Abstract
Estimating the short-term online taxi travel time is an important content in urban planning and navigation forecasting systems. When estimating the taxi travel time, we need to take many factors, such as temporal correlation, spatial dependency, and external factors, into consideration. In this paper, we propose a model named DeepSTTE (Short-term Travel Time Estimation) to estimate the short-term online taxi travel time. Firstly, the model integrates external factors using the embedding method. Further, we leverage the classical convolution networks to obtain the spatial feature information of the original GPS trajectory, and use the temporal convolutional networks (TCN) to obtain the temporal characteristics. Finally, we estimate the online taxi travel time of the entire path by the auxiliary learning part. We perform lots of experiments with real datasets, showing that our model DeepSTTE reduces the errors and performs better than the current methods in estimating the travel time.
This research was supported in part by National Key Research and Development Plan Key Special Projects under Grant No. 2018YFB2100303, Shandong Province colleges and universities youth innovation technology plan innovation team project under Grant No. 2020KJN011 and Program for Innovative Postdoctoral Talents in Shandong Province under Grant No. 40618030001, National Natural Science Foundation of China under Grant No. 61802216, and Postdoctoral Science Foundation of China under Grant No. 2018M642613.
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
Jia., Z., Chen, C., Coifman, B., Varaiya, P.: The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors. In: ITSC 2001, pp. 536–541. 2001 IEEE Intelligent Transportation Systems, Oakland (2001)
Asif, M.T., et al.: Spatio temporal patterns in large-scale traffic speed estimation. IEEE Trans. Intell. Transp. Syst. 15(2), 794–804 (2014)
Luo, W., Tan, H., Chen, L., Ni, L.M.: Finding time period-based most frequent path in big trajectory data. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 713–724. Association for Computing Machinery, New York (2013)
Hull, B., et al.: CarTel: a distributed mobile sensor computing system. In: Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, pp. 125–138 (2006)
Rahmani, M., Jenelius, E., Koutsopoulos, H.N.: Route travel time estimation using low-frequency floating car data. In: 16th International IEEE Conference on Intelligent Transportation Systems, pp. 2292–2297. The Hague (2013)
Wang, D., Zhang, J., Cao, W., Li, J., Zheng, Y.: When will you arrive? Estimating travel time based on deep neural networks. In: Thirty-Second AAAI Conference on Artificial Intelligence, pp. 2500–2507 (2018)
Zhang, H., Wu, H., Sun, W., Zheng, B.: DEEPTRAVEL: a neural network based travel time estimation model with auxiliary supervision. arXiv preprint arXiv:1802.02147 (2018)
Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows estimation. In: Thirty-First AAAI Conference on Artificial Intelligence, pp. 1655–1661 (2017)
Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Advances in neural information processing systems, pp. 1019–1027 (2016)
Sun, Y., Jiang, G., Lam, S. K., Chen, S., He, P.: Bus travel speed estimation using attention network of heterogeneous correlation features. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 73–81. Society for Industrial and Applied Mathematics, Calgary (2019)
Qiu, J., Du, L., Zhang, D., Su, S., Tian, Z.: Nei-TTE: intelligent traffic time estimation based on fine-grained time derivation of road segments for smart city. IEEE Trans. Ind. Inform. 16(4), 2659–2666 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Zhang, X., You, J.: A gated dilated causal convolution based encoder-decoder for network traffic forecasting. IEEE Access 8, 6087–6097 (2020)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer Series in Statistics, vol. 1, no. 10. New York (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fu, L., Li, J., Lv, Z., Li, Y., Lin, Q. (2020). Estimation of Short-Term Online Taxi Travel Time Based on Neural Network. In: Yu, D., Dressler, F., Yu, J. (eds) Wireless Algorithms, Systems, and Applications. WASA 2020. Lecture Notes in Computer Science(), vol 12385. Springer, Cham. https://doi.org/10.1007/978-3-030-59019-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-59019-2_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59018-5
Online ISBN: 978-3-030-59019-2
eBook Packages: Computer ScienceComputer Science (R0)