Abstract
Travel time estimation (TTE) has been recognized as an important problem in location-based services. Existing approaches mainly estimate travel time by learning from large-scale trajectories, they normally assume a path is in a single transportation mode (e.g., driving, biking), and could not provide accurate TTE for mixed-mode paths, which are indeed common in daily life. In this paper, we propose a transportation-mode aware deep neural model called TADNM, which considers both spatio-temporal characteristics and the heterogeneity of underlying transportation modes to achieve more accurate travel time estimation. Specifically, we estimate travel time using the knowledge from (sub-)trajectories not only roughly following the target path, but also being consistent with segments of the target path in terms of transportation mode. To this end, a well-designed neural network model is proposed to integrate the rich information extracted from trajectories first, and then to learn effective representations for capturing the spatial correlations, temporal dependencies and transportation mode effects from the trajectory data. Besides, the proposed model fully considers the transition time of switching transportation mode in the path, and a transportation-mode aware attention mechanism is used to better reflect the impact of transportation mode to the required travel time. Extensive experiments on real trajectory datasets demonstrate the effectiveness of our proposed model.
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References
Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. CoRR abs/1409.1259 (2014). http://arxiv.org/abs/1409.1259
Dai, J., Liu, C., Xu, J., Ding, Z.: On personalized and sequenced route planning. World Wide Web 19(4), 679–705 (2015). https://doi.org/10.1007/s11280-015-0352-2
Endo, Y., Toda, H., Nishida, K., Kawanobe, A.: Deep feature extraction from trajectories for transportation mode estimation. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9652, pp. 54–66. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31750-2_5
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
Hornik, K., Stinchcombe, M.B., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359–366 (1989)
Jenelius, E., Koutsopoulos, H.N.: Travel time estimation for urban road networks using low frequency probe vehicle data. Transp. Res. Part B Methodol. 53, 64–81 (2013)
Jindal, I., Qin, T., Chen, X., Nokleby, M.S., Ye, J.: A unified neural network approach for estimating travel time and distance for a taxi trip. CoRR abs/1710.04350 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Kisialiou, Y., Gribkovskaia, I., Laporte, G.: The periodic supply vessel planning problem with flexible departure times and coupled vessels. Comput. Oper. Res. 94, 52–64 (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)
Li, Y., Fu, K., Wang, Z., Shahabi, C., Ye, J., Liu, Y.: Multi-task representation learning for travel time estimation. In: KDD, pp. 1695–1704. ACM (2018)
Li, Y., Liu, C., Liu, K., Xu, J., He, F., Ding, Z.: On efficient map-matching according to intersections you pass by. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds.) DEXA 2013. LNCS, vol. 8056, pp. 42–56. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40173-2_6
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)
Rahmani, M., Jenelius, E., Koutsopoulos, H.N.: Route travel time estimation using low-frequency floating car data. In: ITSC, pp. 2292–2297. IEEE (2013)
Shang, S., Liu, J., Zheng, K., Lu, H., Pedersen, T.B., Wen, J.-R.: Planning unobstructed paths in traffic-aware spatial networks. GeoInformatica 19(4), 723–746 (2015). https://doi.org/10.1007/s10707-015-0227-9
Vaswani, A., et al.: Attention is all you need. In: NIPS, pp. 5998–6008 (2017)
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. AAAI Press (2018)
Wang, H., Kuo, Y., Kifer, D., Li, Z.: A simple baseline for travel time estimation using large-scale trip data. In: SIGSPATIALGIS, pp. 61:1–61:4. ACM (2016)
Wang, Y., Zheng, Y., Xue, Y.: Travel time estimation of a path using sparse trajectories. In: KDD, pp. 25–34. ACM (2014)
Wang, Z., Fu, K., Ye, J.: Learning to estimate the travel time. In: KDD, pp. 858–866 (2018)
Xu, J., Chen, J., Zhou, R., Fang, J., Liu, C.: On workflow aware location-based service composition for personal trip planning. Future Gener. Comput. Syst. 98, 274–285 (2019)
Xu, J., Gao, Y., Liu, C., Zhao, L., Ding, Z.: Efficient route search on hierarchical dynamic road networks. Distrib. Parallel Databases 33(2), 227–252 (2014). https://doi.org/10.1007/s10619-014-7146-x
Yang, B., Guo, C., Jensen, C.S.: Travel cost inference from sparse, spatio-temporally correlated time series using Markov models. PVLDB 6(9), 769–780 (2013)
Yuan, N.J., Zheng, Y., Zhang, L., Xie, X.: T-finder: a recommender system for finding passengers and vacant taxis. IEEE Trans. Knowl. Data Eng. 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)
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.: Understanding mobility based on GPS data. In: UbiComp. ACM International Conference Proceeding Series, vol. 344, pp. 312–321. ACM (2008)
Zheng, Y., Xie, X., Ma, W.: Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 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. ACM (2009)
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61872258, 61772356, 61876117, and 61802273, the Australian Research Council discovery projects under grant numbers DP170104747, DP180100212, the Open Program of State Key Laboratory of Software Architecture under item number SKLSAOP1801 and Blockshine corporation.
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Xu, S., Xu, J., Zhou, R., Liu, C., Li, Z., Liu, A. (2020). TADNM: A Transportation-Mode Aware Deep Neural Model for Travel Time Estimation. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12112. Springer, Cham. https://doi.org/10.1007/978-3-030-59410-7_32
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