Prediction of Passenger Demand Using Artificial Neural Network Model (Case Study: Tehran BRT Line 10)

Document Type : Original Article

Authors
Mohammad Amin Ebrahimzadeh 1
Hassan Javanshir 2
Abbas Alikhani 3
1 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 Assistant Professor, Faculty of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
3 M.Sc., Grad., Department of Civil Engineering, University of Tehran South, Tehran, Iran.
10.22034/road.2024.415667.2197
Abstract
Public transportation is of particular importance as a heavy system of passenger movement, hence the damage to this system is of great importance either financially or technically. In this research, using the data prepared from the bus company, passenger prediction is discussed. Forecasting is a critical component of transportation systems that can be used to adjust travel behaviors, reduce passenger traffic, and increase the service quality of transportation systems. In this research, the prediction of the number of passengers has been investigated using the neural network method. The prediction method based on the neural network model can be summarized as follows: After examining the data and performing a series of possible tests, the type of hyperbolic tangent function and the number of 6 neurons were determined. Between 7:30 and 8:30 at this time, because people and students go to their workplaces or studies, it has the largest number of passengers. The second peak in the graph, which is shorter, is between 5:30 and 6:30. The neural model with an error of 5% has a good prediction ability and for different stations, the prediction accuracy was 92%. The results show that the neural network is successful in predicting the number of passengers.
Keywords
Neural Network
Bus
Prediction
Passenger
Demand
 -Cui, X., Gao, J., & Wang, Y. (2010). Research of Bus Rapid Transportation based on “Public transportation first”. In 2010 International Conference on Future Information Technology and Management Engineering, IEEE.  Vol. 1, 174-177.
-Dong, X., Xiong, G., Fan, D., Zhu, F., & Lv. Y. (2011). Research on bus rapid transit (BRT) and its real-time scheduling. In Proceedings of 2011 IEEE International Conference on Service Operations, Logistics and Informatics, IEEE.  342-346.
-Firozbakht, E., Ebrahimzadeh, M. A., & Koulivand, H. (2025). Optimizing the Placement of Bus Stations in Urban Transportation Using Neural Network Algorithms to Minimize Traffic Delays. Road.
-Gong, Z., Du, B., Liu, Z., Zeng, W., Perez, P., & Wu, K. (2020). SD-seq2seq: a deep learning model forbus bunching prediction based on smart card data. Paper presented at the 2020 29th International Conference on Computer Communications and Networks (ICCCN).
-Hook, W., & Wright, L. (2007). Bus rapid transit: planning guide (Institute for Transportation and Development Policy: ITDP).
-Levinson, H. S., Zimmerman, S., Clinger, J., & Rutherford, H. C. S. (2002). Bus rapid transit: An overview. Journal of Public Transportation, 5(2), 1-30.
-Lin, D. Y., & Ku, Y. H. (2014). Using genetic algorithms to optimize stopping patterns for passenger rail transportation. Computer‐Aided Civil and Infrastructure Engineering, 29(4), 264-278.
-Liyanage, S., Abduljabbar, R., Dia, H., & Tsai, P. W. (2022). AI-based neural network models for bus passenger demand forecasting using smart card data. Journal of Urban Management, 11(3), 365-380.
-Sun, W. X., Song, T., & Zhong, H. (2009, April). Study on bus passenger capacity forecast based on regression analysis including time series. In 2009 International Conference on Measuring Technology and Mechatronics Automation, IEEE.  Vol. 2, 381-384.
-Zhang, J., Shen, D., Tu, L., Zhang, F., Xu, C., Wang, Y. & Li, Z. (2017). A real-time passenger flow estimation and prediction method for urban bus transit systems. IEEE Transactions on Intelligent Transportation Systems, 18(11), 3168-3178.
-Zhao, S. Z., Ni, T. H., Wang, Y., & Gao. X. T. (2011). A new approach to the prediction of passenger flow in a transit system. Computers & Mathematics with Applications, 61(8),1968-1974.
-Yang, Y., Yuan, Z. Z., Li, J. Y., Wang, Y. H., & Wang, W. C. (2018). Multi-mode public transit OD prediction and scheduling model. Advances in Transportation Studies, 3(Special Issue 2018).