Skip to main content

Advertisement

Springer Nature Link
Log in

Spatio-Temporal Learning for Route-Based Travel Time Estimation

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Travel time estimation (TTE) is a fundamental task to build intelligent transportation systems. However, most existing TTE solutions design models upon simple homogeneous graphs and ignore the heterogeneity of traffic networks, where, e.g., main roads typically contribute differently from side roads. In terms of spatial dimension, few studies consider the dynamic spatial correlations across road segments, e.g., the traffic speed/volume on road segment A may correlate with the traffic speed/volume on road segment B, where A and B could be adjacent or non-adjacent, and such correlations may vary across time. In terms of temporal dimension, even fewer studies consider the dynamic temporal dependences, where, e.g., the historical states of road A may directly correlate with the recent state of A, and may also indirectly correlate with the recent state of road B. To track all aforementioned issues of existing TTE approaches, we provide HDTTE, a solution that employs heterogeneous and dynamic spatio-temporal predictive learning. Specifically, we first design a general multi-relational graph constructor that extracts hidden heterogeneous information of road segments, where we model road segments as nodes and model correlations as edges in the multi-relational graph. Next, we propose a dynamic graph attention convolution module that aggregates dynamic spatial dependence of neighbor roads to focal roads. We also present a novel correlation-augmented temporal convolution module to capture the influence of states at past time steps on current traffic states. Finally, in view of the periodic dependence of traffic, we develop a multi-scale adaptive fusion layer to enable HDTTE to exploit periodic patterns from recent, daily, and weekly traffic states. An experimental study using real-life highway and urban datasets demonstrates the validity of the approach and its advantage over others.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Cheng R, Chen C, Xu L F, Li S, Wang W, Cui H B, Liu K K, Li X L. R4: A framework for route representation and route recommendation. arXiv: 2110.10474, 2021. https://arxiv.org/abs/2110.10474, Jul. 2024.

    Google Scholar 

  2. Singh A, Alabbasi A, Aggarwal V. A reinforcement learning based algorithm for multi-hop ride-sharing: Model-free approach. In Proc. the 33rd Conference on Neural Information Processing Systems Workshop, Dec. 2019.

    Google Scholar 

  3. Yuan N J, Zheng Y, Zhang L H, Xie X. T-finder: A recommender system for finding passengers and vacant taxis. IEEE Trans. Knowledge and Data Engineering, 2013, 25(10): 2390–2403. DOI: https://doi.org/10.1109/TKDE.2012.153.

    Article  Google Scholar 

  4. Maiti S, Pal A, Pal A, Chattopadhyay T, Mukherjee A. Historical data based real time prediction of vehicle arrival time. In Proc. the 17th International IEEE Conference on Intelligent Transportation Systems, Oct. 2014, pp.1837–1842. DOI: https://doi.org/10.1109/ITSC.2014.6957960.

    Google Scholar 

  5. Wang H J, Tang X F, Kuo Y H, Kifer D, Li Z H. A simple baseline for travel time estimation using large-scale trip data. ACM Trans. Intelligent Systems and Technology, 2019, 10(2): Article No. 19. DOI: https://doi.org/10.1145/3293317.

  6. Zhang Y R, Haghani A. A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 2015, 58: 308–324. DOI: https://doi.org/10.1016/j.trc.2015.02.019.

    Article  Google Scholar 

  7. Li Y G, Fu K, Wang Z, Shahabi C, Ye J P, Liu Y. Multi-task representation learning for travel time estimation. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2018, pp.1695–1704. DOI: https://doi.org/10.1145/3219819.3220033.

    Chapter  Google Scholar 

  8. Wang Z, Fu K, Ye J P. Learning to estimate the travel time. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2018, pp.858–866. DOI: https://doi.org/10.1145/3219819.3219900.

    Chapter  Google Scholar 

  9. Yuan H T, Li G L, Bao Z F, Feng L. Effective travel time estimation: When historical trajectories over road networks matter. In Proc. the 2020 ACM SIGMOD International Conference on Management of Data, Jun. 2020, pp.2135–2149. DOI: https://doi.org/10.1145/3318464.3389771.

    Chapter  Google Scholar 

  10. Zhang H Y, Wu H, Sun W W, Zheng B H. DeepTravel: A neural network based travel time estimation model with auxiliary supervision. In Proc. the 27th International Joint Conference on Artificial Intelligence, Jul. 2018, pp.3655–3661. DOI: https://doi.org/10.24963/ijcai.2018/508.

    Google Scholar 

  11. Hong H T, Lin Y C, Yang X Q, Li Z, Fu K, Wang Z, Qie X H, Ye J P. HetETA: Heterogeneous information network embedding for estimating time of arrival. In Proc. the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2020, pp.2444–2454. DOI: https://doi.org/10.1145/3394486.3403294.

    Chapter  Google Scholar 

  12. Yin X Y, Wu G Z, Wei J Z, Shen Y M, Qi H, Yin B C. Deep learning on traffic prediction: Methods, analysis, and future directions. IEEE Trans. Intelligent Transportation Systems, 2022, 23(6): 4927–4943. DOI: https://doi.org/10.1109/TITS.2021.3054840.

    Article  Google Scholar 

  13. Fu K, Meng F L, Ye J P, Wang Z. CompactETA: A fast inference system for travel time prediction. In Proc. the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2020, pp.3337–3345. DOI: https://doi.org/10.1145/3394486.3403386.

    Chapter  Google Scholar 

  14. Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In Proc. the 30th International Conference on Neural Information Processing Systems, Dec. 2016, pp.3844–3852. DOI: https://doi.org/10.5555/3157382.3157527.

    Google Scholar 

  15. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735–1780. DOI: https://doi.org/10.1162/neco.1997.9.8.1735.

    Article  Google Scholar 

  16. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proc. the 2014 Conference on Empirical Methods in Natural Language Processing, Oct. 2014, pp.1724–1734. DOI: https://doi.org/10.3115/v1/d14-1179.

    Google Scholar 

  17. Guo S N, Lin Y F, Feng N, Song C, Wan H Y. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proc. the 33rd AAAI Conference on Artificial Intelligence, Jan. 27-Feb. 1, 2019, pp.922–929. DOI: https://doi.org/10.1609/aaai.v33i01.3301922.

    Google Scholar 

  18. Jin G Y, Yan H, Li F X, Huang J C, Li Y. Spatio-temporal dual graph neural networks for travel time estimation. ACM Trans. Spatial Algorithms and Systems, 2023. DOI: https://doi.org/10.1145/3627819. (Just accepted)

    Google Scholar 

  19. Li Y G, Yu R, Shahabi C, Liu Y. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In Proc. the 6th International Conference on Learning Representations, Apr. 30-May 3, 2018.

    Google Scholar 

  20. Yu B, Yin H T, Zhu Z X. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proc. the 27th International Joint Conference on Artificial Intelligence, Jul. 2018, pp.3634–3640. DOI: https://doi.org/10.24963/ijcai.2018/505.

    Google Scholar 

  21. Jiang R H, Yin D, Wang Z N, Wang Y Z, Deng J W, Liu H C, Cai Z K, Deng J L, Song X, Shibasaki R. DL-Traff: Survey and benchmark of deep learning models for urban traffic prediction. In Proc. the 30th ACM International Conference on Information & Knowledge Management, Oct. 2021, pp.4515–4525. DOI: https://doi.org/10.1145/3459637.3482000.

    Google Scholar 

  22. Jiang W W, Luo J Y. Graph neural network for traffic forecasting: A survey. Expert Systems with Applications, 2022, 207: 117921. DOI: https://doi.org/10.1016/j.eswa.2022.117921.

    Article  Google Scholar 

  23. Tedjopurnomo D A, Bao Z F, Zheng B H, Choudhury F M, Qin A K. A survey on modern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Trans. Knowledge and Data Engineering, 2022, 34(4): 1544–1561. DOI: https://doi.org/10.1109/TKDE.2020.3001195.

    Google Scholar 

  24. Yuan H T, Li G L. A survey of traffic prediction: From spatio-temporal data to intelligent transportation. Data Science and Engineering, 2021, 6(1): 63–85. DOI: https://doi.org/10.1007/s41019-020-00151-z.

    Article  Google Scholar 

  25. Wang D, Zhang J B, Cao W, Li J, Zheng Y. When will you arrive? Estimating travel time based on deep neural networks. In Proc. the 32nd AAAI Conference on Artificial Intelligence, Feb. 2018, pp.2500–2507. DOI: https://doi.org/10.5555/3504035.3504340.

    Google Scholar 

  26. Li X C, Cong G, Sun A X, Cheng Y. Learning travel time distributions with deep generative model. In Proc. the 2019 World Wide Web Conference, May 2019, pp.1017–1027. DOI: https://doi.org/10.1145/3308558.3313418.

    Chapter  Google Scholar 

  27. Fang X M, Huang J Z, Wang F, Zeng L K, Liang H J, Wang H F. ConSTGAT: Contextual spatial-temporal graph attention network for travel time estimation at Baidu maps. In Proc. the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2020, pp.2697–2705. DOI: https://doi.org/10.1145/3394486.3403320.

    Chapter  Google Scholar 

  28. Jin G Y, Wang M, Zhang J L, Sha H Y, Huang J C. STGNN-TTE: Travel time estimation via spatial-temporal graph neural network. Future Generation Computer Systems, 2022, 126: 70–81. DOI: https://doi.org/10.1016/j.future.2021.07.012.

    Article  Google Scholar 

  29. Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In Proc. the 5th International Conference on Learning Representations, Apr. 2017.

    Google Scholar 

  30. Fang Z Q, Pan L, Chen L, Du Y T, Gao Y J. MDTP: A multi-source deep traffic prediction framework over spatio-temporal trajectory data. Proceedings of the VLDB Endowment, 2021, 14(8): 1289–1297. DOI: https://doi.org/10.14778/3457390.3457394.

    Article  Google Scholar 

  31. Geng X, Li Y G, Wang L Y, Zhang L Y, Yang Q, Ye J P, Liu Y. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In Proc. the 33rd AAAI Conference on Artificial Intelligence, Jan. 2019, pp.3656–3663. DOI: https://doi.org/10.1609/aaai.v33i01.33013656.

    Google Scholar 

  32. Wu Z H, Pan S R, Long G D, Jiang J, Chang X J, Zhang C Q. Connecting the dots: Multivariate time series forecasting with graph neural networks. In Proc. the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2020, pp.753–763. DOI: https://doi.org/10.1145/3394486.3403118.

    Chapter  Google Scholar 

  33. Wu Z H, Pan S R, Long G D, Jiang J, Zhang C Q. Graph WaveNet for deep spatial-temporal graph modeling. In Proc. the 28th International Joint Conference on Artificial Intelligence, Aug. 2019, pp.1907–1913. DOI: https://doi.org/10.24963/ijcai.2019/264.

    Google Scholar 

  34. Fu X Y, Zhang J N, Meng Z Q, King I. MAGNN: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proc. the 2020 Web Conference, Apr. 2020, pp.2331–2341. DOI: https://doi.org/10.1145/3366423.3380297.

    Chapter  Google Scholar 

  35. Zhang C X, Song D J, Huang C, Swami A, Chawla N V. Heterogeneous graph neural network. In Proc. the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2019, pp.793–803. DOI: https://doi.org/10.1145/3292500.3330961.

    Chapter  Google Scholar 

  36. Bai L, Yao L N, Li C, Wang X Z, Wang C. Adaptive graph convolutional recurrent network for traffic forecasting. In Proc. the 34th International Conference on Neural Information Processing Systems, Dec. 2020, pp.17809–17815. DOI: https://doi.org/10.5555/3495724.3497218.

    Google Scholar 

  37. Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph attention networks. arXiv: 1710.10903, 2017. https://arxiv.org/abs/1710.10903, Jul. 2024.

    Google Scholar 

  38. Ye J X, Zhao J J, Ye K J, Xu C Z. How to build a graph-based deep learning architecture in traffic domain: A survey. IEEE Trans. Intelligent Transportation Systems, 2022, 23(5): 3904–3924. DOI: https://doi.org/10.1109/TITS.2020.3043250.

    Article  Google Scholar 

  39. Zhang Z W, Cui P, Zhu W W. Deep learning on graphs: A survey. IEEE Trans. Knowledge and Data Engineering, 2022, 34(1): 249–270. DOI: https://doi.org/10.1109/TKDE.2020.2981333.

    Article  Google Scholar 

  40. Guo S N, Lin Y F, Wan H Y, Li X C, Cong G. Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans. Knowledge and Data Engineering, 2022, 34(11): 5415–5428. DOI: https://doi.org/10.1109/TKDE.2021.3056502.

    Article  Google Scholar 

  41. Miller J, Hardt M. When recurrent models don’t need to be recurrent. arXiv: 1805.10369, 2018. https://arxiv.org/abs/1805.10369v1, Jul. 2024.

    Google Scholar 

  42. Zhu H, Wang L, Zhang P Y, Yan Y H. Multi-accent adaptation based on gate mechanism. In Proc. the 20th Annual Conference of the International Speech Communication Association, Sept. 2019, pp.744–748.

    Google Scholar 

  43. Lian J, Zhang L. One-month Beijing taxi GPS trajectory dataset with taxi IDs and vehicle status. In Proc. the 1st Workshop on Data Acquisition to Analysis, Nov. 2018, pp.3–4. DOI: https://doi.org/10.1145/3277868.3277870.

    Chapter  Google Scholar 

  44. Han L Z, Du B W, Sun L L, Fu Y J, Lv Y S, Xiong H. Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In Proc. the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Aug. 2021, pp.547–555. DOI: https://doi.org/10.1145/3447548.3467275.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Software Technology, Zhejiang University, Ningbo, 315048, China

    Zi-Quan Fang  (房子荃) & Qi-Chen Sun  (孙琦晨)

  2. College of Computer Science, Zhejiang University, Hangzhou, 310027, China

    Lu Chen  (陈　璐), Dan-Lei Hu  (胡丹蕾) & Yun-Jun Gao  (高云君)

Authors
  1. Zi-Quan Fang  (房子荃)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qi-Chen Sun  (孙琦晨)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lu Chen  (陈　璐)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dan-Lei Hu  (胡丹蕾)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yun-Jun Gao  (高云君)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yun-Jun Gao  (高云君).

Ethics declarations

Conflict of Interest The authors declare that they have no conflict of interest.

Additional information

This work was supported by the National Key Research and Development Program of China under Grant No. 2021YFC3300303, and the National Natural Science Foundation of China under Grant Nos. 62025206, 61972338, and 62102351.

Zi-Quan Fang received his Ph.D. degree in computer science from Zhejiang University, Hangzhou, in 2023. He is currently a researcher at Zhejiang University’s “Hundred Talents Program”. His research interests include big trajectory data management, spatio-temporal data mining, and distributed streaming data processing.

Qi-Chen Sun received his B.S. degree in computer science from Zhejiang University of Technology, Hangzhou, in 2021. He is currently working toward his M.S. degree in the School of Software Technology, Zhejiang University, Ningbo. His research interests include spatio-temporal data mining and intelligent traffic system.

Lu Chen received her Ph.D. degree in computer science from Zhejiang University, Hangzhou, in 2016. She worked as a professor at Aalborg University from 2017 to 2020. She is currently a researcher at Zhejiang University’s “Hundred Talents Program”. Her research interests include indexing and querying metric spaces.

Dan-Lei Hu received her B.S. degree in computer science from China Agricultural University, Beijing, in 2021. She is currently working toward her Ph.D. degree in the College of Computer Science, Zhejiang University, Hangzhou. Her research interests include trajectory data mining and analytics.

Yun-Jun Gao received his Ph.D. degree in computer science from Zhejiang University, Hangzhou, in 2008. He is currently a professor in the College of Computer Science, Zhejiang University, Hangzhou. His research interests include spatial and spatio-temporal databases, metric and incomplete/uncertain data management, graph databases, spatio-textual data processing, and database usability.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, ZQ., Sun, QC., Chen, L. et al. Spatio-Temporal Learning for Route-Based Travel Time Estimation. J. Comput. Sci. Technol. 39, 1107–1122 (2024). https://doi.org/10.1007/s11390-024-2828-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-024-2828-y

Keywords