计算机科学技术学报 ›› 2020,Vol. 35 ›› Issue (2): 338-352.doi: 10.1007/s11390-020-9970-y

• • 上一篇    下一篇

利用城市区域间的多重相关性进行人群流动预测

Qiang Zhou, Jing-Jing Gu*, Member, CCF, Chao Ling, Wen-Bo Li, Yi Zhuang, Jian Wang, Senior Member, CCF, Member, ACM, IEEE   

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • 收稿日期:2019-08-20 修回日期:2020-01-17 出版日期:2020-03-05 发布日期:2020-01-20
  • 通讯作者: Jing-Jing Gu E-mail:gujingjing@nuaa.edu.cn
  • 作者简介:Qiang Zhou received his B.E. degree in software engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, in 2016. He is currently working toward his Ph.D. degree in the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing. His current research interests include urban computing, data mining, deep learning and spatio-temporal prediction.
  • 基金资助:
    This work was supported by the National Natural Science Foundation of China under Grant No. 61572253, and the Aviation Science Fund of China under Grant No. 2016ZC52030.

Exploiting Multiple Correlations Among Urban Regions for Crowd Flow Prediction

Qiang Zhou, Jing-Jing Gu*, Member, CCF, Chao Ling, Wen-Bo Li, Yi Zhuang, Jian Wang, Senior Member, CCF, Member, ACM, IEEE        

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2019-08-20 Revised:2020-01-17 Online:2020-03-05 Published:2020-01-20
  • Contact: Jing-Jing Gu E-mail:gujingjing@nuaa.edu.cn
  • About author:Qiang Zhou received his B.E. degree in software engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, in 2016. He is currently working toward his Ph.D. degree in the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing. His current research interests include urban computing, data mining, deep learning and spatio-temporal prediction.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant No. 61572253, and the Aviation Science Fund of China under Grant No. 2016ZC52030.

人群流动预测是城市计算领域重要的基础任务之一,准确的流量预测是交通管理、城市规划和公共安全管理等多个高阶应用的前提。但是,人群流动会受到不同区域之间多种复杂因素不同程度的影响。因此准确地表示面向人群流动模式的城市区域间相关性并充分地挖掘空间信息,对提高人群流动预测的准确性十分重要。在本文中,我们不仅考虑了城市区域的地理距离特征,也考虑了城市区域之间的交互频率和城市区域流量模式的相似性。我们将上述多个区域相关性建立成关系图结构。之后,我们提出一种基于深度学习的多图卷积门控循环单元(MGCGRU)模型,它能综合多重空间信息、时间信息、额外信息对城市人群流动进行准确的预测。针对时间信息,我们的模型使用较短的时间依赖特征以减少高动态环境下数据分布不稳定性的影响,并有利于减少空间复杂度。在上海的两个真实数据集上的实验结果表明,我们提出的模型优于其他先进的时空数据预测方法。由于考虑了更丰富的空间信息,我们的模型预测时的鲁棒性更高,且在多步预测中预测效果较好。

关键词: 人群流动预测, 多图卷积网络, 多重相关性挖掘

Abstract: Crowd flow prediction has become a strategically important task in urban computing, which is the prerequisite for traffic management, urban planning and public safety. However, due to variousness of crowd flows, multiple hidden correlations among urban regions affect the flows. Besides, crowd flows are also influenced by the distribution of Points-of-Interests (POIs), transitional functional zones, environmental climate, and different time slots of the dynamic urban environment. Thus, we exploit multiple correlations between urban regions by considering the mentioned factors comprehensively rather than the geographical distance and propose multi-graph convolution gated recurrent units (MGCGRU) for capturing these multiple spatial correlations. For adapting to the dynamic mobile data, we leverage multiple spatial correlations and the temporal dependency to build an urban flow prediction framework that uses only a little recent data as the input but can mine rich internal modes. Hence, the framework can mitigate the influence of the instability of data distributions in highly dynamic environments for prediction. The experimental results on two real-world datasets in Shanghai show that our model is superior to state-of-the-art methods for crowd flow prediction.

Key words: crowd flow prediction, multi-graph convolutional network, multiple correlations mining

[1] Zheng Y, Capra L, Wolfson O, Yang H. Urban computing:Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 2014, 5(3):Article No. 38.
[2] Zhang J B, Zheng Y, Qi D K. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proc. the 31st AAAI Conference on Artificial Intelligence, February 2017, pp.1655-1661.
[3] Zheng Z, Yang Y, Liu J et al. Deep and embedded learning approach for traffic flow prediction in urban informatics. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10):3927-3939.
[4] Sun J, Zhang J, Li Q et al. Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks. arXiv:1903.07789, 2019. https://arxiv.org/abs/1903.07789,August 2019.
[5] Du B, Peng H, Wang S et al. Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Transactions on Intelligent Transportation Systems. doi:10.1109/TITS.2019.2900481.
[6] Chai D, Wang L, Yang Q. Bike flow prediction with multigraph convolutional networks. In Proc. the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2018, pp.397-400.
[7] Geng X, Li Y, Wang L et al. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In Proc. the 33rd AAAI Conference on Artificial Intelligence, January 2019, pp.3656-3663.
[8] Ramaswami A, Russell A G, Culligan P J et al. Metaprinciples for developing smart, sustainable, and healthy cities. Science, 2016, 352(6288):940-943.
[9] Ai Y, Li Z, Gan M et al. A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Computing and Applications, 2019, 31(5):1665-1677.
[10] Shuman D I, Narang S K, Frossard P, Ortega A, Vandergheynst P. The emerging field of signal processing on graphs:Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine, 2013, 30(3):83-98.
[11] Li Y X, Zheng Y, Zhang H C, Chen L. Traffic prediction in a bike-sharing system. In Proc. the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2015, Article No. 33.
[12] Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J, Li Z. Deep multi-view spatial-temporal network for taxi demand prediction. In Proc. the 32nd AAAI Conference on Artificial Intelligence, February 2018, pp.2588-2595.
[13] Holmgren J, Aspegren S, Dahlstroma J. Prediction of bicycle counter data using regression. Procedia Computer Science, 2017, 113:502-507.
[14] Kumar S V, Vanajakshi L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review, 2015, 7(3):Article No. 21.
[15] Abadi A, Rajabioun T, Ioannou P A. Traffic flow prediction for road transportation networks with limited traffic data. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2):653-662.
[16] Li Y, Yu R, Shahabi C, Liu Y. Diffusion convolutional recurrent neural network:Data-driven traffic forecasting. In Proc. the 6th International Conference on Learning Representations, April 2018.
[17] Cheng A Y, Jiang X, Li Y F, Zhang C, Zhu H. Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method. Physica A:Statistical Mechanics and its Applications, 2017, 466:422-434.
[18] Achar A, Bharathi D, Kumar B A et al. Bus arrival time prediction:A spatial Kalman filter approach. IEEE Transactions on Intelligent Transportation Systems. doi:10.1109/TITS.2019.2909314.
[19] Liu J M, Sun L L, Li Q, Ming J C, Liu Y C, Xiong H. Functional zone based hierarchical demand prediction for bike system expansion. In Proc. the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2017, pp.957-966.
[20] Liu J M, Sun L L, Chen W W, Xiong H. Rebalancing bike sharing systems:A multi-source data smart optimization. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.1005-1014.
[21] Srivastava N, Mansimov E, Salakhutdinov R. Unsupervised learning of video representations using LSTMs. arXiv:1502.04681, 2015. https://arxiv.org/abs/1502.04681,August 2019.
[22] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473, 2014. https://arxiv.org/abs/1409.0473,August 2019.
[23] Cho K, van Merrienboer B, Gulcehre C et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078, 2014. https://arxiv.org/abs/1406.1078,August 2019.
[24] Thirumalai C, Koppuravuri R. Bike sharing prediction using deep neural networks. JOIV:International Journal on Informatics Visualization, 2017, 1(3):83-87.
[25] Shi X J, Chen Z R, Wang H, Yeung D Y, Wong W K, WOO W C. Convolutional LSTM network:A machine learning approach for precipitation nowcasting. In Proc. the 2015 Annual Conference on Neural Information Processing Systems, December 2015, pp.802-810.
[26] Bruna J, Zaremba W, Szlam A et al. Spectral networks and locally connected networks on graphs. arXiv:1312.6203, 2013. https://arxiv.org/abs/1312.6203,August 2019.
[27] Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In Proc. the 2016 Annual Conference on Neural Information Processing Systems, December 2016, pp.3844-3852.
[28] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907, 2016. https://arxiv.org/abs/1609.02907,August 2019.
[29] Zhang X, He L, Chen K, Luo Y, Zhou J, Wang F. Multi-view graph convolutional network and its applications on neuroimage analysis for Parkinson's disease. arXiv:1805.08801, 2018. https://arxiv.org/abs/1805.08801,August 2019.
[30] Yao H, Tang X, Wei H, Zheng G, Yu Y, Li Z. Modeling spatial-temporal dynamics for traffic prediction. arXiv:1803.01254, 2018. https://arxiv.org/abs/1803.01254,August 2019.
[31] Yuan N J, Zheng Y, Xie X et al. Discovering urban functional zones using latent activity trajectories. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(3):712-725.
[32] Erman J, Arlitt M F, Mahanti A. Traffic classification using clustering algorithms. In Proc. the 2nd Annual ACM Workshop on Mining Network Data, September 2006, pp.281-286.
[33] Cho K, van Merrienboer B, Bahdanau D et al. On the properties of neural machine translation:Encoder-decoder approaches. arXiv:1409.1259, 2014. https://arxiv.org/abs/1409.1259,August 2019.
[34] Friedman J H. Greedy function approximation:A gradient boosting machine. The Annals of Statistics, 2001, 29(5):1189-1232.
[35] Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks. In Proc. the 2014 Annual Conference on Neural Information Processing Systems, December 2014, pp.3104-3112.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 周笛;. A Recovery Technique for Distributed Communicating Process Systems[J]. , 1986, 1(2): 34 -43 .
[2] 陈世华;. On the Structure of Finite Automata of Which M Is an(Weak)Inverse with Delay τ[J]. , 1986, 1(2): 54 -59 .
[3] 王选; 吕之敏; 汤玉海; 向阳;. A High Resolution Chinese Character Generator[J]. , 1986, 1(2): 1 -14 .
[4] C.Y.Chung; 华宣仁;. A Chinese Information Processing System[J]. , 1986, 1(2): 15 -24 .
[5] 潘启敬;. A Routing Algorithm with Candidate Shortest Path[J]. , 1986, 1(3): 33 -52 .
[6] 章萃; 赵沁平; 徐家福;. Kernel Language KLND[J]. , 1986, 1(3): 65 -79 .
[7] 屈延文;. AGDL: A Definition Language for Attribute Grammars[J]. , 1986, 1(3): 80 -91 .
[8] 王建潮; 魏道政;. An Effective Test Generation Algorithm for Combinational Circuits[J]. , 1986, 1(4): 1 -16 .
[9] 陈肇雄; 高庆狮;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[10] 黄河燕;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
版权所有 © 《计算机科学技术学报》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn
总访问量: