Skip to main content
SpringerLink
Log in

GSTA: gated spatial–temporal attention approach for travel time prediction

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Accurate travel time prediction between two locations is one of the most substantial services in transport. In travel time prediction, origin–destination (OD) method is more challenging since it has no intermediate trajectory points. This paper puts forward a deep learning-based model, called Gated Spatial–Temporal Attention (GSTA), to optimize the OD travel time prediction. While many trip features are available, their relations and particular contributions to the output are usually unknown. To give our model the flexibility to select the most relevant features, we develop a feature selection module with an integration unit and a gating mechanism to pass or suppress the trip feature based on its contribution. To capture spatial–temporal dependencies and correlations in the short and long term, we propose a new pair-wise attention mechanism with spatial inference and temporal reasoning. In addition, we adapt and integrate multi-head attention to improve model performance in case of sophisticated dependencies in long term. Extensive experiments on two large taxi datasets in New York City, USA, and Chengdu, China demonstrate the superiority of our model in comparison with other models.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Abbar S, Stanojevic R, Mokbel MSTAD (2020) Spatio-temporal adjustment of traffic-oblivious travel-time estimation. In: Proceedings—IEEE international conference on mobile data management 2020–June, 79–88 https://doi.org/10.1109/MDM48529.2020.00029

  2. Abdollahi M, Khaleghi T, Yang K (2020) An integrated feature learning approach using deep learning for travel time prediction. Exp Syst Appl. https://doi.org/10.1016/j.eswa.2019.112864

    Article  Google Scholar 

  3. Aceto G, Ciuonzo D, Montieri A, Pescapè A (2019) Mimetic: mobile encrypted traffic classification using multimodal deep learning. Comput Netw 165:106944. https://doi.org/10.1016/j.comnet.2019.106944. https://www.sciencedirect.com/science/article/pii/S1389128619304669

  4. Ahmed MS, Cook AR (1979) Analysis of freeway traffic time-series data by using box-Jenkins techniques. Transp Res Rec 722:1–9

    Google Scholar 

  5. Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. http://arxiv.org/abs/1607.06450

  6. Clevert DA, Unterthiner T, Hochreiter S (2016) Fast and accurate deep network learning by exponential linear units (ELUs). In: Proceedings of the 4th international conference on learning representations, ICLR 2016: conference track proceedings. pp 1–14

  7. Commission NTL (2021) TLC trip record data: TLC. Accessed on 2 May 2021. https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page

  8. Dataset DCT (2021) DiDi Chengdu taxi dataset. Accessed on 2 May 2021. https://outreach.didichuxing.com/app-vue/dataList

  9. Dauphin YN, Fan A, Auli M, Grangier D (2016) Language modeling with gated convolutional networks. CoRR abs/1612.0. http://arxiv.org/abs/1612.08083

  10. Delling D (2018) Route planning in transportation networks: from research to practice. In: Proceedings of the 26th ACM SIGSPATIAL international conference on advances in geographic information systems, SIGSPATIAL ’18, p. 2. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3274895.3282802

  11. Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1996). Support vector regression machines. In: Proceedings of the 9th international conference on neural information processing systems, NIPS’96, p. 155–161. MIT Press, Cambridge, MA, USA

  12. Du W, Sun B, Kuai J, Xie J, Yu J, Sun T (2021) Highway travel time prediction of segments based on ANPR data considering traffic diversion. J Adv Transp 2021:1–16. https://doi.org/10.1155/2021/9512501

    Article  Google Scholar 

  13. Fang X, Huang J, Wang F, Zeng L, Liang H, Wang H (2020) ConSTGAT: contextual spatial-temporal graph attention network for travel time estimation at Baidu Maps. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. pp 2697–2705. https://doi.org/10.1145/3394486.3403320

  14. Fei X, Lu CC, Liu K (2011) A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction. Transp Res C Emerg Technol 19(6):1306–1318. https://doi.org/10.1016/j.trc.2010.10.005

    Article  Google Scholar 

  15. Guo G, Zhang T (2020) A residual spatio-temporal architecture for travel demand forecasting. Transp Res C Emerg Technol 115:102639. https://doi.org/10.1016/j.trc.2020.102639

    Article  Google Scholar 

  16. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 2016-Decem, 770–778. https://doi.org/10.1109/CVPR.2016.90

  17. Ishak S, Kotha P, Alecsandru C (2003) Optimization of dynamic neural network performance for short-term traffic prediction. Transp Res Rec 1836:45–56

    Article  Google Scholar 

  18. Jindal I, Tony Q, Chen X, Nokleby M, Ye J (2017) A unified neural network approach for estimating travel time and distance for a taxi trip. http://arxiv.org/abs/1710.04350

  19. Kankanamge KD, Witharanage YR, Withanage CS, Hansini M, Lakmal D, Thayasivam U (2019) Taxi trip travel time prediction with isolated xgboost regression. In: MERCon 2019: Proceedings, 5th international multidisciplinary moratuwa engineering research conference (April 2020), 54–59 . https://doi.org/10.1109/MERCon.2019.8818915

  20. Ke G, Meng Q, Finely T, Wang T, Chen W, Ma W, Ye Q, Liu, T.Y (2017). Lightgbm: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems 30 (NIP 2017)

  21. Khodaverdian Z, Sadr H, Edalatpanah SA (2021) A shallow deep neural network for selection of migration candidate virtual machines to reduce energy consumption. In: Proceedings of the 2021 7th international conference on web research (ICWR), pp 191–196. https://doi.org/10.1109/ICWR51868.2021.9443133

  22. Khodaverdian Z, Sadr H, Edalatpanah SA, Solimandarabi MN (2021) Combination of convolutional neural network and gated recurrent unit for energy aware resource allocation. CoRR abs/210612178. https://arxiv.org/abs/2106.12178

  23. Li Y, Fu K, Wang Z, Shahabi C, Ye J, Liu Y (2018) Multi-task representation learning for travel time estimation. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining

  24. Myung J, Kim D, Kho S, Park C (2011) Travel time prediction using k nearest neighbor method with combined data from vehicle detector system and automatic toll collection system. Transp Res Rec 2256:51–59

    Article  Google Scholar 

  25. Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: Proceedings of the 28th international conference on international conference on machine learning, ICML’11, p. 689–696. Omni press, Madison, WI, USA

  26. Prokhorchuk A, Dauwels J, Jaillet P (2020) Estimating travel time distributions by bayesian network inference. IEEE Trans Intell Transp Syst 21(5):1867–1876. https://doi.org/10.1109/TITS.2019.2899906

    Article  Google Scholar 

  27. Savarese P, Figueiredo D (2017) Residual gates: a simple mechanism for improved network optimization

  28. Service N.W (2021) NWS New York significant weather events archive. Accessed on 2 May 2021. https://www.weather.gov/okx/stormevents

  29. Sun Y, Wang Y, Fu K, Wang Z, Yan Z, Zhang C, Ye J (2020) FMA-ETA: estimating travel time entirely based on FFN with attention pp 1–10 . http://arxiv.org/abs/2006.04077

  30. Tan K, Chen J, Wang D (2018) Gated residual networks with dilated convolutions for supervised speech separation department of computer science and engineering, the Ohio state university, USA center for cognitive and brain sciences, the Ohio state university, USA. Icassp 1:21–25

    Google Scholar 

  31. Ting P, Wada T, Chiu Y, Sun M, Sakai K, Ku W, Jeng AA, Hwu J (2020) Freeway travel time prediction using deep hybrid model: taking sun Yat-Sen freeway as an example. IEEE Trans Veh Technol 69(8):8257–8266

    Article  Google Scholar 

  32. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inform Process Syst 2017:5999–6009

    Google Scholar 

  33. Wang D, Zhang J, Cao W, Li J, Zheng Y When will you arrive? Estimating travel time based on deep neural networks

  34. Wang H, Tang X, Kuo YH, Kifer D, Li Z (2019) A simple baseline for travel time estimation using large-scale trip data. ACM Trans Intel Syst Technol 10(2):1–22. https://doi.org/10.1145/3293317

    Article  Google Scholar 

  35. Wang Z, Fu K, Ye J, Labs DAI, Chuxing D (2018) Learning to estimate the travel. Time 1:858–866

    Google Scholar 

  36. Wu Z, Rilett LR, Ren W (2021) New methodologies for predicting corridor travel time mean and reliability. Int J Urban Sci. https://doi.org/10.1080/12265934.2021.1899844

    Article  Google Scholar 

  37. Xu S, Zhang R, Cheng W, Xu J (2020) MTLM: a multi-task learning model for travel time estimation. GeoInformatica. https://doi.org/10.1007/s10707-020-00422-x

    Article  Google Scholar 

  38. Yuan H, Li G, Bao Z, Feng L (2020) Effective travel time estimation: when historical trajectories over road networks matter. In: Proceedings of the ACM SIGMOD international conference on management of data. pp 2135–2149. https://doi.org/10.1145/3318464.3389771

  39. Zhang H, Wu H, Sun W, Zheng B (2018) DEEPTRAVEL: a neural network based travel time estimation model with auxiliary supervision. IJCAI Int Joint Conf Artif Intel 2018:3655–3661. https://doi.org/10.24963/ijcai.2018/508

    Article  Google Scholar 

  40. Zou Z, Yang H, Zhu AX (2020) Estimation of travel time based on ensemble method with multi-modality perspective urban big data. IEEE Access 8(2):24819–24828. https://doi.org/10.1109/ACCESS.2020.2971008

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant U1811463, and also in part by the Innovation Foundation of Science and Technology of Dalian under Grant 2018J11CY010.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, China

    Alkilane Khaled, Alfateh M. Tag Elsir & Yanming Shen

Authors
  1. Alkilane Khaled
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alfateh M. Tag Elsir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanming Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanming Shen.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khaled, A., Elsir, A.M.T. & Shen, Y. GSTA: gated spatial–temporal attention approach for travel time prediction. Neural Comput & Applic 34, 2307–2322 (2022). https://doi.org/10.1007/s00521-021-06560-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06560-z

Keywords

Search

Navigation