research-article

Context-aware Spatial-Temporal Neural Network for Citywide Crowd Flow Prediction via Modeling Long-range Spatial Dependency

Authors:

Diansheng Guo, and

Depeng JinAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 3

Article No.: 49, Pages 1 - 21

https://doi.org/10.1145/3477577

Published: 22 October 2021 Publication History

Abstract

Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims at predicting the inflow (the traffic of crowds entering a region in a given time interval) and outflow (the traffic of crowds leaving a region for other places) of each region in the city with knowing the historical flow data. In this article, we propose DeepSTN+, a deep learning-based convolutional model, to predict crowd flows in the metropolis. First, DeepSTN+ employs the ConvPlus structure to model the long-range spatial dependence among crowd flows in different regions. Further, PoI distributions and time factor are combined to express the effect of location attributes to introduce prior knowledge of the crowd movements. Finally, we propose a temporal attention-based fusion mechanism to stabilize the training process, which further improves the performance. Extensive experimental results based on four real-life datasets demonstrate the superiority of our model, i.e., DeepSTN+ reduces the error of the crowd flow prediction by approximately 10%–21% compared with the state-of-the-art baselines.

References

[1]

George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung. 2015. Time Series Analysis: Forecasting Andcontrol. John Wiley & Sons.

Digital Library

[2]

Jie Feng, Ziqian Lin, Tong Xia, Funing Sun, Diansheng Guo, and Yong Li. 2020. A sequential convolution network for populationflow prediction with explicitly correlation modelling. In Proceedings of the 29th International Joint Conference on Artificial Intelligence.C. Bessiere (Ed.). International Joint Conferences on Artificial Intelligence Organization, 1331–1337.

Digital Library

[3]

Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2015. Content-aware point of interest recommendation onlocation-based social networks. In Proceedings of the 29th AAAIConference on Artificial Intelligence. 1721–1727.

Digital Library

[4]

S. Guo, Youfang Lin, S. Li, Zhaoming Chen, and H. Wan. 2019. Deep spatial temporal 3d convolutional neural networks for traffic data forecasting. IEEE Transactions on Intelligent Transportation Systems 20, 10 (2019), 3913–3926.

[5]

James Douglas Hamilton. 1994. Time Series Analysis.Vol. 2. Princeton university press Princeton, NJ.

[6]

Minh X. Hoang, Yu Zheng, and Ambuj K. Singh. 2016. FCCF: Forecasting citywide crowd flows based on bigdata. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.

Digital Library

[7]

Minh X. Hoang, Yu Zheng, and Ambuj K. Singh. 2016. Forecasting citywide crowd flows based on bigdata. InProceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographical Information Systems. ACM, 1–10.

Digital Library

[8]

Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep networktraining by reducing internal covariate shift. In Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37 (ICML’15). JMLR.org, 448–456.

Digital Library

[9]

H. Jie, S. Li, S. Gang, and S. Albanie. 2017. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 42, 8 (2017), 2011–2023.

[10]

Wenwei Jin, Youfang Lin, Zhihao Wu, and Huaiyu Wan. 2018. Spatio-temporal recurrent convolutional networks for citywide short-term crowd flows prediction. In Proceedings of the 2nd International Conference on Computer and Data Analysis. ACM, 28–35.

Digital Library

[11]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Proceedings of the Advances in Neural Information Processing Systems. 1097–1105.

Digital Library

[12]

Yann A. LeCun, Léon Bottou, Genevieve B. Orr, and Klaus-Robert Müller. 2012. Efficient backprop. In Proceedings of the Neural Networks: Tricks of the Trade. Springer, 9–48.

[13]

Ting Li, Junbo Zhang, Kainan Bao, Yuxuan Liang, Yexin Li, and Yu Zheng. 2020. AutoST: Efficient neural architecture search forspatio-temporal prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 794–802.

Digital Library

[14]

Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In International Conference on Learning Representations. https://openreview.net/forum?id=SJiHXGWAZ.

[15]

Yexin Li, Yu Zheng, Huichu Zhang, and Lei Chen. 2015. Traffic prediction in a bike-sharing system. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 1–10.

Digital Library

[16]

Y. Liang, K. Ouyang, Y. Wang, Y. Liu, and D. S. Rosenblum. 2020. Revisiting Convolutional neural networks for citywide crowd flow analytics. In the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Springer.

[17]

Ziqian Lin, Jie Feng, Ziyang Lu, Yong Li, and Depeng Jin. 2019. DeepSTN+: Context-aware spatial-temporal neuralnetwork for crowd flow prediction in metropolis. InProceedings of the 33rd AAAI Conference on Artificial Intelligence. 1021–1027.

Digital Library

[18]

Zheyi Pan, Yuxuan Liang, W. Wang, Y. Yu, Y. Zheng, and Junbo Zhang. 2019. Urban traffic prediction from spatio-temporal data using deep meta learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1720–1730.

Digital Library

[19]

Jingbo Shang, Yu Zheng, Wenzhu Tong, Eric Chang, and Yong Yu. 2014. Inferring gas consumption and pollution emission of vehicles throughout a city. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1027–1036.

Digital Library

[20]

Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional neural network based image classification using small training sample size. In Proceedings of the 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR’15). 730–734. DOI:https://doi.org/10.1109/ACPR.2015.7486599

[21]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15, 1 (2014), 1929–1958.

Digital Library

[22]

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1–9.

[23]

Dong Wang, Wei Cao, Jian Li, and Jieping Ye. 2017. DeepSD: Supply-demand prediction for onlinecar-hailing services using deep neural networks. In Proceedings of the IEEE 33rd International Conference on Data Engineering. IEEE, 243–254.

[24]

Dawen Xia, Binfeng Wang, Huaqing Li, Yantao Li, and Zili Zhang. 2016. A distributed spatial–temporal weighted model on Map Reduce for short-term traffic flow forecasting. Neurocomputing 179, C, (2016), 246–263.

Digital Library

[25]

Shi Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Proceedings of the Advances in Neural Information Processing Systems. 802–810.

Digital Library

[26]

Fengli Xu, Jie Feng, Pengyu Zhang, and Yong Li. 2016. Context-aware real-time population estimation formetropolis. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 1064–1075.

Digital Library

[27]

Huaxiu Yao, XianfengTang, Hua Wei, Guanjie Zheng, and Zhenhui Li. 2019. Revisiting spatial-temporal similarity: A deeplearning framework for traffic prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 5668–5675.

Digital Library

[28]

Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxidemand prediction. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2588–2595.

Digital Library

[29]

Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018. Spatio-temporal graph convolutional networks: Adeep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18).

Digital Library

[30]

Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat Thalmann. 2013. Time-aware point-of-interest recommendation. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 363–372.

Digital Library

[31]

Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. 1655–1661.

Digital Library

[32]

Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, and Xiuwen Yi. 2016. DNN-based Prediction Model for Spatio-Temporaldata. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 1–4.

Digital Library

[33]

Junbo Zhang, Yu Zheng, Junkai Sun, and Dekang Qi. 2020. Flow Prediction in spatio-temporal networks based on multitask deep learning. IEEE Transactions on Knowledge and Data Engineering 32, 3 (2020), 468–478.

[34]

Ali Zonoozi, Jung-jae Kim, Xiao-Li Li, and Gao Cong. 2018. Periodic-CRN: A convolutional recurrent model for crowd density prediction with recurring periodic patterns. InProceedings of the 27th International Joint Conference on Artificial Intelligence. 3732–3738.

Cited By

Rong CLiu ZDing JLi Y(2024)Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic InformationACM Transactions on Knowledge Discovery from Data10.1145/364914118:6(1-17)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1145/3649141
Li XZhou HYao WLi WLiu BLin Y(2024)Intricate Spatiotemporal Dependency Learning for Temporal Knowledge Graph ReasoningACM Transactions on Knowledge Discovery from Data10.1145/364836618:6(1-19)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3648366
Zhang YLi TYuan YXu FYang FSun FLi Y(2024)Demand-driven Urban Facility Visit PredictionACM Transactions on Intelligent Systems and Technology10.1145/362523315:2(1-24)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3625233
Show More Cited By

Index Terms

Context-aware Spatial-Temporal Neural Network for Citywide Crowd Flow Prediction via Modeling Long-range Spatial Dependency
1. Applied computing
  1. Law, social and behavioral sciences

Recommendations

3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction
Crowd flow prediction is an essential task benefiting a wide range of applications for the transportation system and public safety. However, it is a challenging problem due to the complex spatio-temporal dependence and the complicated impact of urban ...
Read More
STA3DCNN: Spatial-Temporal Attention 3D Convolutional Neural Network for Citywide Crowd Flow Prediction
Neural Information Processing
Abstract
Crowd flow prediction is of great significance to the construction of smart cities, and recently became a research hot-spot. As road conditions are constantly changing, the forecasting crowd flows accurately and efficiently is a challenging task. ...
Read More
STGs: construct spatial and temporal graphs for citywide crowd flow prediction
Abstract
Crowd flow prediction is one of the most remarkable issues in a wide range of areas, from traffic control to public safety, and aims to forecast the inflow and outflow of crowds in each region of a city. Most existing studies adopt CNN and its ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 3

June 2022

494 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3485152

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2021

Accepted: 01 July 2021

Revised: 01 May 2021

Received: 01 May 2019

Published in TKDD Volume 16, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

National Key Research and Development Program of China
National Nature Science Foundation of China
Beijing Natural Science Foundation
Beijing National Research Center for Information Science and Technology
Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
916
Total Downloads

Downloads (Last 12 months)276
Downloads (Last 6 weeks)33

Other Metrics

View Author Metrics

Citations

Cited By

Rong CLiu ZDing JLi Y(2024)Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic InformationACM Transactions on Knowledge Discovery from Data10.1145/364914118:6(1-17)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1145/3649141
Li XZhou HYao WLi WLiu BLin Y(2024)Intricate Spatiotemporal Dependency Learning for Temporal Knowledge Graph ReasoningACM Transactions on Knowledge Discovery from Data10.1145/364836618:6(1-19)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3648366
Zhang YLi TYuan YXu FYang FSun FLi Y(2024)Demand-driven Urban Facility Visit PredictionACM Transactions on Intelligent Systems and Technology10.1145/362523315:2(1-24)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3625233
Derhab AMohiuddin IHalboob WAlmuhtadi J(2024)Crowd Congestion Forecasting Framework Using Ensemble Learning Model and Decision Making Algorithm: Umrah Use CaseIEEE Access10.1109/ACCESS.2024.339490512(67453-67469)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3394905
Uhlík OOkřinová PTokarevskikh AApeltauer TApeltauer J(2024)Real-time RSET prediction across three types of geometries and simulation training dataset: A comparative study of machine learning modelsDevelopments in the Built Environment10.1016/j.dibe.2024.10046118(100461)Online publication date: Apr-2024
https://doi.org/10.1016/j.dibe.2024.100461
Li TXi YWang HLi YTarkoma SHui P(2023)Learning Representations of Satellite Imagery by Leveraging Point-of-InterestsACM Transactions on Intelligent Systems and Technology10.1145/358934414:4(1-32)Online publication date: 8-May-2023
https://dl.acm.org/doi/10.1145/3589344
Ling SYu ZCao SZhang HHu S(2023)STHAN: Transportation Demand Forecasting with Compound Spatio-Temporal RelationshipsACM Transactions on Knowledge Discovery from Data10.1145/356557817:4(1-23)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3565578
Jin GSha HXi ZHuang J(2023)Urban hotspot forecasting via automated spatio-temporal information fusionApplied Soft Computing10.1016/j.asoc.2023.110087136:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110087
Zhang SLiu YXiao YHe R(2022)Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed predictionJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.08.02534:10(8996-9010)Online publication date: Nov-2022
https://doi.org/10.1016/j.jksuci.2022.08.025
Kong XWei XZhang JXing WLu W(2022)JointGraph: joint pre-training framework for traffic forecasting with spatial-temporal gating diffusion graph attention networkApplied Intelligence10.1007/s10489-022-04218-453:11(13723-13740)Online publication date: 15-Oct-2022
https://dl.acm.org/doi/10.1007/s10489-022-04218-4

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents