{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T18:43:16Z","timestamp":1776105796591,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"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":["42371418"],"award-info":[{"award-number":["42371418"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Traffic flow prediction is one of the most important and attractive topics in geographical information science (GIS), traffic management, and logistics. Traffic flows exhibit significant complexity and dynamics, requiring a thorough understanding of their spatiotemporal evolution patterns for accurate prediction and analysis. Existing studies utilizing deep learning for traffic flow prediction often suffer from distribution shift issues, leading to poor generalization capabilities when dealing with data that has different spatiotemporal distributions. Based on this, we propose a traffic flow prediction model based on prompt learning, leveraging graph convolutional networks to focus on the spatiotemporal dependencies of traffic flows. The model utilizes spatiotemporal context learning capabilities to capture the periodic states of traffic flows, enhancing the extraction of spatiotemporal features by integrating spatiotemporal information. Experimental results show that the spatiotemporal traffic flow prediction model equipped with a spatiotemporal prompt learning module outperforms several mainstream benchmark models in terms of predictive performance. The model presents efficient learning performance that reaches optimal state in a short period of time, reduces the impact of distribution shifts, and can be adapted to spatiotemporal traffic flow data under varying spatiotemporal contexts.<\/jats:p>","DOI":"10.3390\/ijgi14010011","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T07:00:37Z","timestamp":1735628437000},"page":"11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Temporal-Spatial Traffic Flow Prediction Model Based on Prompt Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8014-6172","authenticated-orcid":false,"given":"Siteng","family":"Cai","sequence":"first","affiliation":[{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8856-8746","authenticated-orcid":false,"given":"Gang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Jing","family":"He","sequence":"additional","affiliation":[{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yulun","family":"Du","sequence":"additional","affiliation":[{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Zhichao","family":"Si","sequence":"additional","affiliation":[{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yunhao","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"ref_1","first-page":"20240095","article-title":"Addressing the urban congestion challenge based on traffic bottlenecks","volume":"382","author":"Lieberthal","year":"2024","journal-title":"Philos. Trans. A"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104370","DOI":"10.1016\/j.trc.2023.104370","article-title":"Stability analysis and connected vehicles management for mixed traffic flow with platoons of connected automated vehicles","volume":"157","author":"Qin","year":"2023","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_3","unstructured":"Li, Y., Yu, R., Shahabi, C., and Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s12544-015-0170-8","article-title":"Short-term traffic flow prediction using seasonal ARIMA model with limited input data","volume":"7","author":"Kumar","year":"2015","journal-title":"Eur. Transp. Res. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102025","DOI":"10.1016\/j.simpat.2019.102025","article-title":"Short-term traffic flow prediction based on faded memory Kalman Filter fusing data from connected vehicles and Bluetooth sensors","volume":"102","author":"Emami","year":"2020","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1093\/comjnl\/bxz129","article-title":"Adaptive IoT empowered smart road traffic congestion control system using supervised machine learning algorithm","volume":"64","author":"Ata","year":"2021","journal-title":"Comput. J."},{"key":"ref_7","unstructured":"Wang, Y., Gao, Z., Long, M., Wang, J., and Philip, S.Y. (2018, January 10\u201315). Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_8","unstructured":"Lin, Z., Li, M., Zheng, Z., Cheng, Y., and Yuan, C. (2020, January 7\u201312). Slf-attention convlstm for spatiotemporal prediction. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"114363","DOI":"10.1016\/j.eswa.2020.114363","article-title":"Tree-RNN: Tree structural recurrent neural network for network traffic classification","volume":"167","author":"Ren","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1049\/iet-its.2016.0208","article-title":"LSTM network: A deep learning approach for short-term traffic forecast","volume":"11","author":"Zhao","year":"2017","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Zhou, L., Su, Y., Xia, H., and Xu, B. (2023). Gated Recurrent Unit Embedded with Dual Spatial Convolution for Long-Term Traffic Flow Prediction. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12090366"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100736","DOI":"10.1109\/ACCESS.2021.3097141","article-title":"Intelligent traffic flow prediction using optimized GRU model","volume":"9","author":"Hussain","year":"2021","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"81606","DOI":"10.1109\/ACCESS.2020.2991462","article-title":"City-wide traffic congestion prediction based on CNN, LSTM and transpose CNN","volume":"8","author":"Ranjan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.trc.2020.01.010","article-title":"A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data","volume":"112","author":"Bogaerts","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.neunet.2021.10.021","article-title":"Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction","volume":"145","author":"Ali","year":"2022","journal-title":"Neural Netw."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Roy, A., Roy, K.K., Ahsan Ali, A., Amin, M.A., and Rahman, A.M. (2021, January 11\u201314). SST-GNN: Simplified spatio-temporal traffic forecasting model using graph neural network. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Virtual.","DOI":"10.1007\/978-3-030-75768-7_8"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"117921","DOI":"10.1016\/j.eswa.2022.117921","article-title":"Graph neural network for traffic forecasting: A survey","volume":"207","author":"Jiang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2010510","DOI":"10.1080\/23311916.2021.2010510","article-title":"Traffic flow prediction models\u2013A review of deep learning techniques","volume":"9","author":"Kashyap","year":"2022","journal-title":"Cogent Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6913","DOI":"10.1109\/TNNLS.2022.3183903","article-title":"Bidirectional spatial-temporal adaptive transformer for urban traffic flow forecasting","volume":"34","author":"Chen","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_22","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_23","unstructured":"Vaswani, A. (2017). Attention is all you need. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A review on the attention mechanism of deep learning","volume":"452","author":"Niu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102258","DOI":"10.1016\/j.adhoc.2020.102258","article-title":"A deep learning method based on an attention mechanism for wireless network traffic prediction","volume":"107","author":"Li","year":"2020","journal-title":"Ad Hoc Netw."},{"key":"ref_26","first-page":"5149","article-title":"Meta-learning in neural networks: A survey","volume":"44","author":"Hospedales","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vanschoren, J. (2019). Meta-learning. Automated Machine Learning: Methods, Systems, Challenges, Springer.","DOI":"10.1007\/978-3-030-05318-5_2"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ding, N., Hu, S., Zhao, W., Chen, Y., Liu, Z., Zheng, H.T., and Sun, M. (2021). Openprompt: An open-source framework for prompt-learning. arXiv.","DOI":"10.18653\/v1\/2022.acl-demo.10"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Khattak, M.U., Rasheed, H., Maaz, M., Khan, S., and Khan, F.S. (2023, January 17\u201324). Maple: Multi-modal prompt learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01832"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, J., Loy, C.C., and Liu, Z. (2022, January 18\u201324). Conditional prompt learning for vision-language models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01631"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","article-title":"Learning to prompt for vision-language models","volume":"130","author":"Zhou","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","unstructured":"Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., and Scialom, T. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Song, C., Lin, Y., Guo, S., and Wan, H. (2020, January 9\u201311). Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"ref_36","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume":"33","author":"Bai","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., and Zhang, C. (2020, January 6\u201310). Connecting the dots: Multivariate time series forecasting with graph neural networks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual.","DOI":"10.1145\/3394486.3403118"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cirstea, R.G., Yang, B., Guo, C., Kieu, T., and Pan, S. (2022, January 9\u201312). Towards spatio-temporal aware traffic time series forecasting. Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICDE53745.2022.00262"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., and Zhu, Z. (2017). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv.","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-GCN: A temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_41","unstructured":"Wiles, O., Gowal, S., Stimberg, F., Alvise-Rebuffi, S., Ktena, I., Dvijotham, K., and Cemgil, T. (2021). A fine-grained analysis on distribution shift. arXiv."},{"key":"ref_42","unstructured":"Koh, P.W., Sagawa, S., Marklund, H., Xie, S.M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R.L., and Liang, P. (2021, January 18\u201324). Wilds: A benchmark of in-the-wild distribution shifts. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_43","unstructured":"Chen, M., Goel, K., Sohoni, N.S., Poms, F., Fatahalian, K., and R\u00e9, C. (2021, January 18\u201324). Mandoline: Model evaluation under distribution shift. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_44","first-page":"1660","article-title":"Adaptive conformal inference under distribution shift","volume":"34","author":"Gibbs","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4389","DOI":"10.1007\/s11227-020-03435-3","article-title":"Deep learning and case-based reasoning for predictive and adaptive traffic emergency management","volume":"77","author":"Louati","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"131333","DOI":"10.1016\/j.energy.2024.131333","article-title":"Energy-optimal car-following model for connected automated vehicles considering traffic flow stability","volume":"298","author":"Qin","year":"2024","journal-title":"Energy"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/1\/11\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:57:53Z","timestamp":1760115473000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/1\/11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,31]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["ijgi14010011"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14010011","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,31]]}}}