{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T21:13:28Z","timestamp":1779225208878,"version":"3.51.4"},"reference-count":60,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The origin\u2013destination (OD) matrix describes traffic flow information between regions. It is a critical input for intelligent transportation systems (ITS). However, obtaining the OD matrix remains challenging due to high costs and privacy concerns. Synthetic data, which have the same statistical distribution of real data, help address privacy issues and data scarcity. Based on Generative Adversarial Networks (GAN), OD matrix generation models, which can effectively generate a synthetic OD matrix, help to address the challenge of obtaining OD matrix data in ITS research. However, existing OD matrix generation methods can only handle with tens of nodes. To address this challenge, this study proposes the Origin\u2013Destination Progressive Growing Generative Adversarial Networks (OD-PGGAN) for large-scale OD matrix generation task which adapt the PGGAN architecture. OD-PGGAN adopts a progressive learning strategy to gradually learn the structure of the OD matrix from a coarse to fine scale. OD-PGGAN utilizes multi-scale generators and discriminators to perform generation and discrimination tasks at different spatial resolutions. OD-PGGAN introduces a geography-based upsampling and downsampling algorithm to maintain the geographical significance of the OD matrix during spatial resolution transformations. The results demonstrate that the proposed OD-PGGAN can generate a large-scale synthetic OD matrix with 1024 nodes that have the same distribution as the real sample and outperforms two classical methods. The OD-PGGAN can effectively provide reliable synthetic data for transportation applications.<\/jats:p>","DOI":"10.3390\/ijgi14040172","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T09:06:51Z","timestamp":1744621611000},"page":"172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Generating Large-Scale Origin\u2013Destination Matrix via Progressive Growing Generative Adversarial Networks Model"],"prefix":"10.3390","volume":"14","author":[{"given":"Zehao","family":"Yuan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"},{"name":"Geocomputation Center for Social Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanyan","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"},{"name":"Geocomputation Center for Social Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3591-9968","authenticated-orcid":false,"given":"Biyu","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"},{"name":"Geocomputation Center for Social Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yubo","family":"Luo","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"},{"name":"Geocomputation Center for Social Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxin","family":"Teng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"},{"name":"Geocomputation Center for Social Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"},{"name":"Geocomputation Center for Social Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"An, S., Lee, B., and Shin, D. (2011, January 26\u201328). A survey of intelligent transportation systems. Proceedings of the 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks, Bali, Indonesia.","DOI":"10.1109\/CICSyN.2011.76"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/MVT.2009.935537","article-title":"Intelligent transportation systems","volume":"5","author":"Dimitrakopoulos","year":"2010","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_3","unstructured":"Pavlyuk, D. (2018). Spatiotemporal Big Data Challenges for Traffic Flow Analysis. Reliability and Statistics in Transportation and Communication: Selected Papers from the 17th International Conference on Reliability and Statistics in Transportation and Communication, RelStat\u201917, Riga, Latvia, 18\u201321 October 2017, Springer."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"de Montjoye, Y., Hidalgo, C.A., Verleysen, M., and Blondel, V.D. (2013). Unique in the Crowd: The privacy bounds of human mobility. Sci. Rep., 3.","DOI":"10.1038\/srep01376"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.tra.2019.09.026","article-title":"MaaS surveillance: Privacy considerations in mobility as a service","volume":"131","author":"Cottrill","year":"2020","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3610224","article-title":"Generative models for synthetic urban mobility data: A systematic literature review","volume":"56","author":"Kapp","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3672557","article-title":"In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)","volume":"10","author":"Pfoser","year":"2024","journal-title":"ACM Trans. Spat. Algorithms Syst."},{"key":"ref_8","unstructured":"Rong, C., Ding, J., Liu, Y., and Li, Y. (2024). A large-scale benchmark dataset for commuting origin-destination matrix generation. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Endres, M., Mannarapotta Venugopal, A., and Tran, T.S. (2022, January 22\u201324). Synthetic data generation: A comparative study. Proceedings of the 26th International Database Engineered Applications Symposium (IDEAS\u201922), Budapest, Hungary.","DOI":"10.1145\/3548785.3548793"},{"key":"ref_10","unstructured":"Jordon, J., Szpruch, L., Houssiau, F., Bottarelli, M., Cherubin, G., Maple, C., Cohen, S.N., and Weller, A. (2022). Synthetic Data\u2014What, why and how?. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1146\/annurev-statistics-040720-031848","article-title":"Synthetic data","volume":"8","author":"Raghunathan","year":"2021","journal-title":"Annu. Rev. Stat. Its Appl."},{"key":"ref_12","first-page":"4","article-title":"An interdisciplinary survey on origin-destination flows modeling: Theory and techniques","volume":"57","author":"Rong","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_13","unstructured":"Rong, C., Wang, H., and Li, Y. (2023). Origin-Destination Network Generation via Gravity-Guided GAN. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2566","DOI":"10.1016\/j.trpro.2017.05.301","article-title":"A bi-level Random Forest based approach for estimating OD matrices: Preliminary results from the Belgium National Household Travel Survey","volume":"25","author":"Saadi","year":"2017","journal-title":"Transp. Res. Procedia"},{"key":"ref_15","first-page":"281","article-title":"Estimation of dynamic assignment matrices and OD demands using adaptive Kalman filtering","volume":"6","author":"Hu","year":"2001","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_16","first-page":"49","article-title":"Context-aware spatial-temporal neural network for citywide crowd flow prediction via modeling long-range spatial dependency","volume":"16","author":"Feng","year":"2021","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110311","DOI":"10.1016\/j.engappai.2025.110311","article-title":"Incorporating prior knowledge of collision risk into deep learning networks for ship trajectory prediction in the maritime Internet of Things industry","volume":"146","author":"Zhang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102974","DOI":"10.1016\/j.trc.2021.102974","article-title":"Simulation of price, customer behaviour and system impact for a cost-covering automated taxi system in Zurich","volume":"123","author":"Becker","year":"2021","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zeng, J., Zhang, G., Rong, C., Ding, J., Yuan, J., and Li, Y. (2022, January 17\u201321). Causal learning empowered OD prediction for urban planning. Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM \u201822), Atlanta, GA, USA.","DOI":"10.1145\/3511808.3557255"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s11831-019-09388-y","article-title":"Applications of generative adversarial networks (gans): An updated review","volume":"28","author":"Alqahtani","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1109\/JAS.2023.123744","article-title":"How generative adversarial networks promote the development of intelligent transportation systems: A survey","volume":"10","author":"Lin","year":"2023","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Figueira, A., and Vaz, B. (2022). Survey on synthetic data generation, evaluation methods and GANs. Mathematics, 10.","DOI":"10.3390\/math10152733"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1140\/epjds\/s13688-022-00372-4","article-title":"Generating mobility networks with generative adversarial networks","volume":"11","author":"Mauro","year":"2022","journal-title":"EPJ Data Sci."},{"key":"ref_24","first-page":"1","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","first-page":"128","article-title":"Two-person cooperative games","volume":"21","author":"Nash","year":"1953","journal-title":"Econom. J. Econom. Soc."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ratliff, L.J., Burden, S.A., and Sastry, S.S. (2013, January 2\u20134). Characterization and computation of local Nash equilibria in continuous games. Proceedings of the 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA.","DOI":"10.1109\/Allerton.2013.6736623"},{"key":"ref_27","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_28","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_29","unstructured":"Odena, A. (2016). Semi-supervised learning with generative adversarial networks. arXiv."},{"key":"ref_30","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L.E.O. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning, PMLR 70, Sydney, Australia."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, J., Wang, J., Zhao, M., Zhang, W., Zhang, F., Xie, X., and Guo, M. (2018, January 2\u20137). GraphGAN: Graph Representation Learning with Generative Adversarial Nets. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11872"},{"key":"ref_32","unstructured":"Wang, Z., Zheng, H., He, P., Chen, W., and Zhou, M. (2022). Diffusion-gan: Training gans with diffusion. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ahmad, Z., Jaffri, Z.U.A., Chen, M., and Bao, S. (2024). Understanding GANs: Fundamentals, variants, training challenges, applications, and open problems. Multimed. Tools Appl., 1\u201377.","DOI":"10.1007\/s11042-024-19361-y"},{"key":"ref_34","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive Growing of GANs for Improved Quality, Stability, and Variation. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A style-based generator architecture for generative adversarial networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_36","unstructured":"Brock, A., Donahue, J., and Simonyan, K. (2018). Large Scale GAN Training for High Fidelity Natural Image Synthesis. arXiv."},{"key":"ref_37","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 9\u201315). Self-attention generative adversarial networks. Proceedings of the 36th International Conference on Machine Learning, PMLR 97, Long Beach, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, B., Gu, S., Zhang, B., Bao, J., Chen, D., Wen, F., Wang, Y., and Guo, B. (2022, January 18\u201324). StyleSwin: Transformer-based GAN for High-resolution Image Generation. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01102"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kang, M., Zhu, J., Zhang, R., Park, J., Shechtman, E., Paris, S., and Park, T. (2023, January 17\u201324). Scaling up gans for text-to-image synthesis. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00976"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"35728","DOI":"10.1109\/ACCESS.2024.3370848","article-title":"Generative adversarial networks (GANs) in medical imaging: Advancements, applications, and challenges","volume":"12","author":"Showrov","year":"2024","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Oubara, A., Wu, F., Maleki, R., Ma, B., Amamra, A., and Yang, G. (2024). Enhancing adversarial learning-based change detection in imbalanced datasets using artificial image generation and attention mechanism. ISPRS Int. J. Geo-Inf., 13.","DOI":"10.3390\/ijgi13040125"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"\u0160idlauskas, A., Kri\u0161\u010di\u016bnas, A., and \u010calneryt\u0117, D. (2024). Continuous Satellite Image Generation from Standard Layer Maps Using Conditional Generative Adversarial Networks. ISPRS Int. J. Geo-Inf., 13.","DOI":"10.3390\/ijgi13120448"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"11001","DOI":"10.1088\/2632-2153\/ad1f77","article-title":"Ten years of generative adversarial nets (GANs): A survey of the state-of-the-art","volume":"5","author":"Chakraborty","year":"2024","journal-title":"Mach. Learn. Sci. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1080\/13658816.2024.2312199","article-title":"Simulating human mobility with a trajectory generation framework based on diffusion model","volume":"38","author":"Chu","year":"2024","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105073","DOI":"10.1016\/j.scs.2023.105073","article-title":"An Activity Space-based Gravity Model for Intracity Human Mobility Flows","volume":"101","author":"Zhang","year":"2023","journal-title":"Sustain. Cities Soc."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Alis, C., Legara, E.F., and Monterola, C. (2021). Generalized radiation model for human migration. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-02109-1"},{"key":"ref_47","first-page":"307","article-title":"Understanding Intercity Mobility Patterns in Rapidly Urbanizing China, 2015\u20132019: Evidence from Longitudinal Poisson Gravity Modeling","volume":"113","author":"Gu","year":"2023","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kotsubo, M., and Nakaya, T. (2021). Kernel-based formulation of intervening opportunities for spatial interaction modelling. Sci. Rep., 11.","DOI":"10.1038\/s41598-020-80246-9"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7474","DOI":"10.1109\/TITS.2020.3003310","article-title":"Spatial origin-destination flow imputation using graph convolutional networks","volume":"22","author":"Yao","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_50","first-page":"603","article-title":"Inferring origin-destination flows from population distribution","volume":"35","author":"Rong","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4813","DOI":"10.1007\/s00521-021-06669-1","article-title":"Deep learning for short-term origin\u2013destination passenger flow prediction under partial observability in urban railway systems","volume":"34","author":"Jiang","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_52","first-page":"2109774","article-title":"Network-wide ride-sourcing passenger demand origin-destination matrix prediction with a generative adversarial network","volume":"20","author":"Li","year":"2024","journal-title":"Transp. A Transp. Sci."},{"key":"ref_53","unstructured":"Rong, C., Ding, J., Liu, Z., and Li, Y. (2023). Complexity-aware large scale origin-destination network generation via diffusion model. arXiv."},{"key":"ref_54","unstructured":"Youssef, A. (1999). Image Downsampling and Upsampling Methods, National Institute of Standards and Technology."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"677","DOI":"10.2307\/2087063","article-title":"The P1 P2\/D Hypothesis: On the Intercity Movement of Persons","volume":"11","author":"Zipf","year":"1946","journal-title":"Am. Sociol. Rev."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/nature10856","article-title":"A universal model for mobility and migration patterns","volume":"484","author":"Simini","year":"2012","journal-title":"Nature"},{"key":"ref_57","unstructured":"Sorensen, T. (1948). A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similarity of Species Content and Its Application to Analyses of the Vegetation on Danish Commons, I kommission hos Ejnar Munksgaard."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yang, Y., Herrera, C., Eagle, N., and Gonzalez, M.C. (2014). Limits of predictability in commuting flows in the absence of data for calibration. Sci. Rep., 4.","DOI":"10.1038\/srep05662"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Ding, J., Liu, Y., Jin, D., and Li, Y. (2023, January 13\u201316). Towards generative modeling of urban flow through knowledge-enhanced denoising diffusion. Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany.","DOI":"10.1145\/3589132.3625641"},{"key":"ref_60","unstructured":"Bhandari, P., Anastasopoulos, A., and Pfoser, D. (November, January 29). Urban mobility assessment using LLMs. Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, Atlanta, GA, USA."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/4\/172\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:14:19Z","timestamp":1760030059000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/4\/172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,14]]},"references-count":60,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["ijgi14040172"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14040172","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,14]]}}}