{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T13:44:20Z","timestamp":1763991860446,"version":"3.45.0"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T00:00:00Z","timestamp":1763856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42471465","41871321"],"award-info":[{"award-number":["42471465","41871321"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3005702"],"award-info":[{"award-number":["2022YFC3005702"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Forecasting traffic flow is essential for optimizing resource allocation and improving urban traffic management efficiency. Despite significant advances in deep learning-based approaches, existing models still face challenges in effectively capturing dynamic spatio-temporal dependencies due to the limited representation of node transmission capabilities and distance-sensitive interactions in road networks. This limitation restricts the ability to capture temporal dynamics in spatial dependencies within traffic flow. To address this challenge, this study proposes a Transfer-aware Spatio-Temporal Graph Attention Network with Long-Short Term Memory and Transformer module (TAGAT-LSTM-trans). The model constructs a transfer probability matrix to represent each node\u2019s ability to transmit traffic characteristics and introduces a distance decay matrix to replace the traditional adjacency matrix, thereby offering a more accurate representation of spatial dependencies between nodes. The proposed model integrates a Graph Attention Network (GAT) to construct a TA-GAT module for capturing spatial features, while a gating network dynamically aggregates information across adjacent time steps. Temporal dependencies are modelled using LSTM and a Transformer encoder, with fully connected layers ensuring accurate forecasts. Experiments on real-world highway datasets show that TAGAT-LSTM-trans outperforms baseline models in spatio-temporal dependency modelling and traffic flow forecasting accuracy, validating the effectiveness of incorporating transmission awareness and distance decay mechanisms for dynamic traffic forecasting.<\/jats:p>","DOI":"10.3390\/ijgi14120459","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T13:09:25Z","timestamp":1763989765000},"page":"459","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Traffic Flow Forecasting Method Based on Transfer-Aware Spatio-Temporal Graph Attention Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3381-357X","authenticated-orcid":false,"given":"Yan","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8569-6893","authenticated-orcid":false,"given":"Xiaodi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}]},{"given":"Jipeng","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ins.2022.07.125","article-title":"A spatio-temporal sequence-to-sequence network for traffic flow prediction","volume":"610","author":"Cao","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.artint.2018.03.002","article-title":"Predicting citywide crowd flows using deep spatio-temporal residual networks","volume":"259","author":"Zhang","year":"2018","journal-title":"Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1109\/TITS.2020.2983763","article-title":"Temporal multi-graph convolutional network for traffic flow prediction","volume":"22","author":"Lv","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107530","DOI":"10.1016\/j.comnet.2020.107530","article-title":"Machine learning-based traffic prediction models for intelligent transportation systems","volume":"181","author":"Boukerche","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhuang, W., and Cao, Y. (2022). Short-term traffic flow prediction based on cnn-bilstm with multicomponent information. Appl. Sci., 12.","DOI":"10.3390\/app12178714"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9928073","DOI":"10.1155\/2021\/9928073","article-title":"Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings","volume":"2021","author":"Ren","year":"2021","journal-title":"J. Adv. Transp."},{"key":"ref_8","first-page":"1688","article-title":"Short-Term Traffic Flow Prediction Based on Spatio-Temporal Analysis and CNN Deep Learning","volume":"15","author":"Zhang","year":"2019","journal-title":"Transp. A Transp. Sci."},{"key":"ref_9","first-page":"112","article-title":"Traffic network speed prediction via multi-periodic-component spatial-temporal neural network","volume":"21","author":"Ren","year":"2021","journal-title":"J. Transp. Syst. Eng. Inf. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/j.matpr.2021.04.249","article-title":"Spatio-Temporal vehicle traffic flow prediction using multivariate CNN and LSTM model","volume":"81","author":"Narmadha","year":"2023","journal-title":"Mater. Today Proc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.ins.2021.07.007","article-title":"Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning","volume":"578","author":"Peng","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103526","DOI":"10.1016\/j.trc.2021.103526","article-title":"Traffic congestion propagation inference using dynamic Bayesian graph convolution network","volume":"135","author":"Luan","year":"2022","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8677","DOI":"10.1109\/TITS.2022.3203791","article-title":"Temporal-spatial quantum graph convolutional neural network based on Schr\u00f6dinger approach for traffic congestion prediction","volume":"24","author":"Qu","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.ins.2022.05.127","article-title":"Attention based spatiotemporal graph attention networks for traffic flow forecasting","volume":"607","author":"Wang","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fu, R., Zhang, Z., and Li, L. (2016, January 11\u201313). Using LSTM and GRU neural network methods for traffic flow prediction. Proceedings of the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China.","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3744","DOI":"10.1109\/TITS.2019.2932785","article-title":"DeepSTD: Mining spatio-temporal disturbances of multiple context factors for citywide traffic flow prediction","volume":"21","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13682","DOI":"10.1109\/TITS.2024.3396382","article-title":"A freeway traffic flow prediction model based on a generalized dynamic spatio-temporal graph convolutional network","volume":"25","author":"Gan","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"14154","DOI":"10.1109\/TITS.2025.3540852","article-title":"An attention-driven spatio-temporal deep hybrid neural networks for traffic flow prediction in transportation systems","volume":"26","author":"Ali","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, Y., Huang, J., Xu, H., Guo, J., and Su, L. (2023). Road traffic flow prediction based on dynamic spatiotemporal graph attention network. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-41932-6"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104356","DOI":"10.1016\/j.jtrangeo.2025.104356","article-title":"Assessing the impacts of transit systems and urban street features on bike-sharing ridership: A graph-based spatiotemporal analysis and prediction model","volume":"128","author":"Lu","year":"2025","journal-title":"J. Transp. Geogr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104440","DOI":"10.1016\/j.jtrangeo.2025.104440","article-title":"Exploring spatiotemporal dynamic of metro ridership and the influence of built environment factors at the station level: A case study of Nanjing, China","volume":"129","author":"Wang","year":"2025","journal-title":"J. Transp. Geogr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104069","DOI":"10.1016\/j.jtrangeo.2024.104069","article-title":"How do access and spatial dependency shape metro passenger flows","volume":"123","author":"Cui","year":"2025","journal-title":"J. Transp. Geogr."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14317","DOI":"10.1109\/JSEN.2020.3007809","article-title":"Sensing data supported traffic flow prediction via denoising schemes and ANN: A comparison","volume":"20","author":"Chen","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104646","DOI":"10.1016\/j.trc.2024.104646","article-title":"Traffic state estimation from vehicle trajectories with anisotropic Gaussian processes","volume":"163","author":"Wu","year":"2024","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Deng, H. (2025). Traffic-Forecasting Model with Spatio-Temporal Kernel. Electronics, 14.","DOI":"10.3390\/electronics14071410"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: Continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_29","first-page":"18","article-title":"Design and Case Study of Long Short Term Modeling for Next POI Recommendation","volume":"11","author":"An","year":"2024","journal-title":"Int. J. Eng. Res. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, S., Cui, Y., Xu, J., Li, L., Meng, L., Yang, W., Zhang, F., and Zhou, X. (2024). Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline. arXiv.","DOI":"10.1109\/TKDE.2025.3580465"},{"key":"ref_31","unstructured":"Kingma, D.P. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","article-title":"Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results","volume":"129","author":"Williams","year":"2003","journal-title":"J. Transp. Eng."},{"key":"ref_33","unstructured":"Zivot, E., and Wang, J. (2006). Vector Autoregressive Models for Multivariate Time Series. Modeling Financial Time Series with S-PLUS\u00ae, Springer."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_35","unstructured":"Li, Y., Yu, R., Shahabi, C., and Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv."},{"key":"ref_36","unstructured":"Guo, S., Lin, Y., Feng, N., Song, C., and Wan, H. (February, January 27). Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"16655","DOI":"10.1007\/s00521-022-07285-3","article-title":"Spatial-temporal dynamic semantic graph neural network","volume":"34","author":"Zhang","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"126146","DOI":"10.1016\/j.eswa.2024.126146","article-title":"CCNN-former: Combining convolutional neural network and Transformer for image-based traffic time series prediction","volume":"268","author":"Liu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"113336","DOI":"10.1016\/j.knosys.2025.113336","article-title":"Cross-city transfer learning for traffic forecasting via incremental distribution rectification","volume":"315","author":"Yang","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"104154","DOI":"10.1016\/j.jtrangeo.2025.104154","article-title":"Dynamic patterns of intercity mobility and influencing factors: Insights from similarities in spatial time-series","volume":"124","author":"Yu","year":"2025","journal-title":"J. Transp. Geogr."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pranolo, A., Saifullah, S., Utama, A.B., Wibawa, A.P., and Bastian, M. (2024). High-performance traffic volume prediction: An evaluation of RNN, GRU, and CNN for accuracy and computational trade-offs. BIO Web Conf., 148.","DOI":"10.1051\/bioconf\/202414802034"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Jiang, J., Han, C., Zhao, W.X., and Wang, J. (2023, January 7\u201314). Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction. Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA.","DOI":"10.1609\/aaai.v37i4.25556"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"00368504241283315","DOI":"10.1177\/00368504241283315","article-title":"TVGCN: Time-varying graph convolutional networks for multivariate and multifeature spatiotemporal series prediction","volume":"107","author":"Sun","year":"2024","journal-title":"Sci. Prog."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/12\/459\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T13:42:10Z","timestamp":1763991730000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/12\/459"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,23]]},"references-count":43,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["ijgi14120459"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14120459","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,23]]}}}