Abstract
The problem of travel time estimation is widely considered as the fundamental challenge of modern logistics. The complex nature of interconnections between spatial aspects of roads and temporal dynamics of ground transport still preserves an area to experiment with. However, the total volume of currently accumulated data encourages the construction of the learning models which have the perspective to significantly outperform earlier solutions. In order to address the problems of travel time estimation, we propose a new method based on transformer architecture – TransTTE.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The full data could be requested from semenova.bnl@gmail.com.
References
Asghari, M., Emrich, T., Demiryurek, U., Shahabi, C.: Probabilistic estimation of link travel times in dynamic road networks, pp. 1–10 (2015)
Jin, G., Wang, M., Zhang, J., Sha, H., Huang, J.: STGNN-TTE: travel time estimation via spatial-temporal graph neural network. Future Gener. Comput. Syst. 126, 70–81 (2022). https://www.sciencedirect.com/science/article/pii/S0167739X21002740
Li, Y., Fu, K., Wang, Z., Shahabi, C., Ye, J., Liu, Y.: Multi-task representation learning for travel time estimation. In: International Conference on Knowledge Discovery and Data Mining (KDD 2018) (2018)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization (2019)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Porvatov, V., Semenova, N., Chertok, A.: Hybrid graph embedding techniques in estimated time of arrival task. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds.) Complex Networks & Their Applications X. SCI, vol. 1016, pp. 575–586. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-93413-2_48
Wang, Z., Fu, K., Ye, J.: Learning to estimate the travel time. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 858–866. KDD 2018, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219900
Ying, C., et al.: Do transformers really perform bad for graph representation? arXiv preprint arXiv:2106.05234 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Semenova, N., Porvatov, V., Tishin, V., Sosedka, A., Zamkovoy, V. (2023). Logistics, Graphs, and Transformers: Towards Improving Travel Time Estimation. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13718. Springer, Cham. https://doi.org/10.1007/978-3-031-26422-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-031-26422-1_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26421-4
Online ISBN: 978-3-031-26422-1
eBook Packages: Computer ScienceComputer Science (R0)
