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Nowadays, cities as a place of living and human activity are facing serious challenges in providing human needs. Increasing in population growth, vehicle ownership and communication development has led to complexity of the transportation system and its problems, including congestion, environmental pollution and the consumption of non-renewable resources. Therefore, changes in urban transport policies and efforts to develop and more use of the public transport, especially the bus, are one of the most important concerns in urban transport planning. A review of various studies suggests that planning for efficient use of bus infrastructures and enhancing the efficiency of public transportation operation in the world, require information on the infrastructure and passenger demand for lines and bus stations. Accordingly, it is necessary to carry out studies to predict passenger demand for bus stations in Tehran. Thus, this study predicts bus stations passenger demand for future short-term periods, using data gathered by AFC (Automated Fare Collection) and AVL (Automatic Vehicle Location). For this purpose, firstly AFC and AVL data was sorted according to the time for each bus line. Since passengers use their smart card while they are getting off the bus it means at the exit station thus identifying their origin station is vital, so that in second step, data of two data bases is compared and matched by writing computer code in Matlab software to determine the origin stations of passengers and then forming origin-destination demand matrix for each bus line in terms of its stations. This matrix is considered as the main data base of the study, a time series analysis, a seasonal autoregressive integrated moving average (SARIMA) and neural network as an artificial model are calibrated based on the available data. Both models’ goodness of fit indices are compared in terms of learning and generalization capabilities. For this purpose, initial data is divided into two subsets called learning and test data sets and comparison indices are computed for both aforementioned sets. The models’ results show that the multi-layer perceptron neural network model in terms of goodness of fit indices in both learning and generalization capabilities in prediction of bus station passenger demand is better than SARIMA model; however, the manner of influencing different factors such as day of week or month of year in passenger demand in each station is more clear in time series analysis. The passenger demand for each stations in first month in spring is different from the rest months in this season. Months in summer is also show different trends for passenger demand, while all months in fall and the first two months in winter have similar passenger demand in various stations. Official holidays has also significant influence on passenger demand so that reduce passenger demand by approximately 256 persons on average. All days in week have meaningful effects on passenger demand in comparison with Friday so that Monday and Thursday have the highest and the lowest effect on weekday passenger demand in bus stations in comparison with Friday, respectively. This analysis comparison show that if the precision of future prediction is important then neural network outweigh time series regression, while the identification of influential variables on passenger demand is better done by time series analysis.

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One of the information needed for all planning problems and specifically transportation planning is to have accurate prediction about the future. Traffic variables prediction is one of the efficient tools in travel demand management. Using this tool and advanced traveler information systems (ATIS), the predicted traffic variables are informed to the users and transportation system operators to make plans and set policies. In this study, the average speed and traffic volume of the Karaj to Chalus suburban road with the high variation of traffic variables in the north of Iran is predicted. The Karaj to Chalous road is part of the route from Tehran as the capital of Iran to the country's northern coast. Along the Karaj to Chalous road, three parallel roads, with different lengths, connect Tehran with the cities of the north. In general, finding the pattern of non-mandatory trips is more complicated than mandatory trips. Generally, the predictive methods are divided into three groups, naïve, parametric and non-parametric methods. Among the various predictive models, the SARIMA as a parametric model and the artificial neural network and the support vector machine as nonparametric models are employed. In the data pre-processing step, the variables affecting the average speed and traffic volume are extracted and added to the dataset as predictor variables. These variables are related to time, calendar, holidays, weather, and roads blockage. Also, because of the importance of the maximum and minimum values of traffic speed and volume, as critical values and rare events, models are evaluated with emphasis on the prediction of rare events compared to normal values. The results show that, for the test data, the lowest root mean square error of predicting the average traffic speed and traffic volume are obtained using artificial neural network and support vector machine models equals 139 vehicles per hour and 5 kilometers per hour, respectively. In terms of R² of prediction-observation plot, the performance of SARIMA for predicting the average speed and traffic volume is the same for the test dataset. In contrast the R² of hourly traffic volume prediction is higher for the training data. The R² of artificial neural network model and the support vector machine for traffic volume prediction is higher than traffic speed prediction. The lowest root mean square error of predicting the first and fourth quartile of the observed average traffic speed values is obtained by support vector machine models and artificial neural network, respectively. Also, predicting the first quartile and fourth quartile of the observed traffic volume values by the support vector machine model is more accurate than two other models. Using predicted traffic parameters and providing them to travelers and transportation agencies by intelligent transportation systems leads to make a balance between travel demand and travel supply in the near future which is the main aim of this study. Travelers can have a better personal plan for their future trips based on these predictions. Also, the transportation agencies are more prepared to deal with critical traffic situations and can prevent traffic congestion.