Open Access
| Issue |
E3S Web Conf.
Volume 723, 2026
2026 International Conference on Artificial Intelligence in Energy and Infrastructure (AIEI 2026)
|
|
|---|---|---|
| Article Number | 01011 | |
| Number of page(s) | 6 | |
| Section | Artificial Intelligence, Machine Learning & Data Analytics | |
| DOI | https://doi.org/10.1051/e3sconf/202672301011 | |
| Published online | 08 July 2026 | |
- D. Elliott, M. Tomasini, M. Oliveira, and R. Menezes, “Tippers and stiffers: An analysis of tipping behavior in taxi trips,” in Proc. IEEE Int. Conf. Data Mining Workshops (ICDMW), New Orleans, LA, USA, 2017, pp. 934–941. [Google Scholar]
- M. Lynn and M. McCall, “Gratitude and gratuity: A meta-analysis of the service-tipping relationship,” Journal of Socio-Economics, vol. 29, no. 2, pp. 203–214, 2000. [Google Scholar]
- D. Soman, “The effect of payment transparency on consumption: Quasi-experiments from the field,” Marketing Letters, vol. 14, no. 3, pp. 173– 183, 2003. [Google Scholar]
- X. Zhao, X. Yan, A. Yu, and P. Van Hentenryck, “Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models,” Travel Behaviour and Society, vol. 20, pp. 22– 35, 2020. [Google Scholar]
- M. T. Kashifi, A. Jamal, M. S. Kashefi, M. Almoshaogeh, and S. M. Rahman, “Predicting the travel mode choice with interpretable machine learning techniques: A comparative study,” Travel Behaviour and Society, vol. 29, pp. 279–296, 2022. [Google Scholar]
- S. Mullainathan and J. Spiess, “Machine learning: An applied econometric approach,” Journal of Economic Perspectives, vol. 31, no. 2, pp. 87–106, 2017. [Google Scholar]
- R. Shwartz-Ziv and A. Armon, “Tabular data: Deep learning is not all you need,” Information Fusion, vol. 81, pp. 84–90, 2022. [Google Scholar]
- T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 785–794. [Google Scholar]
- G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “LightGBM: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 3146–3154. [Google Scholar]
- L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “CatBoost: Unbiased boosting with categorical features,” in Advances in Neural Information Processing Systems, vol. 31, 2018, pp. 6639–6649. [Google Scholar]
- Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko, “Revisiting deep learning models for tabular data,” in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 18932–18943. [Google Scholar]
- L. Grinsztajn, E. Oyallon, and G. Varoquaux, “Why do tree-based models still outperform deep learning on typical tabular data?” in Advances in Neural Information Processing Systems, vol. 35, pp. 507–520, 2022. [Google Scholar]
- S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems, vol. 30, 2017. [Google Scholar]
- X. Kong, Y. Zhang, W. L. Eisele, and X. Xiao, “Using an interpretable machine learning framework to understand the relationship of mobility and reliability indices on truck drivers’ route choices,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 13419–13428, 2022. [Google Scholar]
- M. A. Mansournia, M. Nazemipour, A. I. Naimi, and D. G. Altman, “Reflection on modern methods: Demystifying robust standard errors for epidemiologists,” International Journal of Epidemiology, vol. 50, no. 1, pp. 346–351, 2021. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

