Abstract
Transportation-mode aware travel time estimation (TA-TTE) aims to estimate the travel time of a path in a specific transportation mode (e.g., walking, driving). Different from traditional travel time estimation, TA-TTE requires to consider the heterogeneity of transportation modes due to different moving characteristics in different modes. As a result, when applying classical travel time estimation models, sufficient data is needed for each mode to capture mode-dependent characteristics separately. While in reality, it is hard to obtain enough data in some modes, resulting in a severe data sparsity problem. A practical method to solve this problem is to leverage the mode-independent knowledge (e.g., time for waiting for traffic lights) learned from other modes. To this end, we propose a meta-optimized method called MetaMG, which learns well-generalized initial parameters to support effective knowledge transfer across different modes. Particularly, to avoid negative transfer, we integrate a spatial-temporal memory in meta-learning to cluster trajectories according to spatial-temporal distribution similarity for enhanced knowledge transfer. Besides, a multi-granularity trajectory representation is adopted in our base model to explore more useful features in different spatial granularities while improving the robustness. Finally, comprehensive experiments on real-world datasets demonstrate the superior performance of our proposed method over existing approaches.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: NIPS, pp. 3981–3989 (2016)
Fang, X., Huang, J., Wang, F., Liu, L., Sun, Y., Wang, H.: SSML: self-supervised meta-learner for en route travel time estimation at Baidu maps. In: KDD, pp. 2840–2848 (2021)
Fang, X., Huang, J., Wang, F., Zeng, L., Liang, H., Wang, H.: ConSTGAT: contextual spatial-temporal graph attention network for travel time estimation at Baidu maps. In: KDD, pp. 2697–2705 (2020)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML, vol. 70, pp. 1126–1135 (2017)
Fu, K., Meng, F., Ye, J., Wang, Z.: CompactETA: a fast inference system for travel time prediction. In: KDD, pp. 3337–3345 (2020)
Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: NIPS, pp. 1019–1027 (2016)
Graves, A., Wayne, G., Danihelka, I.: Neural Turing Machines. CoRR abs/1410.5401 (2014)
Kisialiou, Y., Gribkovskaia, I., Laporte, G.: The periodic supply vessel planning problem with flexible departure times and coupled vessels. COR 94, 52–64 (2018)
Liu, Y., et al.: MetaStore: a task-adaptative meta-learning model for optimal store placement with multi-city knowledge transfer. TIST 12(3), 28:1–28:23 (2021)
Lv, Z., Xu, J., Zhao, P., Liu, G., Zhao, L., Zhou, X.: Outlier trajectory detection: a trajectory analytics based approach. In: DASFAA, vol. 10177, pp. 231–246 (2017)
Madotto, A., Lin, Z., Wu, C., Fung, P.: Personalizing dialogue agents via meta-learning. In: ACL, pp. 5454–5459 (2019)
Munkhdalai, T., Yuan, X., Mehri, S., Trischler, A.: Rapid adaptation with conditionally shifted neurons. In: ICML, pp. 3664–3673 (2018)
Qian, K., Yu, Z.: Domain adaptive dialog generation via meta learning. arXiv preprint arXiv:1906.03520 (2019)
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.P.: Meta-learning with memory-augmented neural networks. In: ICML, vol. 48, pp. 1842–1850 (2016)
Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. arXiv preprint arXiv:1703.05175 (2017)
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: CVPR, pp. 1199–1208 (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, pp. 2500–2507 (2018)
Wang, H., Tang, X., Kuo, Y., Kifer, D., Li, Z.: A simple baseline for travel time estimation using large-scale trip data. TIST 10(2), 19:1–19:22 (2019)
Xu, J., Chen, J., Zhou, R., Fang, J., Liu, C.: On workflow aware location-based service composition for personal trip planning. FGCS 98, 274–285 (2019)
Xu, J., Gao, Y., Liu, C., Zhao, L., Ding, Z.: Efficient route search on hierarchical dynamic road networks. DPD 33(2), 227–252 (2015)
Xu, J., Zhao, J., Zhou, R., Liu, C., Zhao, P., Zhao, L.: Predicting destinations by a deep learning based approach. TKDE 33, 651–666 (2021)
Xu, S., Xu, J., Zhou, R., Liu, C., Li, Z., Liu, A.: TADNM: a transportation-mode aware deep neural model for travel time estimation. In: DSFAA, pp. 468–484 (2020)
Xu, S., Zhang, R., Cheng, W., Xu, J.: MTLM: a multi-task learning model for travel time estimation. GeoInformatica (2020). https://doi.org/10.1007/s10707-020-00422-x
Yao, H., Liu, Y., Wei, Y., Tang, X., Li, Z.: Learning from multiple cities: a meta-learning approach for spatial-temporal prediction. In: WWW (2019)
Ye, H.J., Sheng, X.R., Zhan, D.C.: Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach. ML 109(3), 643–664 (2020)
Yuan, H., Li, G., Bao, Z., Feng, L.: Effective travel time estimation: when historical trajectories over road networks matter. In: SIGMOD, pp. 2135–2149 (2020)
Yuan, N.J., Zheng, Y., Zhang, L., Xie, X.: T-Finder: a recommender system for finding passengers and vacant taxis. TKDE 25(10), 2390–2403 (2013)
Zhang, H., Wu, H., Sun, W., Zheng, B.: DeepTravel: a neural network based travel time estimation model with auxiliary supervision. In: IJCAI, pp. 3655–3661 (2018)
Zhao, J., Xu, J., Zhou, R., Zhao, P., Liu, C., Zhu, F.: On prediction of user destination by sub-trajectory understanding: a deep learning based approach. In: CIKM, pp. 1413–1422 (2018)
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.: Understanding mobility based on GPS data. In: UbiComp, vol. 344, pp. 312–321 (2008)
Zheng, Y., Xie, X., Ma, W.: GeoLife: a collaborative social networking service among user, location and trajectory. DEB 33(2), 32–39 (2010)
Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining interesting locations and travel sequences from GPS trajectories. In: WWW, pp. 791–800 (2009)
Acknowledgements
This work was supported by the National Natural Science Foundation of China projects under grant numbers (No. 61872258, No. 61772356, No. 62072125), the major project of natural science research in universities of Jiangsu Province under grant number 20KJA520005, the priority academic program development of Jiangsu higher education institutions, young scholar program of Cyrus Tang Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fan, Y., Xu, J., Zhou, R., Liu, C. (2022). Transportation-Mode Aware Travel Time Estimation via Meta-learning. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-00126-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-00125-3
Online ISBN: 978-3-031-00126-0
eBook Packages: Computer ScienceComputer Science (R0)