After 1860’ s the railway systems has started to be used in urban transportation in the world. However, especially afterthe 1950’ s, automobile usage has been increased quickly. Thus the dense of transportation has started to move fromrailway systems to the ones with rubber tire vehicles in Istanbul. Traffic density in urban transportation has startedto be experienced since 1950 with the increase in the automobile usage and other rubber tire vehicles. The length ofrailway systems in Istanbul, which reached to 130 kilometers till the year of 1950, became null with a continuousdecrease in 1966. However, the priority in the developed countries in the same period of time was given to the masstransportation systems, specifically to the railway ones, that were more comfortable, more secure and more economic(Ocak and Manisali, 2006).

Delay is one of the most important problems of railway transportation systems, which has a lot of material and spiritual costs. Loss of PASSENGER trust, waste of PASSENGERs' time, blockage in the onelane system, depreciation of diesel, compensation to PASSENGERs, loss of fuel, etc. are just some of the costs incurred in the rail transportation system. No studies have been conducted in Iran on the cost of train delays. In the railway company of the Islamic Republic of Iran, billions of Tomans are imposed annually due to train delays, the amount of which are currently unknown and cannot be calculated. It is also possible to have a better sensitivity and understanding of delays when the amount of damage imposed on the system is clear and measurable. Therefore, in this research, first all the factors and sub-factors of delay are identified and then the separation and classification of all factors and sub-factors of delay along with their relationships are given in a schematic model and then using scientific and accurate costing, the amount of damage Each of the factors of delay in the PASSENGER trains of the railways of the Islamic Republic of Iran, it should be specified that using this costing can be ranked different factors, the amount of damage to the railway company in different time periods and with Attention to the sensitivity that arises to the types of delays in order to improve, reduce and eliminate delays, as well as optimal investment management.

The PASSENGER line planning is a process of strategic long-term decision-making problem in the field of railway PASSENGER planning. A line is a direct railway connection between two stations. A line is characterized by the origin and destination stations, the frequency per day, the route between these two stations, and the intermediate stops at passing railway stations. Various mathematical models have been proposed for PASSENGER line planning problem, and different solution methods have been provided so far. Two solution methods based on column generation algorithm and genetic algorithm have been proposed in this article, the first alghorithm is defined in one master problem and two sub problems. Since the solution provided by column generation method is not of the integer number type, a heuristic algoadrithm has been proposed here for converting the results to integer numbers. The objective function for line planning problem in this article is to maximize the number of direct PASSENGERs. Direct PASSENGERs are the PASSENGERs that can travel from their origin station to their destination station without having to change trains. Results on the proposed solution methods, for twirty two test problems, are compared to those of solutions obtained via CPLEX software, one of the well-known solvers for solving both linear and mix integer programming problems using branch and bound algorithm. Results show that the proposed solution methods have high performance and accuracy.

One of the major issues in railways is PASSENGER train delays and this issue in railway has many reasons, therefore predicting PASSENGER train delay is a very difficult task. The aim of this paper is to present an artificial neural network based model with high accuracy to predict the delays of PASSENGER trains in the Islamic Republic of Iran Railway. In the proposed model three different methods to define inputs, including normalized real number, binary coding and binary set encoding inputs have been used. To find an appropriate structure for proposed neural network model, three different strategies, called quick, dynamic and multiple are investigated. In this research, the registered data of PASSENGER train delays in Iranian railway within the period (2004-2009) have been used. To eliminate any inconsistent and noisy data which always company with real world data set, a comprehensive preprocessing on this data set was done. To get more knowledge about data, some graphs such as seasonal average of delays, monthly average of delays, and total delays since 1383 to 1387 per year were sketched. To prevent models from over fitting with training data specifications, according to cross validation, the existing PASSENGER train delays data set were divided into three subsets called training set, validation set and testing set, respectively. To evaluate the proposed model, the result of three different data input methods and three different structures were compared to each other and also to some common prediction methods such as decision tree and multinomial logistic regression. In comparison, different neural networks, training time, accuracy of neural network on testing data set and network size were considered and to compare neural networks with other well-known prediction methods, training time and accuracy of neural network on testing data set were considered and compared. To do a fair comparison among all models, a time-accuracy graph was sketched. The results revealed the higher accuracy of the proposed model.

In this research entitled noise standards for PASSENGER cars in Iran, in order to identity the permissible levels of sound for passing-by noise and the noise from vehicle exhausts and horns, through a step processing method, at first the real current situation of vehicles was obtained through different tests carried out on 100 vehicles of different types. The results have been analyzed statistically using the ATATGRAPHICS PLUS Version 2.1 software. The second step has been the study of other countries' standards and regulations as well as the European Commission Directives 70/15/EEC and 70/388/EEC which have formed the basis of this study.According to the test results analysed and the Directives mentioned above, the permissible sound levels that have been identified are as follows: 77 dB (A) for passing-by noise of the Ml class; 80 dB (A) for the NI class; 86 dB (A) for exhaust noise and 87 dB (A) TO 112 dB (A) for horn noise for all types of vehicle.

In this article, the demand for air travel is analyzed. By using a model, the impact of geographical, socioeconomic, and competitive aspects has been investigated while studying PASSENGER travel demand. To this end, departure data on at Kerman airport have been gathered during the period of 2011 to 2020. First, the demand is forecasted by using an economic model. In this model the importance of significant differences of all variables is examined. Therefore, an entirely new set of data is produced and the minor variables have been removed. The K-Means clustering algorithm is then used to analyze this data, after which it is used as training data for neural network learning. The neural network used for this analysis is an LSTM Deep Learning Network, which has been used to forecast PASSENGER demand for future coming years. Finally, with economic and social variables including GDP, income, population, inflation, exchange rate, gasoline prices and oil prices for the coming years, the percentage change in the number of PASSENGERs for each year compared to the previous year has been predicted. Based on the outputs of the neural network, changes in air travel demand are determined based on the variables of gross national product, mean income, gasoline price, and oil price inflation for each specific time. Among all these variables, the most important variable is GDP, which has a significant influence on air travel demand. The accuracy obtained in this method is 83%, which is a very good accuracy level for air travel demand compared to other regression methods.

The aim of this paper is to predict PASSENGER train delays in Iranian Railways using data mining techniques. The results of this research are used to design train timetables. The data used in this research includes a database of PASSENGER train delays from year 2013 to 2018, including 380,748 records. Independent variables for prediction model include year, month, day, day of the week, departure time, axis, train type, car type, origin and destination of the train and the train owner.  In order to model prediction of train delay, two kind of prediction, named Numerical and Classification are used on entire database. Neural network and C5.0 methods are used for classification prediction. The Twostep clustering method is used to divide the delay field into three labels. Regression, CHAID and neural network methods are used for numerical prediction. To evaluate prediction results, we divide existing data set into two subsets called training set and test set, in which delays from year 2013 to 2016 are the training set and delays of year 2017 are the test set. By evaluating the prediction methods, the results show that in numerical prediction, neural network method and in prediction by classification, C5.0 method has higher accuracy than other methods. Therefore, these two techniques have been used to predict the train delays of year 2018. Numerical prediction is used by grouping some database fields. The results show that the prediction by grouping has higher accuracy than the prediction on the entire database.