Short-term prediction of bus passenger demand, case study: Karimkhan bridge- Jomhoori square line

Document Type : Scientific - Research

Authors

1 Tarbiat Modares

2 Tarbiat Modares University, Tehran, Iran

3 Civil and Environmental Engineering Dept. Tarbiat Modares University

4 Electrical and Computer Engineering Dept. Tarbiat Modares University

Abstract

In recent decades, increasing urban population, urbanization development and the lack of public transportation in response to increasing demand for inter-city trips have led to an increase in the use of private cars in Tehran. Therefore, changes in urban transport policies and efforts to develop and improve public transportation, especially the bus, are one of the most important strategies in the urban transport planning and require the necessary surveys to have a growth in share of public transportation. Studies show that planning for the optimal use of bus infrastructure and the use of solutions to increase the efficiency of this system in the world, requires information on infrastructure and bus lines demand. Accordingly, it is necessary to carry out studies to predict bus lines passenger demand in Tehran, for operational lines planning which are currently empirically carried out by the line manager and there is no planning based on the existing conditions for the future. Thus, this study predicts bus lines passenger demand for the short-term future, using AFC data (data recorded from smart credit card transactions for fares payment) and calibration of a suitable model. For this purpose, AFC information is firstly arranged for each bus line, then Sarima and neural network model (multi- layer perceptron) are calibrated based on. The model results show that the multi- layer perceptron model in terms of error indices in prediction of bus passenger demand is better than Sarima model and it is a more appropriate method for predicting short-term bus lines passenger demand.

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References

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History

  • Receive Date: 09 August 2017
  • Revise Date: 26 December 2017
  • Accept Date: 27 December 2017

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