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    Improving the Stability of an Urban Traffic Network with Limited Data by Using Percolation Theory and Dynamic Clustering

    , M.Sc. Thesis Sharif University of Technology Hassanzadeh, Ehsan (Author) ; Amini, Zahra (Supervisor)
    Abstract
    One of the most vital aspects of understanding the traffic phenomenon is scrutinizing the traffic transition status, such as the transition from free flow to congestion. The Percolation Theory is a renowned theory focusing on analyzing various network types to detect the critical zones, which are the zones including links that are important to control to improve stability. By calculating the quality indices of network links, the Percolation Theory can simulate the traffic percolation propagation in the network and determine possible critical zones for further analysis. Most studies in this field assume access to data of several traffic parameters for the entire transportation network, such... 

    Evaluating the Impact of Gasoline Price Change on the Passing Car Volume in the Provinces of Iran and Tehran and the Impact of CBD Entry Policy Change on the Passing Car Volume in Tehran

    , M.Sc. Thesis Sharif University of Technology Oshanreh, Mohammad Mehdi (Author) ; Amini, Zahra (Supervisor)
    Abstract
    Nowadays, various policies are adopted by transportation managers and planners. These policies aim to improve system performance, reduce user costs, control and reduce air pollution, reduce noise pollution, and ultimately reduce congestion. A set of these policies in the form of transportation demand management is presented in the literature. A common way to find the effect of a policy on user behavior is to use questionnaires. Other causal inference models have been proposed in disciplines such as statistics, political science, marketing science, epidemiology, and psychology. The purpose of these models is to find the causal effect of an intervention (treatment) on a system. These studies... 

    Using Simulation-Optimization Approach for Fire Station Location and Vehicle Assignment Problem: a Case Study in Tehran, Iran

    , M.Sc. Thesis Sharif University of Technology Pirmohammadi, Ali (Author) ; Amini, Zahra (Supervisor)
    Abstract
    In this research, the problem of locating fire stations and allocating equipment has been studied and a simulation-optimization approach has been presented to solve the problem. The mathematical models of this research were developed based on the idea of the randomness of the covered demand and the maximum expected coverage model. In these models, the issue of non-availability of equipment to cover accidents, the random nature of accidents, various fire incidents and the equipment needed to cover them are considered. Two mathematical models with deterministic and non-deterministic approach with different scenarios for demand are proposed. The non-deterministic model is developed with the aim... 

    Extraction and Processing Urban Data for Modeling Particulate Matter Concentrations in Tehran Using Probabilistic Neural Network

    , M.Sc. Thesis Sharif University of Technology Alaie, Ahmad Ali (Author) ; Arhami, Mohammad (Supervisor) ; Amini, Zahra (Co-Supervisor)
    Abstract
    The hourly concentrations of particulate matter in Tehran are modelled in this study. High levels of particles are one of the main air pollution challenges in this metropolis, especially in the colder seasons. A probabilistic neural network is used for modelling. The model uses Bayes' theorem which has a very high ability to tackle the complexities and uncertainties. Traffic, meteorology, land use, baseline concentration (at 5 am), vegetation, along with other data including the location of each station, time of recording each concentration data, area and population of the municipal district of each station are considered. This research introduced a cheap and accurate method for collecting... 

    Modeling Gaseous Air Pollutants Concentration in Tehran Using Artificial Neural Network and Land Use Regression

    , M.Sc. Thesis Sharif University of Technology Mirzaee, Mohsen (Author) ; Mohammad Arhami (Supervisor) ; Amini, Zahra (Co-Supervisor)
    Abstract
    In this thesis the hourly concentration of different gaseous air pollutants in Tehran is modeled using Land Use Regression (LUR) and Artificial Neural Network, separately. Both models are provided with the same set of input data; the first step is to find these data. Since traffic affects air pollution, information about traffic conditions is one of the main inputs in air pollution modeling. Therefore, to obtain traffic information, in this thesis, first a novel method is developed to extract and analyze Google Maps traffic data. In this method, image processing is used along with the Geographic Information System (GIS) to count the number of pixels of different traffic colors for each road... 

    Using Machine Learning to Predict the Behavior of Concrete Dams with Data of Monitoring

    , M.Sc. Thesis Sharif University of Technology Momeni Golshad, Mohamad Reza (Author) ; Ghaemian, Mohsen (Supervisor) ; Amini, Zahra (Supervisor)
    Abstract
    Karun 4 arch concrete dam with a height of 230 meters is designed and built in one of the most complex natural places. Therefore, monitoring the behavior and evaluating the safety and stability of this dam, both with regard to the height and nature of the dam itself and with regard to its national and international dimensions, is of special importance and sensitivity. This sensitivity is doubled due to some problems and issues encountered in the body of the Karun 4 dam, which of course are considered probable and possible phenomena in arch concrete dams. The Karun 4 Dam construction site is located in the southwest of Iran on the Karun River, immediately upstream of the Karun 3 Dam reservoir...