identify the relationships between the components of the evaluation model of the urban transportation system in the metropolitan area of Tehran based on sustainable development indicators, structural-interpretive modeling was used. The participants of this section were 28 experts in the fields of urban planning, transportation, economy, social sciences, and environment active in Tehran municipality, who were selected in a non-probability, purposeful way and with a snowball pattern. In the next step, the validation of the designed model was done with the method of structural equation modeling and the partial least squares technique. The statistical population of the quantitative part includes specialists in the aforementioned fields who work in the metropolitan area of Tehran, and the sample size is 530 people. Library studies and questionnaires were considered as data collection tools, which were validated using construct validity, convergent validity, and divergent validity methods. Validation was performed in the qualitative section using the CASP model. The reviewed articles in the qualitative section were evaluated based on the ten criteria of this model and the articles with a score above 25 were selected. The validity of the questionnaire was measured through content validity (opinions from experts) and its validity was confirmed. Also, in a preliminary study, the total Cronbach’s alpha of the questionnaire was calculated as 0.856. After distributing the questionnaire to the selected sample, the validity of the questionnaire was checked using three methods construct validity (external model), convergent validity (AVE), and divergent validity. is greater than 0.5. Composite reliability (CR) and Cronbach’s alpha coefficient of each factor were determined to calculate reliability. The combined reliability and Cronbach’s alpha of all dimensions of the model should be greater than 0.7. Meta-composite analysis was used to identify the underlying categories of the research and provide the initial model. Validation of the designed model was done by the partial least squares method. Data analysis of the qualitative phase of the research was done with Maxqda software and related to the quantitative phase with Smart PLS software.
FindingsFrom the review and analysis of the articles, several 166 indicators were obtained, then the indicators that had the same meaning or were repeated in different forms and expressions were removed, and finally, with the category and classification of the final indicators, 9 components (main category) and 53 indicators (subcategory) were obtained. The findings of the research showed that the components of the model are not independent of each other and the components of pollutant production, consumption resources, energy consumption, justice and access, direct and indirect costs in the sector of independent structures, and the components of economic well-being, travel safety, health and human health, environmental compatibility are in the dependent structures section and the relationship is such that the set of direct and indirect costs and justice and access affect energy consumption. Consuming sources and production of pollutants interact and lead to environmental compatibility. Finally, economic prosperity can be achieved through travel health and safety. Also, in the model validation section, the GOF index was obtained as 0.616, which is greater than 0.36. The RMS_theta index was 0.102, which is less than 0.12. The SRMR index was also calculated as 0.050, which is less than 0.08, so the fit of the model is favorable.
ConclusionThe results of the research showed that direct and indirect costs and justice and access affect energy consumption. In the results of Hirai and Kamim’s study (2022), the effectiveness of the justice component is also mentioned, and from this point of view, it is consistent with the results of the present study. The comprehensive view is important because it defines the boundaries in which all the departments should work collectively. This perspective also calls for greater attention to quality of life and includes equity analysis to include the distributive effects of transportation between regions and countries over time. On the other hand, the transportation-oriented perspective is also important because of considers the specific goals of the sector that guide the development of transportation policies and programs. In addition, it should be acknowledged that some decisions will be made with a more appropriate and relevant transportation perspective.It was also shown that the consumption of resources and pollutant production have mutual effects and lead to environmental compatibility. In the results of the studies of Patterson et al. (2021) and Mohammadpour et al. Some of the key environmental outputs (or pollutants and waste) include hazardous waste from vehicle scrapping, noise emissions, local air pollution, and climate change. Although significant progress has been made in the proportion of recyclable vehicle waste at the end of a vehicle’s life, it is estimated that about 25% of a vehicle remains unrecyclable, and much of this can be considered hazardous. Also, noise pollution caused by traffic can disrupt sleep patterns, affect cognitive function, and aggravate some cardiovascular problems. Since noise pollution is related to the number of ongoing activities and the noise intensity of those activities, it is not surprising that it is mainly an urban problem.Finally, the results of the research showed that it is possible to achieve economic well-being through travel health and safety. This importance has been confirmed in the results of Marzi et al.’s study (1401). In addition to significant economic benefits, the transportation system also creates economic costs. Road traffic accidents and air quality both lead to loss of life or decrease in quality of life, which has direct costs for the economy in terms of lost productivity and less well-being of citizens. Also, providing the transportation system and subsidies for passengers has costs for the government that must be financed through taxes or fare collection. Therefore, the implementation of the model presented in the current research, by reducing the marginal costs, helps to increase the economic well-being of the country.

In any transportation planning, including rural transportation, the characteristics of existing supply and demand must be identified. According to this knowledge, the main needs and problems of the villagers in the field of transportation can be identified. In urban transportation planning, travel models are usually built in four stages. In the first stage, travel production and absorption models, in the second stage, travel distribution models, in the third stage, vehicle mode choice models, and in the final stage, route selection and traffic assignment models. In this study, and according to the studies conducted on the difference in behavioral pattern as well as the difference in the way of planning in the village and city, modeling related to the stage of production and absorption of travel is done using backward elimination regression. This study was conducted by case study of a number of villages in Qazvin city and can be generalized to other villages that have similar characteristics. Finally, according to the developed models, it was found that in work trips, the variable of employment rate, in educational trips, the number of students, in shopping trips and ethnics and medicine and in the demand of total travel variable population have the greatest impact on the model. With the current situation demand, it is possible to develop a forecast demand model for different horizons by applying the population growth rate and employment. By estimating the demand for travel, in order to create and build road and rural facilities, road pavement and road maintenance can be planned.

In most countries of the world, saving human lives is one of the most important and first factors that have been considered by politicians. Among the causes of death, road accident is known as one of the 10 causes of death and casualties. Therefore, paying attention to reducing the number of accidents and also reducing the severity of accidents, is the goal of the country's officials, and their planning and prioritization is in the same direction. One of the most important parts of traffic accident analysis and prediction is selecting and using appropriate models. In this study, extensive research has been tried to give a good view to researchers to choose a suitable model. Also, the human factor in accidents has been studied and the parameters affecting this factor have been studied and the most important ones have been stated. Therefore, the purpose of this study is to examine in detail the most important factors (appropriate model and appropriate parameter) in the evaluation of accidents. Results are shown that Deep learning approach/Data mining/machine learning models had the highest power with 87.27%, followed by Poisson-lognormal and generalized additive models. It was also concluded that most models were used in suburban accidents, however, there were one model “microscopic simulations” that were used more in urban accidents. Deep learning approach / Data mining / machine learning has allocated the most up-to-date research with an average close to 2016 (2015.82). Random-parameters models are next with an average of 13.4. Duration models with the lowest mean (1998.2) are at the bottom of this classification and have the oldest research. Based on this information, it can be concluded that today researchers are more inclined to new models such as Deep Learning, which may be due to the high accuracy of these models.

This paper outlines the behavior of pedestrians in crossing signalized intersections and finds the main and effective variables that influence violation behavior in Tehran. For that purpose, required data was collected by recording pedestrian behavior then asking them to complete questionnaires. A binary logit (BL) model was developed to determine the influence and significance of each collected variable in the decision-making process. A structural equation model (SEM) was used to determine the interaction between observed and hidden variables and describe how they influence behavioral intention. After collecting the questionnaires and removing the false ones, 304 valid questionnaires remained. The structural model results showed that attitude, perceived behavior control, and the presence of other pedestrians directly influence behavior intention. As a result, when a pedestrian believes that breaking the law will save time or be a convenient way out, the chance of breaking the law increases. It also increases when they perceive more control over the behavior. Subjective norms and sex, on the other hand, have an indirect influence on intention. Since men feel more control over their behavior, they are more likely to violate it. The binary logit model results showed that the violation probability is higher among men than women. It is also more probable that pedestrians aged 25 to 29 violate the law more often at intersections. The existence of other pedestrians waiting for the green light can reduce the violation probability, and the presence of other violators can increase it. Increasing the speed of the approaching vehicle will decrease the violation probability. A comparison between the two models showed that SEM could describe the impact of psychological variables better, and BL could better define traffic and condition-related variables. On the other hand, the effect of other pedestrians on behavior is significant in both models.
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The effect of displacement on the spread of viruses is a very important issue that has been studied by different societies in order to identify the factors influencing the spread of the disease. The use of spatial statistical models is one of the most recent modeling of how the virus spreads in the regions of a city or country based on location data, and one of the most important features of these models is localization based on the relationship between data. The location of people infected with the virus and local explanatory variables affecting the spread of the virus. In this study, in the first step, among the 12 available explanatory variables, 1795 spatial models were built using the exploratory regression method, so based on that spatial dependence, collinearity of the explanatory variables are considered, and finally, 5 variables that have the least linear and spatial correlation And also, they had the most effect on the spread of the disease and were selected to continue the modeling. In the second step, in order to model the effect of the identified variables on the spread of the coronavirus, three location-based statistical models, geographically weighted regression (GWR) and geographically weighted Poisson regression (GWPR) were used. The results of the study showed that the amount of movement, the population of elderly people in each traffic area and then the number of cars per household has the greatest impact on the spread of the coronavirus. Also, by creating severe restrictions on travel in polluted traffic areas in order to cut off the chain of transmission to other areas, as well as reducing the amount of travel in other areas, such as virtual education in schools and universities and remote work in offices, it can have a significant impact in 6 months. to prevent the spread of the coronavirus. At the end of the study, 3 scenarios were proposed to prevent the spread of the virus in critical conditions for 22 areas of Tehran and 603 traffic areas.

Road accidents reduce traffic safety due to injuries and fatalities. Investigating and prioritizing factors contributing to road accidents have been based on deficient traditional ways as they do not consider the probability density of factors contributing to road accidents. Accordingly, an examination of accident road factors based on the probability density seems necessary. Thus, this paper first aimed at using principle components analysis (PCA) as a statistical prioritization tool for identifying the main and sub-main factors that contribute to injury severity on Borujerd-Khorramabad as a four-lane rural highway during the years 2015 to 2017. Secondly, the multivariate Gaussian probability model was used as a probabilistic density approach to estimate the probability density based on the relationship between factors that contributing to injury severity and the Pearson correlation. The results obtained through PCA indicated that factors contributing to injury severity were ranked in terms of Eigen values and rotated component matrix. Findings from the PCA model showed that OS, PSL, AADT, SL, R, and S, as 6 important factors affecting the accident occurrence relevant to injury severity. The results of the probability density also showed that the relation between operating speed with posted speed limits, and the relation among segment length, operating speed and radius are considerable due to increasing the probability density of accident occurrence. Moreover, AADT with operating speed and operating speed with slope and radius have significant effects on the probability density of occurring accidents. The results of the present study show that applying a multivariate Gaussian probability model helps to estimate the probability density of the accident occurrence of factors contributing to road accidents based on their values.

 The activity plans of individuals and their characteristics play an important role in transportation models and realated travel behaviors. Therefore, considering people individually without taking into account their role in the household and the effects of other household members on them reduces the accuracy and validity of transportation models. Therefore, in this paper, two separate models were developed, in one of which the individual was in the form of a household, and in the other, each household was considered as a decision-making unit. The data used in this paper was from a Washington household survey. The models were the multinomial logit in which the daily activity patterns of individuals were once considered to form a trip chain of individuals using the household activity plans and again for shared trips of households to calculate the utility of possible choice options for each individual and household and compare them with other options. The trip chain formation model was built with seven activity objectives of "work, education, companionship (escort), personal work (self-employment and virtual work with phone and internet), shopping, food, and social/recreational" and variables in categories of "type of individuals, age group, household income, household composition, gender/child," and the shared trip model was built with three types of activity patterns of "work, education, companionship (escort), personal work (self-employment and virtual work with phone and internet), This kind of behavior in the home and with other people will definitely have a big effect on how realistic the models are and, as a result, on progress and growth in the transportation planning.

jams and, as a result, long queues, increased travel time, increased chances of accidents, and environmental pollution. Several methods, such as ramp metering control, variable speed limit, lane change control, tolls, etc., have been studied and investigated to control highways. One of the approaches to improve the performance of these controls is their simultaneous and coordinated integration and implementation. In this paper, we will investigate the effect of the combination of three concurrent controls of ramp metering, variable speed limit, and highway tolls on the traffic conditions of the highway network. For this purpose, the reinforcement learning method has been used to manage the control values as variables. Traffic data is first predicted by predictive models based on neural networks and then given to the simulation of Sumo software. The controller model then selects the ramp rate, speed limit, and toll values based on the simulation outputs and applies them to the network. The results of implementing the proposed model show the significant impact of the model in improving traffic parameters . The model reduced the average travel time by 11.5% compared to the condition without controls, which is more than the effect of implementing single controls or a double combination of the mentioned methods. In addition, in the event of an accident in the network, the proposed management can significantly improve the network conditions compared to the situation without control.

Considering that the issue of choosing the passenger's route and the parameters involved in it has been the focus and study of transportation planners and policymakers for decades, in this article it has been tried to increase the computational efficiency and reduce the data processing time by examining and improving the mathematical model of passenger route selection in previous studies. This issue has been addressed through multi-core data processing (parallel processing) based on the modification of mathematical models presented in previous studies in the form of a public transport dynamic assignment model with the shortest path algorithm based on the schedule and the travel elimination sub-algorithm. The results were compared with the outputs of a non-dynamic model based on the shortest link-based algorithm to measure the effect of considering the capacity constraint and dynamics of the algorithm on the calculation time and the accuracy of the output. Even though the number of calculations went up by 13.7% compared to the basic model, the time it took to solve the problem went down by 20% because of parallel processing.

Traffic lights are important means of establishing order, safety and traffic flow control. The purpose of this research is to investigate the effect of intelligentization of intersection traffic lights on traffic flow indicators. For this purpose, the calibration of the Synchro intersection simulator software was done and then the conditions of the intersection before and after the intelligentization were simulated in it and the results were compared. No significant relationship was found between the improvement of delay time due to intelligentization of lights with cycle length, traffic volume and delay time (intelligent and nonintelligent lights). The lack of coordination and matching of the traffic light cycle with the volume of traffic was the main cause of the delay in the unintelligent mode. The maximum points of delay time improvement were in the places of cycle change in the non-smart light. The time improvement values were also proportional to the change value of the smart light cycle changes. The process of cycle changes in the smart light was proportional to the changes in delay time improvement. Latency improvement did not follow a normal distribution and tended towards lower numbers.

To make informed decisions about transportation infrastructure planning, planners and engineers must be able to anticipate the response to transportation demand by changing the characteristics of the transportation system and the characteristics of the people using the transportation system. For this purpose, travel demand models are used. The first generation of demand models are travel based models. Activity based models were developed due to insufficient accuracy of four-step models predicting. . In this study, in order to predict activity based travel demand, some software models were modified by models made in previous studies. According to the study area. in the next step, data on synthetic population, individuals, households, traffic zones, land use information, and impedance matrices between Washington, DC, areas were entered into the Activitysim software. in the next step, the travel information obtained from the Software was compared with the actual travel data in the study area and also with the number of trips of the first version of Actgen software. Comparisons of travel matrices indicate that the focus of travel according to the output of Activitysim software models is higher than the actual travel data, and the travel matrices of Actgen software provides more dispersion. also, in the number of trips based on travel mode and different trip destination, we see the proximity of the Activitysim models outputs and actual data. actual basic information about the distribution of travel by different age groups, income groups, household size, number of cars, gender distribution of the population and the approximate proximity of these data to the number of travel outputs of implemented software models show the accuracy of input information and has high analytical power of software models as well as modified models based on the study area.

After The Emergence of the COVID-19 Virus, Beyond the Inappropriate Aspects of the Contagion of the Virus, the Effects on the Global Society Have Been Appeared. Changes in Mental Health, Social Relations, Lifestyle, Transportation, Income, Living Motivation, Etc. Has Been Investigated in Various Researches. in this Study, the Effects of COVID-19 Contagion on Changes in Traffic Volume, Modes, Air Pollutions Such as Particulate Matter (PM), Sulphur Oxides (SOx), Carbon Monoxide (CO), and Nitrogen Oxides (NOx) For Karaj-Chalus Corridor, is Investigated by SPSS Using Pearson Correlation and Cross-Correlation, And Multivariate Regression. in Addition, the Effects of Control and Management Policies Against Corona Virus Contagion on Each Aforementioned Parameters Were Evaluated. the Results of this Study Showed That, During the Peak Period of the COVID-19 Contagion, the Traffic Volume is Accompanied with A Significant Reduction as Well as the Amount of Generated Pollutants. However, After A Short Time Elapsed Since the Peak of Both the Contagion and the Lockdown, Traffic Volume Increased Even More Than Pre-COVID Period. Moreover, the Analysis of Transport Mode Changes and The Heavy Vehicles' Pollution Showed That Traffic Restrictions and Fines for Non-Domestic Passengers, Despite Higher Risk of Infection, Augmented The Interest of Travelling with Suburban Buses. Cross-Correlation Analysis Showed After 20 Days of Entering Higher Amount of Vehicles to Mazandaran District, Covid Cases Increased Respectively.

One of the traditional ways to improve the condition of the networks is the construction of new roads, which is not a desirable option today, especially in crowded urban areas due to the high cost of construction. One of the effective options in this regard is traffic management methods. The issue of traffic assignment, especially dynamic traffic assignment by providing a platform for examining the status of the network under study by providing a traffic flow model is one of the important tools and components in the discussion of traffic management. In this study, using a complete and general dynamic network loading method based on LWR network expansion model, the dynamic network loading problem is formulated as a system of differential algebraic equations that the resulting dynamic network loading model is able to form, propagate and lose physical queue. In this research, an algorithm based on the fixed point formula has been used to solve dynamic user equilibrium problems. Then the software package algorithm in C ++ language is defined to implement both systems of differential algebraic equations and the fixed point algorithm by considering the queue spillback. The program is developed in such a way that it can be used to solve dynamic user balance problems and load dynamic network in any large-scale network defined by the user. The C ++ program was implemented for the Chicago metropolitan area network with 86,179 origin-destination pairs and 250,000 routes, and after 69 repetitions, the convergence threshold was reached and the results were evaluated. Finally, the results of this study are evaluated and the accuracy of the results is shown.

The solution to congestion is the balance of demand and supply side. Increasing network capacity with road construction and more infrastructure is expensive and environmentally damaging. With better utilization of existing infrastructure, road capacity can be increased. Different traffic management methods have been proposed to address the rapid growth of travel demand, which seem to be promising. Due to the reduction of human error due to faster reaction, Autonomous adopt less distance between vehicles, which can eventually increase capacity by reducing the space between vehicles. The ability of connecting to each other can also further contribute to the formation of Platooning. Increasing the number of AVs on roads around the world causes uncertainty about their impact on the capacity of roads with AV and non-AV vehicles and mostly faces many challenges. Therefore, in this study, by applying the characteristics of AV and non-AV vehicle models in the road network of west Tehran, related southern arterial routes by various methods, total of 825 simulations were performed. These simulations take into account the models of car following, lane change and Platooning, when they participate in the network with different share values with %10 intervals that normal vehicles constitute the entire traffic flow and in Each stage of the simulation contributes to AV cars, showing the Autonomous Vehicles effects that have on the capacity of the network passages, The results of the thesis show that the full penetration of Autonomous vehicles in Platoon can contribute more than %50 in the capacity of the network as well as increasing the amount of time headway for the free driving and Leader of the platoon vehicles, which will have a positive effect on the interaction of AVs in low penetration, and these promise to experts to observe its effects in reality.

The passengers’ expectations of road transportation systems' service quality lead transportation policy-makers to determine the technical requirements to meet these expectations. It means that Road trip Designs (RDs) as technical requirements should be translated based on Road users’ Requirements (RRs) as passengers’ expectations. We classified the RRs and RDs to 8 and 10 requirements, respectively. The Quality Function Deployment (QFD) method can translate the RRs to RDs in road transportation systems. On the one hand, due to the inherent uncertainty in decision-makers viewpoints in such systems, the Fuzzy QFD (FQFD) can be applied as a more accurate translation of RRs to RDs. On the other hand, the Evidential Reasoning (ER) approach, as one of the best evidence-based decision analysis methods, deals with the raised ambiguity and chaos by decision makers’ viewpoints. Accordingly, a novel hybrid FQFD-ER approach to prioritize the service quality criteria in road transportation systems has been provided in this paper. Moreover, a novel mathematical lemma is provided to hybridize and link the decision-making approaches of the FQFD and ER. Considering the weights of decision makers and fuzzy trapezoidal numbers to achieve the better results are other innovations of this paper. Totally, this study aims to integrate the FQFD with the ER approach to prioritize the RDs based on RRs in related road transportation systems to the Arba'een ceremony as a real case study. The obtained results by hybrid FQFD-ER revealed that the suitable road lanes in terms of width and number mostly improve the service quality.