Fine-Grained Urban Flow Prediction
Open accessAbstract
Urban flow prediction benefits smart cities in many aspects, such as traffic management and risk assessment. However, a critical prerequisite for these benefits is having fine-grained knowledge of the city. Thus, unlike previous works that are limited to coarse-grained data, we extend the horizon of urban flow prediction to fine granularity which raises specific challenges: 1) the predominance of inter-grid transitions observed in fine-grained data makes it more complicated to capture the spatial dependencies among grid cells at a global scale; 2) it is very challenging to learn the impact of external factors (e.g., weather) on a large number of grid cells separately. To address these two challenges, we present a Spatio-Temporal Relation Network (STRN) to predict fine-grained urban flows. First, a backbone network is used to learn high-level representations for each cell. Second, we present a Global Relation Module (GloNet) that captures global spatial dependencies much more efficiently compared to existing methods. Third, we design a Meta Learner that takes external factors and land functions (e.g., POI density) as inputs to produce meta knowledge and boost model performances. We conduct extensive experiments on two real-world datasets. The results show that STRN reduces the errors by 7.1% to 11.5% compared to the state-of-the-art method while using much fewer parameters. Moreover, a cloud-based system called UrbanFlow 3.0 has been deployed to show the practicality of our approach.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Tracking locations and analysing mobility has contributed to many day-to-day applications such as traffic management, routing, recommender systems, and cellular network load balancing.
“Tracking locations and analysing mobility has contributed to many day-to-day applications, for example, traffic management and forecasting [87,88], routing individuals [89] and delivery packages [90], recommender systems [91], dynamic load balancing systems for cellular networks [92], to name a few.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [88]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Self-citationHochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667 · see citing works
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
Normalized cuts and image segmentation
Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,842 · see citing works
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157 · see citing works
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738 · see citing works
Yu, T., Yin, H.-T., & Zhu, Z.-X. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/505 · Cited by 5,695 · see citing works
Testing for unit roots in autoregressive-moving average models of unknown order
Saïd, S. E., & Dickey, D. A. (1984) · Biometrika · doi:10.1093/biomet/71.3.599 · Cited by 3,258 · see citing works
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Wu, Z., Pan, S., Long, G., Jiang, J. B., & Zhang, C. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/264 · Cited by 3,158 · see citing works
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301922 · Cited by 3,123 · see citing works
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
Zhang, J., Zheng, Y., & Qi, D. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10735 · Cited by 2,396 · see citing works
Self-citationZheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2629592 · Cited by 1,514 · see citing works
Self-citationA simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D. G. T., Malinowski, M., Pascanu, R., Battaglia, P., & Lillicrap, T. (2017) · arXiv · doi:10.48550/arxiv.1706.01427 · Cited by 1,281 · see citing works
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Yao, H., Wu, F. Q., Ke, J., Tang, X., Jia, Y., Lu, S., Gong, P., Ye, J., & Li, Z. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11836 · Cited by 1,222 · see citing works
Discovering regions of different functions in a city using human mobility and POIs
Yuan, J., Zheng, Y., & Xie, X. (2012) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339561 · Cited by 1,118 · see citing works
Self-citationTemporal Relational Reasoning in Videos
Zhou, B., Andonian, A., Oliva, A., & Torralba, A. B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_49 · Cited by 1,104 · see citing works
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Yao, H., Tang, X., Wei, H., Zheng, G., & Li, Z. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015668 · Cited by 848 · see citing works
Videos as Space-Time Region Graphs
Wang, X., & Gupta, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_25 · Cited by 786 · see citing works
DNN-based prediction model for spatio-temporal data
Zhang, J., Zheng, Y., Qi, D., Li, R., & Yi, X. (2016) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2996913.2997016 · Cited by 729 · see citing works
Self-citationUrban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning
Pan, Z., Liang, Y., Wang, W., Yu, Y. H., Zheng, Y., & Zhang, J. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330884 · Cited by 598 · see citing works
Self-citationGeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction
Liang, Y., Ke, S., Zhang, J., Yi, X., & Zheng, Y. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/476 · Cited by 539 · see citing works
Self-citationGraph-Based Global Reasoning Networks
Chen, Y., Rohrbach, M., Yan, Z., Shuicheng, Y., Feng, J., & Kalantidis, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00052 · Cited by 538 · see citing works
Deep Spatial–Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting
Guo, S., Lin, Y., Li, S., Chen, Z., & Wan, H. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2906365 · Cited by 458 · see citing works
Traffic prediction in a bike-sharing system
Li, Y., Zheng, Y., Zhang, H., & Chen, L. (2015) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2820783.2820837 · Cited by 360 · see citing works
Self-citationFlow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning
Zhang, J., Zheng, Y., Sun, J., & Qi, D. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2891537 · Cited by 327 · see citing works
Self-citationContextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction
Liu, L., Qiu, Z., Li, G., Wang, Q., Ouyang, W., & Lin, L. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2915525 · Cited by 278 · see citing works
DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis
Lin, Z., Feng, J., Lu, Z., Li, Y., & Jin, D. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011020 · Cited by 276 · see citing works
Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-Grained Air Quality
Qi, Z., Wang, T., Song, G., Hu, W., Li, X., & Zhang, Z. M. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2823740 · Cited by 275 · see citing works
Deep Multi-View Spatial-Temporal Network for Taxi Demand PredictionPreprint
Yao, H., Wu, F. Q., Ke, J., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.08714 · Cited by 256 · see citing works
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows PredictionPreprint
Zhang, J., Zheng, Y., & Qi, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.00081 · Cited by 206 · see citing works
Self-citationGSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction
Fang, S., Zhang, Q., Meng, G.-F., Xiang, S., & Pan, C.-H. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/317 · Cited by 191 · see citing works
Prediction of human emergency behavior and their mobility following large-scale disaster
Song, X., Zhang, Q., Sekimoto, Y., & Shibasaki, R. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623628 · Cited by 183 · see citing works
Beyond Grids: Learning Graph Representations for Visual Recognition
Li, Y., & Gupta, A. (2018) · neural information processing systems · Cited by 165 · see citing works
Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction
Liu, L., Zhen, J., Li, G., Zhan, G., He, Z., Du, B., & Lin, L. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3002718 · Cited by 164 · see citing works
Huang, C., Zhang, J., Zheng, Y., & Chawla, N. V. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3271793 · Cited by 151 · see citing works
Self-citationHoang, M., Zheng, Y., & Singh, A. K. (2016) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2996913.2996934 · Cited by 142 · see citing works
Self-citationLiang, Y., Ouyang, K., Jing, L., Ruan, S., Liu, Y., Zhang, J., Rosenblum, D. S., & Zheng, Y. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330646 · Cited by 141 · see citing works
Self-citationFan, Z., Song, X., Shibasaki, R., & Adachi, R. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2804277 · Cited by 124 · see citing works
Huang, C., Zhang, C., Zhao, J., Wu, X., Yin, D., & Chawla, N. V. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313730 · Cited by 105 · see citing works
Big Data in Complex and Social Networks
Thai, M. T., Wu, W., & Xiong, H. (2016) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315396705 · Cited by 104 · see citing works
Topological Spatio–Temporal Reasoning and Representation
Müller, P. (2002) · Computational Intelligence · doi:10.1111/1467-8640.00196 · Cited by 96 · see citing works
Liu, L., Zhang, R., Peng, J., Li, G., Du, B., & Lin, L. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240681 · Cited by 70 · see citing works
Fine-Grained Urban Flow Inference
Ouyang, K., Liang, Y., Liu, Y., Tong, Z., Ruan, S., Zheng, Y., & Rosenblum, D. S. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3017104 · Cited by 60 · see citing works
Self-citationHierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting
Wu, X., Huang, C., Zhang, C., & Chawla, N. V. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380296 · Cited by 57 · see citing works
Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction
Pan, Z., Wang, Z., Wang, W., Yu, Y. H., Zhang, J., & Zheng, Y. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357832 · Cited by 49 · see citing works
Self-citationInferring Traffic Cascading Patterns
Liang, Y., Jiang, Z., & Zheng, Y. (2017) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3139958.3139960 · Cited by 40 · see citing works
Self-citationMincut pooling in Graph Neural Networks.
Bianchi, F. M., Grattarola, D., & Alippi, C. (2019) · Cited by 31 · see citing works
Feng, J., Lin, Z., Xia, T., Sun, F., Guo, D., & Li, Y. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/185 · Cited by 18 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Chao Huang, Junbo Zhang, Yu Zheng, and Nitesh V Chawla. 2018. DeepCrime: Attentive hierarchical recurrent networks for crime prediction. In ACM Interna- tional Conference on Information and Knowledge Management. 1423-1432.
- Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations.
- Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S. Rosenblum, and Yu Zheng. 2019. UrbanFM: Inferring Fine-Grained Urban Flows. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 3132-3142.
- Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. 2015. Convolutional LSTM network: A machine learning ap- proach for precipitation nowcasting. In Advances in neural information processing systems. 802-810.
- Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018. Spatio-temporal Graph Con- volutional Networks: A Deep Learning Framework for Traffic Forecasting. In IJCAI.
Cited by
Sorted by citations
Long sequence time-series forecasting with deep learning: A survey
Chen, Z., Ma, M., Li, T., Wang, H., & Li, C. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101819 · Cited by 359 · see citing works
A systematic review for transformer-based long-term series forecasting
Su, L., Zuo, X., Li, R., Wang, X., Zhao, H., & Huang, B. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11044-2 · Cited by 219 · see citing works
Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook
Zou, X., Yan, Y., Hao, X., Hu, Y., Wen, H., Liu, E., Zhang, J., Li, Y., Li, T., Zheng, Y., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102606 · Cited by 117 · see citing works
Self-citationSpatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction
Li, Z., Huang, C., Xia, L., Xu, Y., & Pei, J. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00269 · Cited by 96 · see citing works
When do contrastive learning signals help spatio-temporal graph forecasting?
Liu, X., Liang, Y., Huang, C., Zheng, Y., Hooi, B., & Zimmermann, R. (2022) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3557915.3560939 · Cited by 82 · see citing works
Self-citationMulti-view dynamic graph convolution neural network for traffic flow prediction
Huang, X., Ye, Y., Yang, X., & Xiong, L. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119779 · Cited by 81 · see citing works
Pyramid: Enabling Hierarchical Neural Networks with Edge Computing
He, Q., Dong, Z., Chen, F., Deng, S., Liang, W., & Yang, Y. J. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3511990 · Cited by 80 · see citing works
A survey of urban visual analytics: Advances and future directions
Deng, Z., Weng, D., Liu, S., Tian, Y., Xu, M., & Wu, Y. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0275-7 · Cited by 74 · see citing works
Spatio-temporal hierarchical MLP network for traffic forecasting
Qin, Y., Luo, H., Zhao, F., Fang, Y., Tao, X., & Wang, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.063 · Cited by 59 · see citing works
Guo, S., Lin, Y., Gong, L., Wang, C., Zhou, Z., Shen, Z., Huang, Y., & Wan, H. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00125 · Cited by 50 · see citing works
Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective
Fang, Y., Liang, Y., Hui, B., Shao, Z., Deng, L., Liu, X., Jiang, X., & Zheng, K. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709177 · Cited by 48 · see citing works
Self-citationOuyang, X., Yang, Y. Z., Zhang, Y., Zhou, W., Wan, J., & Du, S. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110885 · Cited by 45 · see citing works
Multi-mode dynamic residual graph convolution network for traffic flow prediction
Huang, X., Ye, Y., Ding, W., Yang, X., & Xiong, L. (2022) · Information Sciences · doi:10.1016/j.ins.2022.07.008 · Cited by 43 · see citing works
PCR: A Parallel Convolution Residual Network for Traffic Flow Prediction
Zuo, C., Zhang, X., Zhao, G., & Yan, L. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3525656 · Cited by 43 · see citing works
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
Yang, Y., Jin, M., Wen, H., Zhang, C., Liang, Y., Ma, L., Wang, Y., Liu, C., Yang, B., Xu, Z., et al. (2025) · ACM Computing Surveys · doi:10.1145/3783986 · Cited by 107 · see citing works
Self-citationUrban regional function guided traffic flow prediction
Wang, K., Liu, L., Liu, Y., Li, G., Zhou, F., & Lin, L. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.109 · Cited by 37 · see citing works
Spatio-Temporal Neural Structural Causal Models for Bike Flow Prediction
Deng, P., Zhao, Y., Liu, J., Jia, X., & Wang, M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25542 · Cited by 34 · see citing works
A review of time series forecasting and spatio-temporal series forecasting in deep learning
Yu, Q., Yang, G., Wang, X., Shi, Y., Feng, Y., & Liu, A. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07632-w · Cited by 38 · see citing works
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
Liu, C., Xiao, Z. Q., Wang, D., Wang, L., Jiang, H., Chen, H., & Yu, J. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3137381 · Cited by 30 · see citing works
Predicting Collective Human Mobility via Countering Spatiotemporal Heterogeneity
Zhou, Z., Yang, K., Liang, Y., Wang, B., Chen, H., & Wang, Y. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3296501 · Cited by 29 · see citing works
Self-citationSpatio-Temporal Memory Augmented Multi-Level Attention Network for Traffic Prediction
Liu, Y., Guo, B., Meng, J., Zhang, D., & Yu, Z. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3322405 · Cited by 27 · see citing works
Crowd Flow Prediction for Irregular Regions with Semantic Graph Attention Network
Li, F., Feng, J., Yan, H., Jin, D., & Li, Y. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501805 · Cited by 27 · see citing works
A multi-mode traffic flow prediction method with clustering based attention convolution LSTM
Huang, X., Ye, Y., Wang, C., Yang, X., & Xiong, L. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02770-z · Cited by 24 · see citing works
Unsupervised Domain Adaptation for Crime Risk Prediction Across Cities
Zhou, B., Chen, L., Zhao, S., Li, S., Zheng, Z., & Pan, G. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3207987 · Cited by 24 · see citing works
Road Network-Guided Fine-Grained Urban Traffic Flow Inference
Liu, L., Liu, M., Li, G., Wu, Z., Lin, J., & Lin, L. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3327386 · Cited by 24 · see citing works
Xiao, C., Zhou, J., Xiao, Y., Huang, J., & Xiong, H. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671992 · Cited by 22 · see citing works
TAP: Traffic Accident Profiling via Multi-Task Spatio-Temporal Graph Representation Learning
Liu, Z., Yang, C., Xia, F., Bian, J., Zhu, B., Shen, G., & Kong, X. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3564594 · Cited by 21 · see citing works
Spatio-Temporal Multivariate Probabilistic Modeling for Traffic Prediction
An, Y., Li, Z., Li, X., Liu, W., Yang, X., Sun, H., Chen, M., Zheng, Y., & Gong, Y. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3539680 · Cited by 20 · see citing works
Self-citationSpatial Heterophily Aware Graph Neural Networks
Xiao, C., Zhou, J., Huang, J., Xu, T., & Xiong, H. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599510 · Cited by 20 · see citing works
A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection
Xiao, C., Zhou, J., Huang, J., Zhu, H., Xu, T., Dou, D., & Xiong, H. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00062 · Cited by 18 · see citing works
Zhang, S., Deng, B., & Yang, D. (2022) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-022-00121-6 · Cited by 17 · see citing works
Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference
Hu, J.-F., Liang, Y., Fan, Z.-C., Liu, L., Yin, Y., & Zimmermann, R. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3293814 · Cited by 34 · see citing works
Self-citationTang, T., Mao, J., Liu, R., Liu, Z., Wang, Y., & Huang, D. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3447611 · Cited by 15 · see citing works
Self-citationHigh-Resolution Urban Flows Forecasting With Coarse-Grained Spatiotemporal Data
Xu, Z., Kang, Y., & Cao, Y. H. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3153750 · Cited by 15 · see citing works
Li, L., Bi, J., Yang, K., & Luo, F. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120911 · Cited by 14 · see citing works
Contrastive learning for traffic flow forecasting based on multi graph convolution network
Guo, K., Tian, D., Hu, Y., Sun, Y., Qian, S., Zhou, J., Gao, J., & Yin, B. (2023) · IET Intelligent Transport Systems · doi:10.1049/itr2.12451 · Cited by 13 · see citing works
Dual-track spatio-temporal learning for urban flow prediction with adaptive normalization
Li, X., Gong, Y., Liu, W., Yin, Y., Zheng, Y., & Nie, L. (2024) · Artificial Intelligence · doi:10.1016/j.artint.2024.104065 · Cited by 13 · see citing works
Self-citationHeterogeneous Modular Traffic Prediction Based on Multilayer Graph Convolutional Network
Chang, M., Ding, Z., Zhao, Z., & Cai, Z. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3358747 · Cited by 13 · see citing works
Periodic residual learning for crowd flow forecasting
Wang, C., Liang, Y., & Tan, G. (2022) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3557915.3560947 · Cited by 11 · see citing works
Self-citationCityCAN: Causal Attention Network for Citywide Spatio-Temporal Forecasting
Wang, C., Liang, Y., & Tan, G. (2024) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3616855.3635764 · Cited by 11 · see citing works
Self-citationHe, S., & Shin, K. G. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512145 · Cited by 11 · see citing works
Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction
Zhao, Y. P., Deng, P., Liu, J., Jia, X., & Wang, M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25619 · Cited by 11 · see citing works
Temporal super-resolution traffic flow forecasting via continuous-time network dynamics
Xie, Y., Xiong, Y., Zhang, J., Chen, C., Zhang, Y., Zhao, J. M., Jiao, Y., Zhao, J., & Zhu, Y. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01887-6 · Cited by 10 · see citing works
Self-citationRethinking Urban Mobility Prediction: A Multivariate Time Series Forecasting Approach
Cheng, J., Li, K., Liang, Y., Sun, L., Yan, J., & Wu, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3498054 · Cited by 9 · see citing works
Self-citationScore-based Graph Learning for Urban Flow Prediction
Wang, P., Luo, X., Tai, W., Zhang, K., Trajcevski, G., & Zhou, F. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3655629 · Cited by 9 · see citing works
Chen, H., Han, H., Chen, Y., Chen, Z., Gao, R., & Li, X. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06539-2 · Cited by 8 · see citing works
Peng, H., Li, W., Jin, C., Zhang, Y., Guan, J., Yang, H., & Zhou, S. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3735646 · Cited by 8 · see citing works
DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction
Ren, S., Guo, B., Cao, L., Li, K., Liu, J., & Yu, Z. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3526087 · Cited by 9 · see citing works
Multi-Task Synchronous Graph Neural Networks for Traffic Spatial-Temporal Prediction
He, L., Jin, D., Li, X., Huang, J., & Yoo, J. (2021) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3474717.3483921 · Cited by 8 · see citing works
Li, L., Bi, J., Yang, K., Luo, F., & Yang, L. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945489 · Cited by 8 · see citing works
A study on deep learning model based on global–local structure for crowd flow prediction
Go, H., & Park, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-63310-6 · Cited by 7 · see citing works
Traffic prediction in time series, spatialtemporal, and OD data: A systematic survey
Du, K., Guo, X., Li, L., Song, J., Shi, Q., Hu, M., & Fang, J. (2025) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2025.03.001 · Cited by 10 · see citing works
Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural Networks
Cai, Z., Jiang, R., Lian, X., Yang, C., Wang, Z., Fan, Z., Tsubouchi, K., Kobayashi, H. H., Song, X., & Shibasaki, R. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3310789 · Cited by 7 · see citing works
Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network
Shen, Z., Cai, K., Fang, Q., & Luo, X. (2024) · Journal of Advanced Transportation · doi:10.1155/2024/4349402 · Cited by 7 · see citing works
Yan, H., Jin, G., Wang, D., Liu, Y., & Li, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-24521-3_2 · Cited by 7 · see citing works
Human Mobility Prediction Based on Trend Iteration of Spectral Clustering
Jia, W., Zhao, S., & Zhao, K. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3288132 · Cited by 7 · see citing works
Graph Node Scoring for the Analysis and Visualisation of Mobility Networks and Data
Márquez, R. A. M., & Patanè, G. (2024) · Urban Science · doi:10.3390/urbansci8040155 · Cited by 6 · see citing works
Probabilistic Fine-Grained Urban Flow Inference with Normalizing Flows
Zhong, T., Yu, H., Li, R., Xu, X., Luo, X., & Zhou, F. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747900 · Cited by 6 · see citing works
Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
Hu, C., Liu, X., Wu, S., Yu, F., Song, Y., & Zhang, J. (2023) · Applied Sciences · doi:10.3390/app13127271 · Cited by 5 · see citing works
Self-citationA Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units
Chen, L., Fang, J. H., Liu, T., Cao, S., & Wang, L. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00111 · Cited by 5 · see citing works
Commuting flow prediction using OpenStreetMap data
Atwal, K. S., Anderson, T. M., Pfoser, D., & Züfle, A. E. (2025) · Computational Urban Science · doi:10.1007/s43762-025-00161-5 · Cited by 4 · see citing works
Li, J., Dong, W., & Gui, X. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06774-7 · Cited by 4 · see citing works
Universal Transfer Framework for Urban Spatiotemporal Knowledge Based on Radial Basis Function
Chiu, S.-M., Liou, Y.-S., Chen, Y., Lee, C., Shang, R.-K., Chang, T.-Y., & Zimmermann, R. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3382267 · Cited by 4 · see citing works
Self-citationSTKOpt: Automated Spatio-Temporal Knowledge Optimization for Traffic Prediction
Hong, Y., Chen, L., Wang, L., Xu, X., Luo, G., Wang, C., & Chen, L. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714598 · Cited by 3 · see citing works
Liu, Y., Zhang, J., Dou, R., Zhou, X., Xu, X., Wang, S., & Qi, L. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00295 · Cited by 3 · see citing works
Self-citationA Survey on Modern Deep Learning Techniques for Traffic Volume Prediction
Qin, Z., Wang, M., Zhu, H., Lee, W.-C., Cui, N., Yu, J., Tian, Z., Hong, Y., & Yin, J. (2026) · Data Science and Engineering · doi:10.1007/s41019-025-00340-8 · Cited by 2 · see citing works
QuoGNN: Quotient Graph Neural Network for Urban Flow Forecasting
Gou, X., Han, P., & Zhang, X. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020795 · Cited by 2 · see citing works
Chen, L., Wang, X., & Wang, L. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3517332 · Cited by 2 · see citing works
Effective AOI-level Parcel Volume Prediction: When Lookahead Parcels Matter
Xiang, Y., Fang, J. H., Li, C., Yuan, H., Song, Y., & Chen, J. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709441 · Cited by 2 · see citing works
SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction
Tan, X., Chen, K., Deng, M., Liu, B., Zhao, Z., Tu, Y., & Wu, S. (2025) · Mathematics · doi:10.3390/math13101686 · Cited by 2 · see citing works
Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning
Huang, H., He, S., & Tabatabaie, M. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00086 · Cited by 2 · see citing works
Overcoming Catastrophic Forgetting in Continual Fine-Grained Urban Flow Inference
Xu, X., Zhong, T., Yu, H., Zhou, F., & Trajcevski, G. (2024) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3660523 · Cited by 1 · see citing works
Crowd Flow Prediction from Mobile Traces Through Time Series PoI Stay Counts
Yamada, O., Matsuda, Y., Suwa, H., & Yasumoto, K. (2024) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp61445.2024.00066 · Cited by 1 · see citing works
Mu, W., Qu, H., Gong, Y., Nie, X., & Yin, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128102 · Cited by 1 · see citing works
Xin, H., Lu, X., Sun, Y., Zhu, N., Xu, T., Zhou, J., & Xiong, H. (2026) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3793854 · Cited by 1 · see citing works
Li, X., & Wang, C. (2023) · IET Intelligent Transport Systems · doi:10.1049/itr2.12431 · Cited by 1 · see citing works
A Traffic Flow Prediction Method Using Road Network Data
Tanaka, H., & Kasamatsu, D. (2023) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce59613.2023.10315616 · Cited by 1 · see citing works
A Novel Framework for Joint Learning of City Region Partition and Representation
Deng, M., Zhang, W., Zhao, J. M., Wang, Z., Zhou, M., Luo, J., & Chen, C. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3652857 · Cited by 1 · see citing works
Time Series Analysis in the Web: Recent Advances and Future Trends
Jin, M., Jin, W., Xue, H., Salim, F. D., & Liang, Y. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3715853 · Cited by 1 · see citing works
Self-citationDong, L., Zhang, X., & Liu, L. (2022) · IEEE International Conference on Intelligent Transportation Engineering (ICITE) · doi:10.1109/icite56321.2022.10101467 · Cited by 1 · see citing works
Chen, H., & Han, H. (2023) · International Conference on Intelligent Management and Software Engineering (IMSE) · doi:10.1109/imse61332.2023.00038
Predictive Crime Rate Analysis System
Singh, I., Bhandari, G., & Khatwani, S. (2025) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2025.70309
Exploring the Influence of Behavioral Intention in Next POI Recommendation by Hypergraph Learning
Ni, H., Jin, C., Wei, Y., & Shi, L. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6963-9_25
NDA-CoMBS: Network Demand-Aware Autonomous Cooperative Decision-Making for Mobile Base Stations
Wu, Y., Luo, R., Ji, J., Luo, G., Yang, D., Wang, C., & Chen, L. (2024) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn63567.2024.00114
Long-Term Urban Flow Prediction Against Data Distribution Shift: A Causal Perspective
Liu, Y., Zhou, Q., Li, H., Zhuang, F., & Gu, J. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3612033
Landformer: years ahead long-term traffic forecasting with land use information
Zheng, X., Bagloee, S. A., & Sarvi, M. (2025) · Transportmetrica A Transport Science · doi:10.1080/23249935.2025.2565379
Multimodal Traffic Flow Prediction Based on Causal Inference and Hidden Markov Chains
Xu, S., Cui, Z., Wang, B., Pu, Z., Huang, C., Ren, Y., Li, A., & Yu, H. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-9354-5_16
Alleviating Forgetting in Fine-Grained Urban Flow Inference via Incremental Neural Operator
Gao, Q., Song, X., Huang, L., Trajcevski, G., Zhou, F., & Chen, X. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3856800
- Authors
- Liang, Y., Ouyang, K., Sun, J., Wang, Y., Zhang, J., Zheng, Y., Rosenblum, D., & Zimmermann, R.
- Affiliations
- National University of Singapore (SG)
- Jingdong (China) (CN)
- Southwest Jiaotong University (CN)
- George Mason University (US)
- Venue
- Proceedings of the Web Conference 2021
- Publisher
- ACM
- Published
- 2021-04-19
- Pages
- 1833-1845
- OpenAlex
- W3156972038
- Cited by
- 89
- References
- 46
- Citations by year
- 2026: 4 · 2025: 18 · 2024: 21 · 2023: 24 · 2022: 19 · 2021: 3
- FWCI
- 34.30
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, other-oa)
- Funders
- National Natural Science Foundation of China (72061127001, 62076191)
- Ministry of Education, India
- Beijing Nova Program (Z201100006820053)
- Topics
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Physical Sciences › Engineering › Building and Construction › Urban Design and Spatial Analysis
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- external factors, weather effects, meta-learning, smart cities, traffic management
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
