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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Diversified gradient boosting ensembles for prediction of the cost of forwarding contracts

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DOI: http://dx.doi.org/10.15439/2022F291

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 431436 ()

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Abstract. A common business practice for transportation forwardersis to bid for shipping contracts at the transport or freight exchanges. Based on the detailed contract requirements they try to estimate the total expected cost of its execution and accordingly bid with the fixed price in advance for delivering such shipping service at the prescribed specification and schedule. The capability to accurately predict the cost of contract execution is the critical factor deciding about the profitability of offered shipping services as well as the amount of business drawn from freight exchanges. However, given highly volatile nature of the transport services ecosystem, it is difficult to simultaneously account for countless dynamically changing factors like fuel prices, currency exchange rates, temporal and spatial multitude of routing and implied traffic risks, the properties of cargo and shipping vehicles etc., which leads to big cost under- or over-estimation resulting with loss-making contracts or equally painful missed revenue opportunities. In the context of FedCSIS 2022 data mining competition we propose an accurate and robust predictor of the cost of forwarding contracts built upon the detailed contract data using the ensemble of the state-of-the-art gradient boosting-based regression models. Our established feature engineering framework combined with deep parametric optimization of the individual models and multi-faceted diversification techniques guiding hybrid final model ensembles were instrumental to outperform all the competitive predictors and win the FedCSIS 2022 contest.


  1. A. Janusz, A. Jamiołkowski, M. Okulewicz, Predicting the Costs of Forwarding Contracts: Analysis of Data Mining Competition Results, Proceedings of the 17th Conference on Computer Science and Intelligent Systems (FedCSIS), 2022.
  2. Z. Li, K. Zhang, B. Chen, Y. Dong and L. Zhang, Driver identification in intelligent vehicle systems using machine learning algorithms, IET Intell. Transp. Syst., vol. 13, no. 1, pp. 40-47, 2019.
  3. S. Bhattacharya. Novel approach for Ai based driver behavior analysis model using visual and cognitive data, 2019.
  4. S. Kikuchi, R. Nanda and V. Perincherry. A method to estimate trip O-D patterns using a neural network approach. Transp. Planning Technol. 17(1): 51-65, 1993.
  5. A. Pozarycki. Pavement diagnosis accuracy with controlled application of artificial neural network, The Baltic Journal of Road and Bridge Engineering 10(4): 355-364, 2015.
  6. M. Bielli, G. Ambrosino, M. Boero and M. Mastretta. Artificial intelligence techniques for urban traffic control. Transportation Research Part A: General 25(5):319-325, 1991.
  7. M. Ghanin and G. Abu-Lebdeh. Projected state-wide traffic forecast parameters using artificial neural networks. IET Intel. Transp. Syst. 13(4):661-669, 2019.
  8. J. Lu, L. Feng, J. Yang, M. Hassan, A. Alelaiwi and I. Humar, Artificial agent: The fusion of artificial intelligence and a mobile agent for energy-efficient traffic control in wireless sensor networks, Future Gener. Comput. Syst. 95:45-51, 2019.
  9. Y. Kayikci. A conceptual model for intermodal freight logistics centre location decisions. Proc. Soc. Behav. Sci. 2(3): 6297-6311, 2010.
  10. F. Saadaoui, H. Saadaoui and H. Rabbouch. Hybrid feedforward ANN with NLS-based regression curve fitting for US air traffic forecasting. Neural Computing and Appl. 32:10073-10085, 2019.
  11. J. George, A. Cyril, B. Koshy and L. Mary. Exploring sound signature for vehicle detection and classification using ANN. Int. J. Soft Comput. 4(2):29-36, 2013.
  12. H. Lin, R. Zito and M. Taylor. A review of travel-time prediction in transport and logistics. Proc. Eastern Asia Soc. Transp. Stud. 5:1433-1448, 2005.
  13. H. Kirby and G. Parker. The development of traffic and transport applications of artificial intelligence: An overview. Artificial Intelligence Applications to Traffic Engineering, The Netherlands:VSP, pp. 3-27, 1994
  14. I.C. Bilegan, T.G. Crainic and M. Gendreau. Forecasting freight demand at intermodal terminals using neural networks–an integrated framework. Eur. J. Oper. Res 13(1):22-36, 2008.
  15. K. Kumar, M. Parida and V. Katiyar. Short term traffic flow prediction for a non urban highway using artificial neural network. Proc. Social Behav. Sci. 104:755-764, 2013.
  16. A. Singh, A. Das, U.K. Bera and G.M. Lee. Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks. IEEE Access 9:103497-103512, 2021.
  17. S. Nataraj, C. Alvareza, L. Sadaa, A. Juana, J. Panaderoa and C. Bayliss. Applying Statistical Learning Methods for Forecasting Prices and Enhancing the Probability of Success in Logistics Tenders. Transportation Research Procedia (Elsevier) 47:529–536, 2020.
  18. K. Tsolaki, T. Vafeiadis, A.N. Dimosthenis and I.D. Tzovaras. Utilizing machine learning on freight transportation and logistics applications: A review. ICT Express, 2022.
  19. L. Mason, J. Baxter, P.L. Bartlett, and M. Frean. Boosting Algorithms as Gradient Descent In S.A. Solla and T.K. Leen and K. Müller. Advances in Neural Information Processing Systems 12: 512–518, MIT Press, 1999.
  20. J.H. Friedman. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29(5): 1189-1232, 2001.