Abstract
Transportation time prediction (TIP) of a truck is one of key tasks for supporting the services in bulk logistics like route planning. But TIP prediction is challenging as it involves travel time prediction and dwell time prediction, which are influenced by various complex factors. Besides, there exists mutually constrained effects between travel time prediction and dwell time prediction. In this paper, we propose an Attention Mechanism based Multi-Task prediction framework consisting of travel pattern learning, stay pattern learning and transportation time modeling, called AMP. In view of that low prediction performance resulted by uncertain dwell time and mutually constrained effects between travel time and dwell time, we put forward a stay pattern learning module based on transformer and multi-factor attention mechanism. Furthermore, we design a multi-task learning based prediction module embedded with a mutual cross-attention mechanism to enhance overall prediction performance. Experimental results on a large-scale logistics data set demonstrate that our proposal can reduce MAPE by an average of 9.2%, MAE by an average of 19.5%, and RMSE by an average of 23.0% as compared to the baselines.
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
References
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: SIGKDD, pp. 2697–2705 (2020)
Fu, T.Y., Lee, W.C.: Deepist: deep image-based spatio-temporal network for travel time estimation. In: CIKM, pp. 69–78 (2019)
Hong, H., et al.: Heteta: heterogeneous information network embedding for estimating time of arrival. In: SIGKDD, pp. 2444–2454 (2020)
Jin, G., Wang, M., Zhang, J., Sha, H., Huang, J.: STGNN-TTE: travel time estimation via spatial-temporal graph neural network. Futur. Gener. Comput. Syst. 126, 70–81 (2022)
Luo, W., Tan, H., Chen, L., Ni, L.M.: Finding time period-based most frequent path in big trajectory data. In: SIGMOD, pp. 713–724 (2013)
Newson, P., Krumm, J.: Hidden Markov map matching through noise and sparseness. In: Agrawal, D., et al. (eds.) SIGSPATIAL, pp. 336–343. ACM (2009)
Tiesyte, D., Jensen, C.S.: Similarity-based prediction of travel times for vehicles traveling on known routes. In: SIGSPATIAL, pp. 1–10 (2008)
Wan, F., et al.: Mttpre: a multi-scale spatial-temporal model for travel time prediction. In: SIGSPATIAL, pp. 1–10 (2022)
Wang, D., Zhang, J., Cao, W., Li, J., Zheng, Y.: When will you arrive? Estimating travel time based on deep neural networks. In: AAAI, vol. 32 (2018)
Wang, H., Tang, X., Kuo, Y.H., Kifer, D., Li, Z.: A simple baseline for travel time estimation using large-scale trip data. ACM Trans. Intell. Syst. Technol. 10(2), 1–22 (2019)
Wang, H., et al.: Multi-task weakly supervised learning for origin-destination travel time estimation. IEEE Trans. Knowl. Data Eng. (2023)
Wang, Z., Fu, K., Ye, J.: Learning to estimate the travel time. In: SIGKDD, pp. 858–866 (2018)
Wu, C.H., Ho, J.M., Lee, D.T.: Travel-time prediction with support vector regression. IEEE Trans. Intell. Transp. Syst. 5(4), 276–281 (2004)
Yang, B., Dai, J., Guo, C., Jensen, C.S., Hu, J.: PACE: a PAth-CEntric paradigm for stochastic path finding. VLDB 27, 153–178 (2018)
Zhang, H., Wu, H., Sun, W., Zheng, B.: Deeptravel: a neural network based travel time estimation model with auxiliary supervision. In: Lang, J. (ed.) IJCAI, pp. 3655–3661 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, M., Wu, T., Mao, J., Zhu, K., Zhou, A. (2024). Attention Mechanism Based Multi-task Learning Framework for Transportation Time Prediction. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_30
Download citation
DOI: https://doi.org/10.1007/978-981-97-2262-4_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2264-8
Online ISBN: 978-981-97-2262-4
eBook Packages: Computer ScienceComputer Science (R0)