Public Transportation
In recent decades, the increase in the urban population and the development of urbanization on the one hand and the lack of public transportation in response to the increasing demand for intra-city trips on the other hand, have led to an increase in the use of private cars in Tehran. Therefore, the change in urban transportation policies and efforts to develop public transportation, especially buses, are one of the most important measures in the field of urban transportation and require necessary investigations to develop and make this mode of travel more efficient.
Surveys show that planning for the optimal use of bus infrastructure and using strategies to increase the efficiency of this system in the world requires information on the infrastructure and travel demand of bus stations. Based on this, it is necessary and necessary to carry out studies to estimate the number of passengers at bus stations for the purpose of operational planning of bus lines in Tehran, which is currently experimentally conducted by the line manager and there is no planning based on existing conditions for the future.
Thus, in this project, using AFC information (recorded data from smart ticket card transactions for fare payment) and AVL (data recorded by the automatic locator in buses) available in the bus system of Tehran, the prediction software The number of passengers of bus stations was designed for the short term future. For this purpose, first, the AFC and AVL information related to each bus line is entered into the software in a suitable format, and in the next step, the information of the two AFC and AVL systems is applied to determine the passenger origin-destination matrix (passenger boarding and boarding location). After preparing the origin-destination matrix of the passenger, this matrix is considered as the database of the study, and based on that, the appropriate neural network model (multi-layer perceptron) is paid. By building the model, it is possible to receive two types of output from the software, predicting the number of passengers for a specific day in each line and predicting the number of passengers for a specific day at the stations of each line. It should be noted that this software has the ability to analyze the existing information, and it is also possible to update the software according to the changes in the bus network and the future needs of the city.
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