Abstract
Recently, deep learning has achieved promising results in the calculation of Estimated Time of Arrival (ETA), which is considered as predicting the travel time from the start point to a certain place along a given path. ETA plays an essential role in intelligent taxi services or automotive navigation systems. A common practice is to use embedding vectors to represent the elements of a road network, such as road segments and crossroads. Road elements have their own attributes like length, presence of crosswalks, lanes number, etc. However, many links in the road network are traversed by too few floating cars even in large ride-hailing platforms and affected by the wide range of temporal events. As the primary goal of the research, we explore the generalization ability of different spatial embedding strategies and propose a two-stage approach to deal with such problems.
V. Porvatov and N. Semenova—Equal contribution.
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References
Alsentzer, E., Finlayson, S.G., Li, M.M., Zitnik, M.: Subgraph neural networks. CoRR abs/2006.10538 (2020). https://arxiv.org/abs/2006.10538
Asghari, M., Emrich, T., Demiryurek, U., Shahabi, C.: Probabilistic estimation of link travel times in dynamic road networks. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2015, Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2820783.2820836
Ayhan, S., Costas, P., Samet, H.: Predicting estimated time of arrival for commercial flights. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 33–42, July 2018
Dai, R., Xu, S., Gu, Q., Ji, C., Liu, K.: Hybrid spatio-temporal graph convolutional network: Improving traffic prediction with navigation data. CoRR abs/2006.12715 (2020). https://arxiv.org/abs/2006.12715
Data61, C.: Stellargraph machine learning library. https://github.com/stellargraph/stellargraph (2018)
Fout, A., Byrd, J., Shariat, B., Ben-Hur, A.: Protein interface prediction using graph convolutional networks. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 6533–6542. Curran Associates Inc., Red Hook (2017)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 922–929, July 2019
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2018)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014). arxiv:1412.6980Comment: Published as a conference paper at the 3rd International Conference for Learning Representations, San Diego (2015)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations, ICLR 2017 (2017)
Kviesis, A., Zacepins, A., Komasilovs, V., Munizaga, M.: Bus arrival time prediction with limited data set using regression models, In: Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pp. 643–647, January 2018
Li, J., Cai, D., He, X.: Learning graph-level representation for drug discovery (2017)
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)
Park, K., Sim, S., Bae, H.: Vessel estimated time of arrival prediction system based on a path-finding algorithm. Mar. Trans. Res. 2, 100012 (2021). https://www.sciencedirect.com/science/article/pii/S2666822X21000046
Paruchuri, V., Chellappan, S., Lenin, R.B.: Arrival time based traffic signal optimization for intelligent transportation systems. In: 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), pp. 703–709 (2013)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2014. https://doi.org/10.1145/2623330.2623732
Prokhorchenko, A., et al.: Forecasting the estimated time of arrival for a cargo dispatch delivered by a freight train along a railway section. East.-Eur. J. Enterp. Technol. 3, 30–38 (2019)
Sanchez-Gonzalez, A., et al.: Graph networks as learnable physics engines for inference and control. CoRR abs/1806.01242 (2018). http://arxiv.org/abs/1806.01242
Shi, C., Chen, B.Y., Li, Q.: Estimation of travel time distributions in urban road networks using low-frequency floating car data. ISPRS Int. J. Geo.-Inf. 6, 253 (2017)
Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 914–921, April 2020
Sun, Y., Fu, K., Wang, Z., Zhang, C., Ye, J.: Road network metric learning for estimated time of arrival (2020)
Vaswani, A., et al.: Attention is all you need. CoRR abs/1706.03762 (2017). http://arxiv.org/abs/1706.03762
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph Attention Networks (2018)
Veličković, P., Fedus, W., Hamilton, W.L., Liò, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: International Conference on Learning Representations (2018)
Wang, D., Zhang, J., Cao, W., Li, J., Zheng, Y.: When will you arrive? Estimating travel time based on deep neural networks. In: AAAI (2018)
Wang, Y., Zheng, Y., Xue, Y.: Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 25–34. Association for Computing Machinery, New York (2014). https://doi.org/10.1145/2623330.2623656
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, KDD 2018, pp. 858–866. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3219819.3219900
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. CoRR abs/1801.07455 (2018). http://arxiv.org/abs/1801.07455
Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57–81 (2020)
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The work was supported by the Joint Stock Company “Sberbank of Russia”.
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Porvatov, V., Semenova, N., Chertok, A. (2022). 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. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_48
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