Prediction and Monitoring of Ton-Kilometers and Waybill for Detecting Abnormal Behavior

Document Type : Original Article


1 Master's degree, Department of Industrial Engineering, Technical and Engineering Faculty, Research Science Unit, Islamic Azad University, Tehran, Iran.

2 Professor, Department of Industrial Engineering, Technical and Engineering Faculty, Research Science Unit, Islamic Azad University, Tehran, Iran

3 Assistant Professor, Department of Industrial Engineering, Technical and Engineering Faculty, Research Science Unit, Islamic Azad University, Tehran, Iran


The objective of this research is to predict and monitor the ton-kilometers and waybill on the Iran's roads to find abnormal Behavior. In this study, data was collected from monthly observations over a period of 6 years (1395 to 1400) by the Iran Road Maintenance and Transportation Organization, categorized by province. Different machine learning techniques, deep learning, and time series methods were employed to predict ton-kilometers, and the results were monitored for abnormal behavior following an increase in interest rates and taxes. For model implementation, a dataset of 72 records of ton-kilometers and 72 records of issued waybill, collected from 32 provinces over six years of road transportation, was utilized. Initially, four different prediction methods, including random forest, LSTM neural network, ARIMA, and ETS, were extensively examined. The empirical results indicate that the random forest outperforms the other models. this study employs the statistical quality control tool, the z-score, to detect outliers and abnormal behavior in the data. The empirical findings reveal that out of the 32 provinces, three provinces exhibit abnormal behavior, and one of them is attributed to factors other than an increase in interest rates and transportation taxes.


Main Subjects

  1. Akaike H. (1987). Factor analysis and AIC. Psychometrika, 52(3), 317–32.
  2. Bergstra J. BY. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb), 281-305.
  3. Box, G.E.P., & Jenkins GM. (1976). Time Series Analysis: Forecasting and Control. Holden Day, San Fr. (1970)
  4. Chikodili, N.B., Abdulmalik, M.D., Abisoye, O.A., & Bashir, SA. (2021). Outlier Detection in Multivariate Time Series Data Using a Fusion of K-Medoid, Standardized Euclidean Distance and Z-Score. Commun Comput Inf Sci [Internet]. [cited 2023 Jun 19], 1350, 259–71.
  5. Farazmand, M., Pishvaee, M. S. (2018). Multimodal Transportation Network Design Model under Uncertainty Conditions (Case Study: Cement Transportation in Iran). The Journal of Industrial Management Perspective, 8(3), 115-139. (In Persian)
  6. Fildes, R., Harvey A.C., West, M., & Harrison, J. (1991). Forecasting, Structural Time Series Models and the Kalman Filter. The Journal of the Operational Research Society, 42, 1031
  7. Fox, J. (1995). Applied Regression Analysis and Generalized Linear Models. Sage Publ [Internet]. 2015, 1–817.
  8. Ho, TK. (1995). Random Decision Forests. Proc third Int Conf Doc Anal Recognit 278–282.
  9. Hyndman, R.J., & Koehler, A.B. (2006). Another look at measures of forecast accuracy. Int J Forecast, 22(4), 679–88.
  10. Hyndman, R.J., Koehler, A.B., Snyder, R.D., & Grose S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. Int J Forecast, 18(3), 439–54.
  11. Khan, M.Z., Khan, F.N. (2020). Estimating the demand for rail freight transport in Pakistan: A time series analysis. J Rail Transp Plan Manag [Internet], 14(December 2019),
  12. Kingma, D.P., & Ba, JL. (2015). Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings.
  13. Mansourianfar, M.H., Haghshenas, H. (2018). Micro-scale sustainability assessment of infrastructure projects on urban transportation systems: Case study of Azadi district, Isfahan, Iran. Cities, 72, 149–59. (In Persian)
  14. Mohaghar, A., Heydarzadeh Moghaddam, H., Ghasemi, R. (2023). Developing a Model to Optimize Maximum Coverage of Roadside Units Placement in Vehicular Ad–hoc Network for Intelligent Transportation System. The Journal of Industrial Management Perspective, 13(2), 211-240. (In Persian)
  15. Nikbakhsh, E., Zegordi, S. H. (2014). Hub Arc Covering Location Problem under Disruption. The Journal of Industrial Management Perspective, 4(1), 9-29.
  16. Ord, J.K., Ord, J.K., Koehler, A.B., & Snyder, R.D. (1997). Estimation and prediction for a class of dynamic nonlinear statistical models. J Am Stat Assoc 92(440), 1621–9.
  17. Patil, G.R., & Sahu, P.K. (2016). Estimation of freight demand at Mumbai Port using regression and time series models. KSCE J Civ Eng., 20(5, 2022–32.
  18. Rob, J. Hyndman, Anne B. Koehler, Keith Ord. & Ralph Snyder. (2008). Forecasting with Exponential Smoothing: The State Space Approach Springer Series in Statistics [Internet]. Vasa. [cited 2023 Feb 19].
  19. Sehgal, S., Suman, S., Patel, J., Chauhan, D.S., Singh, N.K., & Singh, R.K. (2017). Gross ton-kilometer forecasting models for freight trains of Northern-central Indian railways. 2017 Int Electr Eng Congr iEECON 2017, (March), 8–10.
  20. Zoghi, H., and Alipourvavossari, M. (2012). Model for Increasing Freight Transportation Price per Ton-Kilometer in Road Transport after Implementing Targeted Subsidy Plan. 9th Int Civ Eng Congr.
  • Receive Date: 04 September 2023
  • Revise Date: 13 October 2023
  • Accept Date: 21 October 2023
  • First Publish Date: 15 November 2023
  • Publish Date: 22 December 2023