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On prediction of traffic flows in smart cities: a multitask deep learning based approach

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Abstract

With the rapid development of transportation systems, traffic data have been largely produced in daily lives. Finding the insights of all these complex data is of great significance to vehicle dispatching and public safety. In this work, we propose a multitask deep learning model called Multitask Recurrent Graph Convolutional Network (MRGCN) for accurately predicting traffic flows in the city. Specifically, we design a multitask framework consisting of four components: a region-flow encoder for modeling region-flow dynamics, a transition-flow encoder for exploring transition-flow correlations, a context modeling component for contextualized fusion of two types of traffic flows and a task-specific decoder for predicting traffic flows. Particularly, we introduce Dual-attention Graph Convolutional Gated Recurrent Units (DGCGRU) to simultaneously capture spatial and temporal dependencies, which integrate graph convolution and recurrent model as a whole. Extensive experiments are carried out on two real-world datasets and the results demonstrate that our proposed method outperforms several existing approaches.

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References

  1. Zhang, J., Wang, F., Wang, K., Lin, W., Xu, X., Chen, C.: Data-driven intelligent transportation systems: A survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011)

    Article  Google Scholar 

  2. Dai, J., Liu, C., Xu, J., Ding, Z.: On personalized and sequenced route planning. World Wide Web 19(4), 679–705 (2016)

    Article  Google Scholar 

  3. Xu, J., Chen, J., Zhou, R., Fang, J., Liu, C.: On workflow aware location-based service composition for personal trip planning. Fut. Gener. Comp. Syst. 98, 274–285 (2019)

    Article  Google Scholar 

  4. Bai, L., Yao, L., Kanhere, S. S., Wang, X., Sheng, Q. Z.: Stg2seq: Spatial-Temporal Graph to Sequence Model for Multi-Step Passenger Demand Forecasting. In: IJCAI, pp. 1981–1987 (2019)

  5. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Google Scholar 

  6. Zhang, J., Zheng, Y., Qi, D.: Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In: AAAI, pp. 1655–1661 (2017)

  7. Peng, S., Shen, Y., Zhu, Y., Chen, Y.: A Frequency-Aware Spatio-Temporal Network for Traffic Flow Prediction. In: DASFAA, pp. 697–712 (2019)

  8. Lin, Z., Feng, J., Lu, Z., Li, Y., Jin, D.: Deepstn+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis. In: AAAI, pp. 1020–1027 (2019)

  9. Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., Wang, Y.: Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4), 818 (2017)

    Article  Google Scholar 

  10. Yuan, J., Zheng, Y., Xie, X.: Discovering Regions of Different Functions in a City Using Human Mobility and Pois. In: SIGKDD, pp. 186–194 (2012)

  11. Lv, Z., Xu, J., Zheng, K., Yin, H., Zhao, P., Zhou, X.: LC-RNN: A Deep Learning Model for Traffic Speed Prediction. In: IJCAI, pp. 3470–3476 (2018)

  12. Zhao, J., Xu, J., Zhou, R., Zhao, P., Liu, C., Zhu, F.: On Prediction of User Destination by Sub-Trajectory Understanding: A Deep Learning Based Approach. In: CIKM, pp. 1413–1422 (2018)

  13. Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. In: AAAI, pp. 922–929 (2019)

  14. Yu, B., Yin, H., Zhu, Z.: Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In: IJCAI, pp. 3634–3640 (2018)

  15. Wang, F., Xu, J., Liu, C., Zhou, R., Zhao, P.: MTGCN: A Multitask Deep Learning Model for Traffic Flow Prediction. In: DASFAA, vol. 12112, pp. 435–451 (2020)

  16. Pan, B., Demiryurek, U., Shahabi, C.: Utilizing Real-World Transportation Data for Accurate Traffic Prediction. In: ICDM. IEEE Computer Society, pp. 595–604 (2002)

  17. Ristanoski, G., Liu, W., Bailey, J.: Time Series Forecasting Using Distribution Enhanced Linear Regression. In: PAKDD, pp. 484–495 (2013)

  18. Chandra, S. R., Al-Deek, H.: Predictions of freeway traffic speeds and volumes using vector autoregressive models. J. Intell Transp. Syst. 13(2), 53–72 (2009)

    Article  Google Scholar 

  19. Wu, C., Ho, J., Lee, D.: Travel-time prediction with support vector regression. IEEE Trans. Intell. Transp. Syst. 5(4), 276–281 (2004)

    Article  Google Scholar 

  20. Cui, Z., Ke, R., Wang, Y.: Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. CoRR, arXiv:1801.02143 (2018)

  21. Zhang, J., Zheng, Y., Qi, D., Li, R., Yi, X.: Dnn-Based Prediction Model for Spatio-Temporal Data. In: SIGSPATIAL. ACM, pp. 92:1–92:4 (2016)

  22. Guo, S., Lin, Y., Li, S., Chen, Z., Wan, H.: Deep spatial-temporal 3d convolutional neural networks for traffic data forecasting. IEEE, Trans. Intell. Transp. Syst. 20(10), 3913–3926 (2019)

    Article  Google Scholar 

  23. Yao, H., Tang, X., Wei, H., Zheng, G., Li, Z.: Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. In: AAAI, pp. 5668–5675 (2019)

  24. Feng, J., Lin, Z., Xia, T., Sun, F., Guo, D., Li, Y.: A Sequential Convolution Network for Population Flow Prediction with Explicitly Correlation Modelling. In: IJCAI, pp. 1331–1337 (2020)

  25. Zhang, J., Zheng, Y., Sun, J., Qi, D.: Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Trans. Knowl. Data Eng. 32(3), 468–478 (2020)

    Article  Google Scholar 

  26. Shi, X., Chen, Z., Wang, H., Yeung, D., Wong, W., Woo, W.: Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. In: NIPS, pp. 802–810 (2015)

  27. Zonoozi, A., Kim, J., Li, X., Cong, G.: Periodic-Crn: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns. In: IJCAI, pp. 3732–3738 (2018)

  28. Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. In: ICLR (2014)

  29. Kipf, T. N., Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks. In: ICLR (2017)

  30. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In: NIPS, pp. 3837–3845 (2016)

  31. Sun, J., Zhang, J., Li, Q., Yi, X., Liang, Y., Zheng, Y.: Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks. IEEE Transactions on Knowledge and Data Engineering (2020)

  32. Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. In: AAAI, pp. 914–921 (2020)

  33. Chen, W., Chen, L., Xie, Y., Cao, W., Gao, Y., Feng, X.: Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting. In: AAAI, pp. 3529–3536 (2020)

  34. Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph Wavenet for Deep Spatial-Temporal Graph Modeling. In: IJCAI, pp. 1907–1913 (2019)

  35. Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In: ICLR (2018)

  36. Chen, C., Li, K., Teo, S. G., Zou, X., Wang, K., Wang, J., Zeng, Z.: Gated Residual Recurrent Graph Neural Networks for Traffic Prediction. In: AAAI, pp. 485–492 (2019)

  37. Zhang, Y., Wei, Y., Yang, Q.: Learning to multitask. In: NeurIPS, pp. 5776–5787 (2018)

  38. Zhao, L., Wang, J., Guo, X.: Distant-Supervision of Heterogeneous Multitask Learning for Social Event Forecasting with Multilingual Indicators. In: AAAI, pp. 4498–4505 (2018)

  39. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., Dean, J.: Distributed Representations of Words and Phrases and Their Compositionality. In: NIPS, pp. 3111–3119 (2013)

  40. Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C: Emerg. Technol. 54, 187–197 (2015)

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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 Nos. DP170104747, DP180100212, A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant No. KYCX20_2714.

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Authors and Affiliations

  1. Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Soochow, China

    Fucheng Wang, Jiajie Xu & Pengpeng Zhao

  2. Swinburne University of Technology, Swinburne, Australia

    Chengfei Liu & Rui Zhou

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  1. Fucheng Wang
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  2. Jiajie Xu
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  3. Chengfei Liu
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  4. Rui Zhou
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  5. Pengpeng Zhao
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Correspondence to Fucheng Wang.

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Wang, F., Xu, J., Liu, C. et al. On prediction of traffic flows in smart cities: a multitask deep learning based approach. World Wide Web 24, 805–823 (2021). https://doi.org/10.1007/s11280-021-00877-4

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  • DOI: https://doi.org/10.1007/s11280-021-00877-4

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