{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T13:09:28Z","timestamp":1763471368528,"version":"3.45.0"},"reference-count":48,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T00:00:00Z","timestamp":1763164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China National Geological survey project Research on the Tracking and Development Strategy of Geological Survey Information Technology","award":["20250009"],"award-info":[{"award-number":["20250009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Accurate prediction of urban grid-scale population inflow is crucial for smart city management and emergency response. However, existing methods struggle to model spatial heterogeneity and quantify prediction uncertainty, limiting their accuracy and decision-support capabilities. This paper proposes PDCDM (POI-enhanced Dual-Dimensional Conditional Diffusion Model), which integrates urban functional semantic awareness with conditional diffusion modeling. The model captures urban functional attributes through multi-scale Point of Interest (POI) feature representations and incorporates them into the diffusion generation process. A dual-dimensional Transformer architecture is employed to decouple the modeling of temporal dependencies and inter-grid interactions, enabling adaptive fusion of grid-level features with dynamic temporal patterns. Building upon this dual-dimensional modeling framework, the conditional diffusion mechanism generates probabilistic predictions with explicit uncertainty quantification. Real-world urban dataset validation demonstrates that PDCDM significantly outperforms existing state-of-the-art methods in prediction accuracy and uncertainty quantification. Comprehensive ablation studies validate the effectiveness of each component and confirm the model\u2019s practicality in complex urban scenarios.<\/jats:p>","DOI":"10.3390\/ijgi14110448","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T12:33:04Z","timestamp":1763469184000},"page":"448","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Urban Grid Population Inflow Prediction via POI-Enhanced Conditional Diffusion with Dual-Dimensional Attention"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9677-4152","authenticated-orcid":false,"given":"Zhiming","family":"Gui","sequence":"first","affiliation":[{"name":"College of Computer Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1622-5939","authenticated-orcid":false,"given":"Yuanchao","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Computer Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9106-039X","authenticated-orcid":false,"given":"Zhenji","family":"Gao","sequence":"additional","affiliation":[{"name":"Integrated Natural Resources Survey Center, CGS, No. 55 Yard, Honglian South Road, Xicheng District, Beijing 100055, China"},{"name":"Technology Innovation Center of Geological Information Engineering of Ministry of Natural Resources, Beijing 100055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.3390\/smartcities7030057","article-title":"Artificial Intelligence in Smart Cities\u2014Applications, Barriers, and Future Directions: A Review","volume":"7","author":"Wolniak","year":"2024","journal-title":"Smart Cities"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2020.01.002","article-title":"Urban Flow Prediction from Spatiotemporal Data Using Machine Learning: A Survey","volume":"59","author":"Xie","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1145\/2629592","article-title":"Urban Computing: Concepts, Methodologies, and Applications","volume":"5","author":"Zheng","year":"2014","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20200209","DOI":"10.1098\/rsta.2020.0209","article-title":"Time-Series Forecasting with Deep Learning: A Survey","volume":"397","author":"Lim","year":"2021","journal-title":"Philos. Trans. R. Soc. A"},{"doi-asserted-by":"crossref","unstructured":"Kontopoulou, V.I., Panagopoulos, A.D., Kakkos, I., and Matsopoulos, G.K. (2023). A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks. Future Internet, 15.","key":"ref_5","DOI":"10.3390\/fi15080255"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1080\/01621459.1970.10481180","article-title":"Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models","volume":"65","author":"Box","year":"1970","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1257\/jep.15.4.101","article-title":"Vector Autoregressions","volume":"15","author":"Stock","year":"2001","journal-title":"J. Econ. Perspect."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s11831-022-09815-7","article-title":"A Review on Kalman Filter Models","volume":"30","author":"Khodarahmi","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"unstructured":"Lipton, Z.C., Berkowitz, J., and Elkan, C. (2015). A Critical Review of Recurrent Neural Networks for Sequence Learning. arXiv.","key":"ref_9"},{"doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Long Short-Term Memory. Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","key":"ref_10","DOI":"10.1007\/978-3-642-24797-2"},{"doi-asserted-by":"crossref","unstructured":"Dey, R., and Salem, F.M. (2017, January 6\u20139). Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks. Proceedings of the 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA.","key":"ref_11","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A Comprehensive Survey on Graph Neural Networks","volume":"32","author":"Wu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., and Bengio, Y. (2018). Graph Attention Networks. arXiv.","key":"ref_13"},{"doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., and Zhu, Z. (2018, January 13\u201319). Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden.","key":"ref_14","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7327","DOI":"10.1109\/TPAMI.2021.3116668","article-title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","volume":"44","author":"Leach","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1007\/s10462-023-10698-8","article-title":"A Review of Predictive Uncertainty Estimation with Machine Learning","volume":"57","author":"Tyralis","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8770","DOI":"10.1109\/TITS.2024.3367779","article-title":"Uncertainty Quantification of Spatiotemporal Travel Demand With Probabilistic Graph Neural Networks","volume":"25","author":"Wang","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107801","DOI":"10.1016\/j.aap.2024.107801","article-title":"Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Crash Prediction","volume":"208","author":"Gao","year":"2024","journal-title":"Accid. Anal. Prev."},{"unstructured":"Hernandez-Lobato, J.M., and Adams, R. (2015, January 7\u20139). Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks. Proceedings of the 32nd International Conference on Machine Learning, Lille, France.","key":"ref_19"},{"doi-asserted-by":"crossref","unstructured":"Mengersen, K.L., Pudlo, P., and Robert, C.P. (2020). Bayesian Neural Networks: An Introduction and Survey. Case Studies in Applied Bayesian Data Science: CIRM Jean-Morlet Chair, Fall 2018, Springer International Publishing.","key":"ref_20","DOI":"10.1007\/978-3-030-42553-1"},{"unstructured":"Damianou, A., and Lawrence, N.D. (May, January 29). Deep Gaussian Processes. Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, Scottsdale, AZ, USA.","key":"ref_21"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1561\/2200000056","article-title":"An Introduction to Variational Autoencoders","volume":"12","author":"Kingma","year":"2019","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative Adversarial Networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"doi-asserted-by":"crossref","unstructured":"Huang, D., Song, X., Fan, Z., Jiang, R., Shibasaki, R., Zhang, Y., Wang, H., and Kato, Y. (2019, January 28\u201330). A Variational Autoencoder Based Generative Model of Urban Human Mobility. Proceedings of the 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, USA.","key":"ref_24","DOI":"10.1109\/MIPR.2019.00086"},{"doi-asserted-by":"crossref","unstructured":"Mo, Z., Fu, Y., and Di, X. (2022, January 8\u201312). Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks. Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China.","key":"ref_25","DOI":"10.1109\/ITSC55140.2022.9921791"},{"unstructured":"Yang, Y., Jin, M., Wen, H., Zhang, C., Liang, Y., Ma, L., Wang, Y., Liu, C., Yang, B., and Xu, Z. (2024). A Survey on Diffusion Models for Time Series and Spatio-Temporal Data. arXiv.","key":"ref_26"},{"unstructured":"Nichol, A.Q., and Dhariwal, P. (2021, January 18\u201324). Improved Denoising Diffusion Probabilistic Models. Proceedings of the 38th International Conference on Machine Learning, Virtual Event.","key":"ref_27"},{"key":"ref_28","first-page":"12438","article-title":"Improved Techniques for Training Score-Based Generative Models","volume":"33","author":"Song","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., and Poole, B. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. arXiv.","key":"ref_29"},{"unstructured":"Song, J., Meng, C., and Ermon, S. (2022). Denoising Diffusion Implicit Models. arXiv.","key":"ref_30"},{"unstructured":"Bilo\u0161, M., Rasul, K., Schneider, A., Nevmyvaka, Y., and G\u00fcnnemann, S. (2023, January 23\u201329). Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. Proceedings of the 40th International Conference on Machine Learning, Honolulu, HI, USA.","key":"ref_31"},{"doi-asserted-by":"crossref","unstructured":"Yuan, Y., Ding, J., Shao, C., Jin, D., and Li, Y. (2023, January 6\u201310). Spatio-Temporal Diffusion Point Processes. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, USA.","key":"ref_32","DOI":"10.1145\/3580305.3599511"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/TKDE.2014.2345405","article-title":"Discovering Urban Functional Zones Using Latent Activity Trajectories","volume":"27","author":"Yuan","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2269","DOI":"10.1109\/TKDE.2019.2915231","article-title":"Understanding Urban Dynamics via Context-Aware Tensor Factorization with Neighboring Regularization","volume":"32","author":"Wang","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s43762-022-00047-w","article-title":"Points of Interest (POI): A Commentary on the State of the Art, Challenges, and Prospects for the Future","volume":"2","author":"Psyllidis","year":"2022","journal-title":"Comput. Urban Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"20170401","DOI":"10.1098\/rsif.2017.0401","article-title":"Examining the Correlates and Drivers of Human Population Distributions across Low- and Middle-Income Countries","volume":"14","author":"Nieves","year":"2017","journal-title":"J. R. Soc. Interface"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.compenvurbsys.2008.05.001","article-title":"Kernel Density Estimation of Traffic Accidents in a Network Space","volume":"32","author":"Xie","year":"2008","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_38","first-page":"6840","article-title":"Denoising Diffusion Probabilistic Models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"unstructured":"Song, Y., and Ermon, S. (2019). Generative Modeling by Estimating Gradients of the Data Distribution. Adv. Neural Inf. Process. Syst., 32.","key":"ref_39"},{"unstructured":"Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B. (2021). DiffWave: A Versatile Diffusion Model for Audio Synthesis. arXiv.","key":"ref_40"},{"unstructured":"Alcaraz, J.L., and Strodthoff, N. (2022). Diffusion-Based Time Series Imputation and Forecasting with Structured State Space Models. Trans. Mach. Learn. Res. arXiv.","key":"ref_41"},{"unstructured":"Yuan, X., and Qiao, Y. (2024). Diffusion-TS: Interpretable Diffusion for General Time Series Generation. arXiv.","key":"ref_42"},{"key":"ref_43","first-page":"24804","article-title":"CSDI: Conditional Score-Based Diffusion Models for Probabilistic Time Series Imputation","volume":"34","author":"Tashiro","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"unstructured":"Nie, Y., Nguyen, N.H., Sinthong, P., and Kalagnanam, J. (2023). A Time Series Is Worth 64 Words: Long-Term Forecasting with Transformers. arXiv.","key":"ref_44"},{"unstructured":"Liu, Y., Hu, T., Zhang, H., Wu, H., Wang, S., Ma, L., and Long, M. (2024). iTransformer: Inverted Transformers Are Effective for Time Series Forecasting. arXiv.","key":"ref_45"},{"unstructured":"Wang, S., Wu, H., Shi, X., Hu, T., Luo, H., Ma, L., Zhang, J.Y., and Zhou, J. (2024). TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting. arXiv.","key":"ref_46"},{"unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., and Long, M. (2023). TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. arXiv.","key":"ref_47"},{"unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R. (2022, January 17\u201323). FEDformer: Frequency Enhanced Decomposed Transformer for Long-Term Series Forecasting. Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, USA.","key":"ref_48"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/11\/448\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T12:59:37Z","timestamp":1763470777000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/11\/448"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,15]]},"references-count":48,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["ijgi14110448"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14110448","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2025,11,15]]}}}