{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T02:13:06Z","timestamp":1783995186379,"version":"3.55.0"},"reference-count":163,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004721","name":"The University of Tokyo","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004721","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Geoinformatica"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Trajectories serve as a cornerstone of intelligent transportation systems, playing an important role in many applications such as traffic flow prediction, route planning, and urban management. However, the availability of such data is limited due to privacy issues, ethical concerns, and the high cost associated with infrastructure deployment. In recent years, rapidly developing generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models (DMs) have demonstrated strong capabilities in learning complex data distributions and generating synthetic data, thereby alleviating the data accessibility issue. In this survey, we systematically review the existing literature on deep generative models that address the problem of trajectory generation. First, we classify the existing literature into two categories: unconditional and conditional trajectory generation. In unconditional generation, trajectories are generated without contextual constraints, whereas conditional generation incorporates several important factors such as road network topology, time of day, and user preferences to guide the trajectory generation process. Then, for each category, we further classify the literature into three methodological types, including VAEs, GANs, and DMs, and analyze how these models address key challenges under different settings. Finally, we discuss promising directions for future research and hope to inspire further advances in trajectory generation.<\/jats:p>","DOI":"10.1007\/s10707-025-00558-8","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T04:44:33Z","timestamp":1756442673000},"page":"1033-1065","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Trajectory generative models: a survey from unconditional and conditional perspectives"],"prefix":"10.1007","volume":"29","author":[{"given":"Weiping","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renhe","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongyuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangjie","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"558_CR1","doi-asserted-by":"crossref","unstructured":"Shang S, Huang C, Hu X, Chen L (2025) An efficient parallel mechanism for processing trajectory split-and-combine. IEEE Internet Things J","DOI":"10.1109\/JIOT.2025.3564518"},{"issue":"2","key":"558_CR2","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.jum.2019.12.001","volume":"9","author":"Z Engin","year":"2020","unstructured":"Engin Z, Dijk J, Lan T, Longley PA, Treleaven P, Batty M, Penn A (2020) Data-driven urban management: mapping the landscape. J Urban Manag 9(2):140\u2013150","journal-title":"J Urban Manag"},{"issue":"12","key":"558_CR3","doi-asserted-by":"publisher","first-page":"24966","DOI":"10.1109\/TITS.2022.3203457","volume":"23","author":"Y Zhu","year":"2022","unstructured":"Zhu Y, Ye Y, Liu Y, James J (2022) Cross-area travel time uncertainty estimation from trajectory data: a federated learning approach. IEEE Trans Intell Transp Syst 23(12):24966\u201324978","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"10","key":"558_CR4","doi-asserted-by":"publisher","first-page":"11438","DOI":"10.1109\/TITS.2023.3276916","volume":"24","author":"G Zou","year":"2023","unstructured":"Zou G, Lai Z, Ma C, Tu M, Fan J, Li Y (2023) When will we arrive? a novel multi-task spatio-temporal attention network based on individual preference for estimating travel time. IEEE Trans Intell Transp Syst 24(10):11438\u201311452","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"558_CR5","doi-asserted-by":"crossref","unstructured":"Jiang R, Song X, Fan Z, Xia T, Chen Q, Miyazawa S, Shibasaki R (2018) Deepurbanmomentum: an online deep-learning system for short-term urban mobility prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.11338"},{"issue":"1","key":"558_CR6","first-page":"276","volume":"35","author":"R Jiang","year":"2021","unstructured":"Jiang R, Cai Z, Wang Z, Yang C, Fan Z, Chen Q, Tsubouchi K, Song X, Shibasaki R (2021) Deepcrowd: a deep model for large-scale citywide crowd density and flow prediction. IEEE Trans Knowl Data Eng 35(1):276\u2013290","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"558_CR7","doi-asserted-by":"crossref","unstructured":"Jiang R, Wang Z, Yong J, Jeph P, Chen Q, Kobayashi Y, Song X, Fukushima S, Suzumura T (2023) Spatio-temporal meta-graph learning for traffic forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, pp 8078\u20138086","DOI":"10.1609\/aaai.v37i7.25976"},{"key":"558_CR8","doi-asserted-by":"crossref","unstructured":"Liu H, Dong Z, Jiang R, Deng J, Deng J, Chen Q, Song X (2023) Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting. In: Proceedings of the 32nd ACM international conference on information and knowledge management, pp 4125\u20134129","DOI":"10.1145\/3583780.3615160"},{"key":"558_CR9","unstructured":"Gao H, Jiang R, Dong Z, Deng J, Ma Y, Song X (2024) Spatial-temporal-decoupled masked pre-training for spatiotemporal forecasting. In: Proceedings of the thirty-third international joint conference on artificial intelligence, pp 3998\u20134006"},{"key":"558_CR10","doi-asserted-by":"crossref","unstructured":"Dong Z, Jiang R, Gao H, Liu H, Deng J, Wen Q, Song X (2024) Heterogeneity-informed meta-parameter learning for spatiotemporal time series forecasting. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pp 631\u2013641","DOI":"10.1145\/3637528.3671961"},{"issue":"4","key":"558_CR11","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1109\/TC.2023.3236902","volume":"73","author":"H-X Hu","year":"2023","unstructured":"Hu H-X, Lin Z-Z, Hu Q, Zhang Y, Wei W, Wang W (2023) Multi-source information fusion based dlaas for traffic flow prediction. IEEE Trans Comput 73(4):994\u20131003","journal-title":"IEEE Trans Comput"},{"key":"558_CR12","unstructured":"Long Q, Yuan Y, Li Y (2024) A universal model for human mobility prediction. arXiv:2412.15294"},{"issue":"3","key":"558_CR13","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1007\/s11280-022-01045-y","volume":"26","author":"M Xu","year":"2023","unstructured":"Xu M, Li X, Wang F, Shang JS, Chong T, Cheng W, Xu J (2023) Learning to effectively model spatial-temporal heterogeneity for traffic flow forecasting. World Wide Web 26(3):849\u2013865","journal-title":"World Wide Web"},{"issue":"1","key":"558_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3191746","volume":"2","author":"R Jiang","year":"2018","unstructured":"Jiang R, Song X, Fan Z, Xia T, Chen Q, Chen Q, Shibasaki R (2018) Deep roi-based modeling for urban human mobility prediction. Proc ACM Interact Mob Wearable Ubiquitous Technol 2(1):1\u201329","journal-title":"Proc ACM Interact Mob Wearable Ubiquitous Technol"},{"key":"558_CR15","doi-asserted-by":"crossref","unstructured":"Rao X, Shang S, Jiang R, Chen L, Han P (2025) Traffic forecasting with patch-based graph convolutional recurrent network. GeoInformatica 1\u201330","DOI":"10.1007\/s10707-025-00544-0"},{"key":"558_CR16","doi-asserted-by":"crossref","unstructured":"Fan Z, Yang X, Yuan W, Jiang R, Chen Q, Song X, Shibasaki R (2022) Online trajectory prediction for metropolitan scale mobility digital twin. In: Proceedings of the 30th international conference on advances in geographic information systems, pp 1\u201312","DOI":"10.1145\/3557915.3561040"},{"key":"558_CR17","first-page":"54748","volume":"37","author":"X Xu","year":"2024","unstructured":"Xu X, Jiang R, Yang C, Fan Z, Sezaki K (2024) Taming the long tail in human mobility prediction. Adv Neural Inf Process Syst 37:54748\u201354771","journal-title":"Adv Neural Inf Process Syst"},{"key":"558_CR18","doi-asserted-by":"crossref","unstructured":"Yang X, Ge H, Wang J, Fan Z, Jiang R, Shibasaki R, Koshizuka N (2024) Causalmob: Causal human mobility prediction with llms-derived human intentions toward public events. arXiv:2412.02155","DOI":"10.1145\/3690624.3709231"},{"key":"558_CR19","doi-asserted-by":"crossref","unstructured":"Feng X, Liu S, Chen H, Zheng K (2023) Continual trajectory prediction with uncertainty-aware generative memory replay. In: 2023 IEEE International Conference on Data Mining (ICDM), pp 1013\u20131018","DOI":"10.1109\/ICDM58522.2023.00116"},{"key":"558_CR20","doi-asserted-by":"crossref","unstructured":"Han P, Li Z, Liu Y, Zhao P, Li J, Wang H, Shang S (2020) Contextualized point-of-interest recommendation. Int Jt Conf Artif Intell","DOI":"10.24963\/ijcai.2020\/344"},{"key":"558_CR21","doi-asserted-by":"crossref","unstructured":"Qin Y, Wang Y, Sun F, Ju W, Hou X, Wang Z, Cheng J, Lei J, Zhang M (2023) Disenpoi: Disentangling sequential and geographical influence for point-of-interest recommendation. In: Proceedings of the sixteenth acm international conference on web search and data mining, pp 508\u2013516","DOI":"10.1145\/3539597.3570408"},{"key":"558_CR22","doi-asserted-by":"crossref","unstructured":"Rao X, Jiang R, Shang S, Chen L, Han P, Yao B, Kalnis P (2024) Next point-of-interest recommendation with adaptive graph contrastive learning. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2024.3509480"},{"issue":"3","key":"558_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3708999","volume":"19","author":"Y Li","year":"2025","unstructured":"Li Y, Fan Z, Song X (2025) Heterogeneous hyperbolic hypergraph neural network for friend recommendation in location-based social networks. ACM Trans Knowl Discov Data 19(3):1\u201329","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"4","key":"558_CR24","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/s11280-024-01279-y","volume":"27","author":"J Zhang","year":"2024","unstructured":"Zhang J, Li Y, Zou R, Zhang J, Jiang R, Fan Z, Song X (2024) Hyper-relational knowledge graph neural network for next poi recommendation. World Wide Web 27(4):46","journal-title":"World Wide Web"},{"key":"558_CR25","doi-asserted-by":"crossref","unstructured":"Xu X, Suzumura T, Yong J, Hanai M, Yang C, Kanezashi H, Jiang R, Fukushima S (2023) Revisiting mobility modeling with graph: a graph transformer model for next point-of-interest recommendation. In: Proceedings of the 31st ACM international conference on advances in geographic information systems, pp 1\u201310","DOI":"10.1145\/3589132.3625644"},{"key":"558_CR26","doi-asserted-by":"crossref","unstructured":"Jiang R, Wang Z, Cai Z, Yang C, Fan Z, Xia T, Matsubara G, Mizuseki H, Song X, Shibasaki R (2021) Countrywide origin-destination matrix prediction and its application for covid-19. In: Machine learning and knowledge discovery in databases. Applied data science track: european conference, ECML PKDD 2021, Bilbao, Spain, September 13\u201317, 2021, Proceedings, Part IV 21, pp 319\u2013334","DOI":"10.1007\/978-3-030-86514-6_20"},{"key":"558_CR27","doi-asserted-by":"crossref","unstructured":"Albanna BH, Moawad IF, Moussa SM, Sakr MA (2015) Semantic trajectories: a survey from modeling to application. Information Fusion and Geographic Information Systems (IF &GIS\u20192015) Deep Virtualization for Mobile GIS, 59\u201376","DOI":"10.1007\/978-3-319-16667-4_4"},{"issue":"8","key":"558_CR28","doi-asserted-by":"publisher","first-page":"3586","DOI":"10.1109\/TVCG.2022.3165385","volume":"29","author":"C Yang","year":"2022","unstructured":"Yang C, Zhang Z, Fan Z, Jiang R, Chen Q, Song X, Shibasaki R (2022) Epimob: Interactive visual analytics of citywide human mobility restrictions for epidemic control. IEEE Trans Visual Comput Graphics 29(8):3586\u20133601","journal-title":"IEEE Trans Visual Comput Graphics"},{"key":"558_CR29","doi-asserted-by":"crossref","unstructured":"Hu Y, Du Y, Zhang Z, Fang Z, Chen L, Zheng K, Gao Y (2024) Real-time trajectory synthesis with local differential privacy. In: 2024 IEEE 40th International Conference on Data Engineering (ICDE), pp 1685\u20131698","DOI":"10.1109\/ICDE60146.2024.00137"},{"issue":"3","key":"558_CR30","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/s10707-023-00507-3","volume":"28","author":"Y Lun","year":"2024","unstructured":"Lun Y, Miao H, Shen J, Wang R, Wang X, Wang S (2024) Resisting tul attack: balancing data privacy and utility on trajectory via collaborative adversarial learning. GeoInformatica 28(3):381\u2013401","journal-title":"GeoInformatica"},{"issue":"6","key":"558_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3649141","volume":"18","author":"C Rong","year":"2024","unstructured":"Rong C, Liu Z, Ding J, Li Y (2024) Learning to generate temporal origin-destination flow based-on urban regional features and traffic information. ACM Trans Knowl Discov Data 18(6):1\u201317","journal-title":"ACM Trans Knowl Discov Data"},{"key":"558_CR32","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/s10707-019-00390-x","volume":"25","author":"S Gong","year":"2021","unstructured":"Gong S, Cartlidge J, Bai R, Yue Y, Li Q, Qiu G (2021) Geographical and temporal huff model calibration using taxi trajectory data. GeoInformatica 25:485\u2013512","journal-title":"GeoInformatica"},{"key":"558_CR33","doi-asserted-by":"crossref","unstructured":"Magnussen BB, Bl\u00e4ser N, Lu H (2023) Daistin: A data-driven ais trajectory interpolation method. In: Proceedings of the 18th international symposium on spatial and temporal data, pp 75\u201384","DOI":"10.1145\/3609956.3609961"},{"issue":"12","key":"558_CR34","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1038\/s42256-024-00938-z","volume":"6","author":"Z Li","year":"2024","unstructured":"Li Z, Han W, Zhang Y, Fu Q, Li J, Qin L, Dong R, Sun H, Deng Y, Yang L (2024) Learning spatiotemporal dynamics with a pretrained generative model. Nat Mach Intell 6(12):1566\u20131579","journal-title":"Nat Mach Intell"},{"key":"558_CR35","unstructured":"Hossam M, Le T, Papasimeon M, Huynh V, Phung D (2021) Text generation with deep variational gan. arXiv:2104.13488"},{"key":"558_CR36","doi-asserted-by":"crossref","unstructured":"Kim J, Oh C, Do H, Kim S, Sohn K (2024) Diffusion-driven gan inversion for multi-modal face image generation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10403\u201310412","DOI":"10.1109\/CVPR52733.2024.00990"},{"key":"558_CR37","doi-asserted-by":"publisher","first-page":"2692","DOI":"10.1109\/TASLP.2024.3399026","volume":"32","author":"S-L Wu","year":"2024","unstructured":"Wu S-L, Donahue C, Watanabe S, Bryan NJ (2024) Music controlnet: multiple time-varying controls for music generation. IEEE\/ACM Trans Audio Speech Lang Process 32:2692\u20132703","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"558_CR38","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.neucom.2020.03.120","volume":"428","author":"X Chen","year":"2021","unstructured":"Chen X, Xu J, Zhou R, Chen W, Fang J, Liu C (2021) Trajvae: A variational autoencoder model for trajectory generation. Neurocomputing 428:332\u2013339","journal-title":"Neurocomputing"},{"key":"558_CR39","doi-asserted-by":"crossref","unstructured":"Hui S, Wang H, Wang Z, Yang X, Liu Z, Jin D, Li Y (2022) Knowledge enhanced gan for iot traffic generation. In: Proceedings of the ACM Web Conference 2022, pp 3336\u20133346","DOI":"10.1145\/3485447.3511976"},{"key":"558_CR40","first-page":"65168","volume":"36","author":"Y Zhu","year":"2023","unstructured":"Zhu Y, Ye Y, Zhang S, Zhao X, Yu J (2023) Difftraj: Generating gps trajectory with diffusion probabilistic model. Adv Neural Inf Process Syst 36:65168\u201365188","journal-title":"Adv Neural Inf Process Syst"},{"key":"558_CR41","doi-asserted-by":"crossref","unstructured":"Long Q, Wang H, Li T, Huang L, Wang K, Wu Q, Li G, Liang Y, Yu L, Li Y (2023) Practical synthetic human trajectories generation based on variational point processes. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, pp 4561\u20134571","DOI":"10.1145\/3580305.3599888"},{"key":"558_CR42","doi-asserted-by":"crossref","unstructured":"Wei T, Lin Y, Guo S, Lin Y, Huang Y, Xiang C, Bai Y, Wan H (2024) Diff-rntraj: A structure-aware diffusion model for road network-constrained trajectory generation. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2024.3460051"},{"key":"558_CR43","doi-asserted-by":"crossref","unstructured":"Wang Y, Zheng T, Liang Y, Liu S, Song M (2024) Cola: Cross-city mobility transformer for human trajectory simulation. In: Proceedings of the ACM web conference 2024, pp 3509\u20133520","DOI":"10.1145\/3589334.3645469"},{"key":"558_CR44","first-page":"124547","volume":"37","author":"J Wang","year":"2024","unstructured":"Wang J, Jiang R, Yang C, Wu Z, Shibasaki R, Koshizuka N, Xiao C (2024) Large language models as urban residents: an llm agent framework for personal mobility generation. Adv Neural Inf Process Syst 37:124547\u2013124574","journal-title":"Adv Neural Inf Process Syst"},{"key":"558_CR45","doi-asserted-by":"crossref","unstructured":"Shin S, Jeon H, Cho C, Yoon S, Kim T (2020) User mobility synthesis based on generative adversarial networks: a survey. In: 2020 22nd International Conference on Advanced Communication Technology (ICACT), pp 94\u2013103","DOI":"10.23919\/ICACT48636.2020.9061335"},{"issue":"3","key":"558_CR46","doi-asserted-by":"publisher","first-page":"3057","DOI":"10.1007\/s10462-023-10598-x","volume":"56","author":"X Kong","year":"2023","unstructured":"Kong X, Chen Q, Hou M, Wang H, Xia F (2023) Mobility trajectory generation: a survey. Artif Intell Rev 56(3):3057\u20133098","journal-title":"Artif Intell Rev"},{"key":"558_CR47","unstructured":"Yan H, Li Y (2023) A survey of generative ai for intelligent transportation systems. arXiv:2312.08248"},{"key":"558_CR48","unstructured":"Zhang Q, Wang H, Long C, Su L, He X, Chang J, Wu T, Yin H, Yiu S-M, Tian Q, Jensen CS (2024) A survey of generative techniques for spatial-temporal data mining. arXiv:2405.09592"},{"key":"558_CR49","unstructured":"Yang Y, Jin M, Wen H, Zhang C, Liang Y, Ma L, Wang Y, Liu C, Yang B, Xu Z, Bian J, Pan S, Wen Q (2024) A survey on diffusion models for time series and spatio-temporal data. arXiv:2404.18886"},{"key":"558_CR50","doi-asserted-by":"crossref","unstructured":"Chen X, Huang C, Wang C, Chen L (2025) Trajectory generation: a survey on methods and techniques. GeoInformatica 1\u201326","DOI":"10.1007\/s10707-025-00545-z"},{"key":"558_CR51","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s13042-019-00973-y","volume":"11","author":"Y Cao","year":"2020","unstructured":"Cao Y, Xue F, Chi Y, Ding Z, Guo L, Cai Z, Tang H (2020) Effective spatio-temporal semantic trajectory generation for similar pattern group identification. Int J Mach Learn Cybern 11:287\u2013300","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"558_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3416914","volume":"2","author":"R Jiang","year":"2021","unstructured":"Jiang R, Song X, Fan Z, Xia T, Wang Z, Chen Q, Cai Z, Shibasaki R (2021) Transfer urban human mobility via poi embedding over multiple cities. ACM Trans Data Sci 2(1):1\u201326","journal-title":"ACM Trans Data Sci"},{"key":"558_CR53","doi-asserted-by":"crossref","unstructured":"Chen L, Shang S, Jensen CS, Yao B, Kalnis P (2020) Parallel semantic trajectory similarity join. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp 997\u20131008","DOI":"10.1109\/ICDE48307.2020.00091"},{"issue":"7","key":"558_CR54","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.3390\/electronics13071251","volume":"13","author":"B Wang","year":"2024","unstructured":"Wang B, Li W, Khattak ZH (2024) Anomaly detection in connected and autonomous vehicle trajectories using lstm autoencoder and gaussian mixture model. Electronics 13(7):1251","journal-title":"Electronics"},{"key":"558_CR55","doi-asserted-by":"crossref","unstructured":"Yang C, Chen L, Wang H, Shang S (2021) Towards efficient selection of activity trajectories based on diversity and coverage. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 689\u2013696","DOI":"10.1609\/aaai.v35i1.16149"},{"issue":"2","key":"558_CR56","doi-asserted-by":"publisher","first-page":"390","DOI":"10.14778\/3705829.3705853","volume":"18","author":"C Yang","year":"2024","unstructured":"Yang C, Jiang R, Xu X, Xiao C, Sezaki K (2024) Simformer: Single-layer vanilla transformer can learn free-space trajectory similarity. Proc VLDB Endow 18(2):390\u2013398","journal-title":"Proc VLDB Endow"},{"key":"558_CR57","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhang D, Feng S, Chen K, Chen L, Han P, Shang S (2024) Kgts: contrastive trajectory similarity learning over prompt knowledge graph embedding. In: Proceedings of the AAAI conference on artificial intelligence, vol 38, pp 8311\u20138319","DOI":"10.1609\/aaai.v38i8.28672"},{"key":"558_CR58","doi-asserted-by":"crossref","unstructured":"Ding Z, Li K, Chen L, Shang S (2025) Parallel online similarity join over trajectory streams. In: Proceedings of the ACM on Web Conference 2025, pp 3426\u20133437","DOI":"10.1145\/3696410.3714945"},{"key":"558_CR59","doi-asserted-by":"crossref","unstructured":"Li L, Xue H, Song Y, Salim F (2024) T-jepa: A joint-embedding predictive architecture for trajectory similarity computation. In: Proceedings of the 32nd ACM international conference on advances in geographic information systems, pp 569\u2013572","DOI":"10.1145\/3678717.3691271"},{"key":"558_CR60","doi-asserted-by":"crossref","unstructured":"Li J, Liu T, Lu H (2024) Clear: Ranked multi-positive contrastive representation learning for robust trajectory similarity computation. In: 2024 25th IEEE International Conference on Mobile Data Management (MDM), pp 21\u201330","DOI":"10.1109\/MDM61037.2024.00024"},{"key":"558_CR61","unstructured":"Chang Y, Cai X, Jensen CS, Qi J (2025) K nearest neighbor-guided trajectory similarity learning. arXiv:2502.00285"},{"key":"558_CR62","doi-asserted-by":"crossref","unstructured":"Chang Y, Tanin E, Cong G, Jensen CS, Qi J (2023) Trajectory similarity measurement: An efficiency perspective. arXiv:2311.00960","DOI":"10.14778\/3665844.3665858"},{"issue":"9","key":"558_CR63","doi-asserted-by":"publisher","first-page":"3333","DOI":"10.1109\/TIT.2005.853308","volume":"51","author":"S-C Tsai","year":"2005","unstructured":"Tsai S-C, Tzeng W-G, Wu H-L (2005) On the jensen-shannon divergence and variational distance. IEEE Trans Inf Theory 51(9):3333\u20133336","journal-title":"IEEE Trans Inf Theory"},{"key":"558_CR64","unstructured":"Thollard F, Dupont P, Higuera CTl (2000) Probabilistic dfa inference using kullback-leibler divergence and minimality. In: Proceedings of the seventeenth international conference on machine learning, pp 975\u2013982"},{"key":"558_CR65","first-page":"1","volume":"1050","author":"DP Kingma","year":"2014","unstructured":"Kingma DP, Welling M (2014) Auto-encoding variational bayes. Statistic 1050:1","journal-title":"Statistic"},{"key":"558_CR66","doi-asserted-by":"crossref","unstructured":"Krajewski R, Moers T, Meister A, Eckstein L (2019) B\u00e9ziervae: Improved trajectory modeling using variational autoencoders for the safety validation of highly automated vehicles. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp 3788\u20133795","DOI":"10.1109\/ITSC.2019.8917297"},{"key":"558_CR67","unstructured":"S\u00f8nderby CK, Raiko T, Maal\u00f8e L, S\u00f8nderby SK, Winther O (2016) Ladder variational autoencoders. Adv Neural Inf Process Syst 29"},{"key":"558_CR68","unstructured":"Van Den\u00a0Oord A, Vinyals O, Kavukcuoglu K (2017) Neural discrete representation learning. Adv Neural Inf Process Syst 30"},{"key":"558_CR69","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27"},{"key":"558_CR70","unstructured":"Radford A (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434"},{"key":"558_CR71","unstructured":"Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of gans for improved quality, stability, and variation. In: International conference on learning representations"},{"issue":"12","key":"558_CR72","doi-asserted-by":"publisher","first-page":"4217","DOI":"10.1109\/TPAMI.2020.2970919","volume":"43","author":"T Karras","year":"2021","unstructured":"Karras T, Laine S, Aila T (2021) A style-based generator architecture for generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 43(12):4217\u20134228","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"558_CR73","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8110\u20138119","DOI":"10.1109\/CVPR42600.2020.00813"},{"issue":"8","key":"558_CR74","first-page":"1","volume":"18","author":"T Li","year":"2024","unstructured":"Li T, Hui S, Zhang S, Wang H, Zhang Y, Hui P, Jin D, Li Y (2024) Mobile user traffic generation via multi-scale hierarchical gan. ACM Trans Knowl Discov Data 18(8):1\u201319","journal-title":"ACM Trans Knowl Discov Data"},{"key":"558_CR75","unstructured":"Sohl-Dickstein J, Weiss E, Maheswaranathan N, Ganguli S (2015) Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning, pp 2256\u20132265"},{"key":"558_CR76","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Adv Neural Inf Process Syst 33:6840\u20136851","journal-title":"Adv Neural Inf Process Syst"},{"key":"558_CR77","unstructured":"Song Y, Ermon S (2019) Generative modeling by estimating gradients of the data distribution. Adv Neural Inf Process Syst 32"},{"key":"558_CR78","doi-asserted-by":"crossref","unstructured":"Cao H, Tan C, Gao Z, Xu Y, Chen G, Heng P-A, Li SZ (2024) A survey on generative diffusion models. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2024.3361474"},{"key":"558_CR79","doi-asserted-by":"crossref","unstructured":"Choi J, Kim S, Jeong Y, Gwon Y, Yoon S (2021) Ilvr: Conditioning method for denoising diffusion probabilistic models. arXiv:2108.02938","DOI":"10.1109\/ICCV48922.2021.01410"},{"key":"558_CR80","first-page":"8633","volume":"35","author":"J Ho","year":"2022","unstructured":"Ho J, Salimans T, Gritsenko A, Chan W, Norouzi M, Fleet DJ (2022) Video diffusion models. Adv Neural Inf Process Syst 35:8633\u20138646","journal-title":"Adv Neural Inf Process Syst"},{"key":"558_CR81","first-page":"24804","volume":"34","author":"Y Tashiro","year":"2021","unstructured":"Tashiro Y, Song J, Song Y, Ermon S (2021) Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Adv Neural Inf Process Syst 34:24804\u201324816","journal-title":"Adv Neural Inf Process Syst"},{"key":"558_CR82","doi-asserted-by":"crossref","unstructured":"Zheng Y, Zhang L, Xie X, Ma W-Y (2009) Mining interesting locations and travel sequences from gps trajectories. In: Proceedings of the 18th international conference on world wide web, pp 791\u2013800","DOI":"10.1145\/1526709.1526816"},{"key":"558_CR83","unstructured":"O\u2019Connell M., Moreira-Matias L, Kan, W (2015) ECML\/PKDD 15: Taxi Trajectory Prediction (I). https:\/\/kaggle.com\/competitions\/pkdd-15-predict-taxi-service-trajectory-i. Kaggle"},{"key":"558_CR84","doi-asserted-by":"crossref","unstructured":"Yuan J, Zheng Y, Zhang C, Xie W, Xie X, Sun G, Huang Y (2010) T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp 99\u2013108","DOI":"10.1145\/1869790.1869807"},{"key":"558_CR85","doi-asserted-by":"crossref","unstructured":"Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1082\u20131090","DOI":"10.1145\/2020408.2020579"},{"issue":"1","key":"558_CR86","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TSMC.2014.2327053","volume":"45","author":"D Yang","year":"2014","unstructured":"Yang D, Zhang D, Zheng VW, Yu Z (2014) Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Trans Syst Man Cybern Syst 45(1):129\u2013142","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"558_CR87","doi-asserted-by":"crossref","unstructured":"Wang C, Chen L, Shang S, Jensen CS, Kalnis P (2024) Multi-scale detection of anomalous spatio-temporal trajectories in evolving trajectory datasets. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pp 2980\u20132990","DOI":"10.1145\/3637528.3671874"},{"key":"558_CR88","doi-asserted-by":"publisher","first-page":"102144","DOI":"10.1016\/j.compenvurbsys.2024.102144","volume":"112","author":"F Huang","year":"2024","unstructured":"Huang F, Lv J, Yue Y (2024) Jointly spatial-temporal representation learning for individual trajectories. Comput Environ Urban Syst 112:102144","journal-title":"Comput Environ Urban Syst"},{"key":"558_CR89","doi-asserted-by":"crossref","unstructured":"Wang Z, Miao H, Wang S, Wang R, Wang J, Zhang J (2025) C2f-tp: A coarse-to-fine denoising framework for uncertainty-aware trajectory prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 39, pp 12810\u201312817","DOI":"10.1609\/aaai.v39i12.33397"},{"key":"558_CR90","doi-asserted-by":"crossref","unstructured":"Han P, Wang J, Yao D, Shang S, Zhang X (2021) A graph-based approach for trajectory similarity computation in spatial networks. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 556\u2013564","DOI":"10.1145\/3447548.3467337"},{"key":"558_CR91","doi-asserted-by":"crossref","unstructured":"Zhou S, Li J, Wang H, Shang S, Han P (2023) Grlstm: trajectory similarity computation with graph-based residual lstm. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, pp 4972\u20134980","DOI":"10.1609\/aaai.v37i4.25624"},{"key":"558_CR92","unstructured":"Zhou S, Shang S, Chen L, Han P, Jensen CS (2024) Grid and road expressions are complementary for trajectory representation learning. arXiv:2411.14768"},{"key":"558_CR93","doi-asserted-by":"crossref","unstructured":"Kong X, Lin H, Jiang R, Shen G (2024) Anomalous sub-trajectory detection with graph contrastive self-supervised learning. IEEE Trans Veh Technol","DOI":"10.1109\/TVT.2024.3382685"},{"key":"558_CR94","doi-asserted-by":"crossref","unstructured":"Dong Z, Chen Q, Jiang R, Wang H, Song X, Tian H (2022) Learning latent road correlations from trajectories. In: 2022 IEEE International Conference on Big Data (Big Data), pp 5458\u20135467","DOI":"10.1109\/BigData55660.2022.10020759"},{"key":"558_CR95","unstructured":"Bao F, Li C, Sun J, Zhu J (2022) Why are conditional generative models better than unconditional ones? arXiv:2212.00362"},{"key":"558_CR96","doi-asserted-by":"crossref","unstructured":"Zhou S, Shang S, Chen L, Jensen CS, Kalnis P (2024) Red: Effective trajectory representation learning with comprehensive information. arXiv:2411.15096","DOI":"10.14778\/3705829.3705830"},{"key":"558_CR97","doi-asserted-by":"crossref","unstructured":"Huang D, Song X, Fan Z, Jiang R, Shibasaki R, Zhang Y, Wang H, Kato Y (2019) A variational autoencoder based generative model of urban human mobility. In: 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp 425\u2013430","DOI":"10.1109\/MIPR.2019.00086"},{"key":"558_CR98","doi-asserted-by":"crossref","unstructured":"Hochreiter S, Schmidhuber J (1996) Lstm can solve hard long time lag problems. Adv Neural Inf Process Syst 9","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"558_CR99","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"558_CR100","doi-asserted-by":"crossref","unstructured":"Ding W, Wang W, Zhao D (2019) A multi-vehicle trajectories generator to simulate vehicle-to-vehicle encountering scenarios. In: 2019 International Conference on Robotics and Automation (ICRA), pp 4255\u20134261","DOI":"10.1109\/ICRA.2019.8793776"},{"key":"558_CR101","doi-asserted-by":"crossref","unstructured":"Heide A, Tamp\u00e8re CM, Nicolai M (2023) Deep learning highway traffic scenario construction with trajectory generators. In: 2023 IEEE Intelligent Vehicles Symposium (IV), pp 1\u20138","DOI":"10.1109\/IV55152.2023.10186614"},{"key":"558_CR102","doi-asserted-by":"crossref","unstructured":"Ouyang K, Shokri R, Rosenblum DS, Yang W (2018) A non-parametric generative model for human trajectories. In: IJCAI, vol 18, pp 3812\u20133817","DOI":"10.24963\/ijcai.2018\/530"},{"key":"558_CR103","unstructured":"Rao J, Gao S, Kang Y, Huang Q (2020) Lstm-trajgan: A deep learning approach to trajectory privacy protection. arXiv:2006.10521"},{"key":"558_CR104","doi-asserted-by":"crossref","unstructured":"Cao C, Li M (2021) Generating mobility trajectories with retained data utility. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 2610\u20132620","DOI":"10.1145\/3447548.3467158"},{"key":"558_CR105","doi-asserted-by":"crossref","unstructured":"Newson P, Krumm J (2009) Hidden markov map matching through noise and sparseness. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 336\u2013343","DOI":"10.1145\/1653771.1653818"},{"key":"558_CR106","unstructured":"Li P, Zhang H, Li W, Huang D, Chen J, Zhang J, Song X, Zhao P, Ryosuke S (2023) Learning to generate pseudo personal mobility. arXiv:2312.11289"},{"key":"558_CR107","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.future.2022.12.027","volume":"142","author":"J Zhang","year":"2023","unstructured":"Zhang J, Huang Q, Huang Y, Ding Q, Tsai P-W (2023) Dp-trajgan: A privacy-aware trajectory generation model with differential privacy. Futur Gener Comput Syst 142:25\u201340","journal-title":"Futur Gener Comput Syst"},{"key":"558_CR108","doi-asserted-by":"crossref","unstructured":"Wang Y, Zheng T, Liu S, Feng Z, Chen K, Hao Y, Song M (2024) Spatiotemporal-augmented graph neural networks for human mobility simulation. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2024.3409071"},{"key":"558_CR109","doi-asserted-by":"crossref","unstructured":"Jarry G, Couellan N, Delahaye D (2021) On the use of generative adversarial networks for aircraft trajectory generation and atypical approach detection. In: Air traffic management and systems IV: selected papers of the 6th ENRI International Workshop on ATM\/CNS (EIWAC2019) 6, pp 227\u2013243","DOI":"10.1007\/978-981-33-4669-7_13"},{"key":"558_CR110","doi-asserted-by":"publisher","first-page":"103091","DOI":"10.1016\/j.trc.2021.103091","volume":"128","author":"S Choi","year":"2021","unstructured":"Choi S, Kim J, Yeo H (2021) Trajgail: Generating urban vehicle trajectories using generative adversarial imitation learning. Transp Res Part C Emerg Technol 128:103091","journal-title":"Transp Res Part C Emerg Technol"},{"key":"558_CR111","doi-asserted-by":"crossref","unstructured":"Yuan Y, Ding J, Wang H, Jin D, Li Y (2022) Activity trajectory generation via modeling spatiotemporal dynamics. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 4752\u20134762","DOI":"10.1145\/3534678.3542671"},{"key":"558_CR112","unstructured":"Chen RT, Rubanova Y, Bettencourt J, Duvenaud DK (2018) Neural ordinary differential equations. Adv Neural Inf Process Syst 31"},{"key":"558_CR113","first-page":"2503","volume":"33","author":"E Mathieu","year":"2020","unstructured":"Mathieu E, Nickel M (2020) Riemannian continuous normalizing flows. Adv Neural Inf Process Syst 33:2503\u20132515","journal-title":"Adv Neural Inf Process Syst"},{"key":"558_CR114","doi-asserted-by":"crossref","unstructured":"Wang H, Gao C, Wu Y, Jin D, Yao L, Li Y (2023) Pategail: A privacy-preserving mobility trajectory generator with imitation learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, pp 14539\u201314547","DOI":"10.1609\/aaai.v37i12.26700"},{"key":"558_CR115","doi-asserted-by":"crossref","unstructured":"Yuan Y, Wang H, Ding J, Jin D, Li Y (2023) Learning to simulate daily activities via modeling dynamic human needs. In: Proceedings of the ACM web conference 2023, pp 906\u2013916","DOI":"10.1145\/3543507.3583276"},{"key":"558_CR116","unstructured":"Mei H, Eisner JM (2017) The neural hawkes process: a neurally self-modulating multivariate point process. Adv Neural Inf Process Syst 30"},{"key":"558_CR117","doi-asserted-by":"crossref","unstructured":"Gao R, Kang J, Lai B, Xu M, Sun G, Zhang T, Zhang W, Yang D (2024) High-quality trajectory generation for autonomous driving: a lightweight federated learning-based diffusion model. In: GLOBECOM 2024-2024 IEEE Global Communications Conference, pp 1641\u20131646","DOI":"10.1109\/GLOBECOM52923.2024.10901719"},{"issue":"5","key":"558_CR118","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1080\/13658816.2024.2312199","volume":"38","author":"C Chu","year":"2024","unstructured":"Chu C, Zhang H, Wang P, Lu F (2024) Simulating human mobility with a trajectory generation framework based on diffusion model. Int J Geogr Inf Sci 38(5):847\u2013878","journal-title":"Int J Geogr Inf Sci"},{"key":"558_CR119","doi-asserted-by":"crossref","unstructured":"Zhao X, Zhang X, Zhang B, Qi J, Dong J, Yu Y (2025) MA$$^{2}$$Traj: Diffusion network with multi-attribute aggregation for trajectory generation. GeoInformatica, 1\u201322","DOI":"10.1007\/s10707-025-00549-9"},{"key":"558_CR120","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"558_CR121","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"issue":"2","key":"558_CR122","first-page":"85","volume":"2","author":"W Wang","year":"2017","unstructured":"Wang W, Liu C, Zhao D (2017) How much data are enough? a statistical approach with case study on longitudinal driving behavior. IEEE Trans Intell Veh 2(2):85\u201398","journal-title":"IEEE Trans Intell Veh"},{"key":"558_CR123","doi-asserted-by":"crossref","unstructured":"Krajewski R, Bock J, Kloeker L, Eckstein L (2018) The highd dataset: a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp 2118\u20132125","DOI":"10.1109\/ITSC.2018.8569552"},{"key":"558_CR124","unstructured":"Kiukkonen N, Blom J, Dousse O, Gatica-Perez D, Laurila J (2010) Towards rich mobile phone datasets: Lausanne data collection campaign. Proc ICPS, Berlin 68(7)"},{"key":"558_CR125","doi-asserted-by":"crossref","unstructured":"Barcel\u00f3 J, Casas J (2005) Dynamic network simulation with aimsun. In: Simulation approaches in transportation analysis: recent advances and challenges, pp 57\u201398","DOI":"10.1007\/0-387-24109-4_3"},{"key":"558_CR126","unstructured":"Carvalho J, Baierl M, Urain J, Peters J (2022) Conditioned score-based models for learning collision-free trajectory generation. In: NeurIPS 2022 workshop on score-based methods"},{"key":"558_CR127","doi-asserted-by":"crossref","unstructured":"Zhou Z, Ding J, Liu Y, Jin D, Li Y (2023) Towards generative modeling of urban flow through knowledge-enhanced denoising diffusion. In: Proceedings of the 31st ACM international conference on advances in geographic information systems, pp 1\u201312","DOI":"10.1145\/3589132.3625641"},{"key":"558_CR128","doi-asserted-by":"crossref","unstructured":"Jiao R, Liu X, Zheng B, Liang D, Zhu Q (2022) Tae: A semi-supervised controllable behavior-aware trajectory generator and predictor. In: 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 12534\u201312541","DOI":"10.1109\/IROS47612.2022.9981029"},{"key":"558_CR129","unstructured":"Choi C, Patil A, Malla S (2019) Drogon: A causal reasoning framework for future trajectory forecast. arXiv:1908.00024"},{"key":"558_CR130","doi-asserted-by":"crossref","unstructured":"Lin H, Krumm J, Shahabi C, Xiong L (2024) Controllable visit trajectory generation with spatiotemporal constraints. In: 2024 IEEE International Conference on Data Mining (ICDM), pp 773\u2013778","DOI":"10.1109\/ICDM59182.2024.00091"},{"key":"558_CR131","doi-asserted-by":"crossref","unstructured":"Zhang L, Mbuya J, Zhao L, Pfoser D, Anastasopoulos A (2025) End-to-end trajectory generation-contrasting deep generative models and language models. ACM Trans Spat Algorithms Syst","DOI":"10.1145\/3716892"},{"key":"558_CR132","doi-asserted-by":"crossref","unstructured":"Deng B, Jing X, Yang T, Qu B, Cudre-Mauroux P, Yang D (2024) Revisiting synthetic human trajectories: imitative generation and benchmarks beyond datasaurus. arXiv:2409.13790","DOI":"10.1145\/3690624.3709180"},{"key":"558_CR133","unstructured":"Sohn K, Lee H, Yan X (2015) Learning structured output representation using deep conditional generative models. Adv Neural Inf Process Syst 28"},{"key":"558_CR134","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhang W, Zhang D, Li Y, Zhang D (2024) Research on key scene trajectory generation method based on bla-vae. In: 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp 1923\u20131930","DOI":"10.1109\/CSCWD61410.2024.10580658"},{"issue":"6","key":"558_CR135","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1109\/TNNLS.2020.3005325","volume":"32","author":"F Zhou","year":"2020","unstructured":"Zhou F, Liu X, Zhang K, Trajcevski G (2020) Toward discriminating and synthesizing motion traces using deep probabilistic generative models. IEEE Trans Neural Netw Learn Syst 32(6):2401\u20132414","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"558_CR136","doi-asserted-by":"crossref","unstructured":"Zhang L, Zhao L, Pfoser D (2022) Factorized deep generative models for end-to-end trajectory generation with spatiotemporal validity constraints. In: Proceedings of the 30th international conference on advances in geographic information systems, pp 1\u201312","DOI":"10.1145\/3557915.3560994"},{"key":"558_CR137","doi-asserted-by":"crossref","unstructured":"Murad A, Ruocco M (2025) Synthetic aircraft trajectory generation using time-based vq-vae. In: 2025 Integrated Communications, Navigation and Surveillance Conference (ICNS), pp 1\u201310","DOI":"10.1109\/ICNS65417.2025.10976929"},{"key":"558_CR138","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv:1411.1784"},{"key":"558_CR139","doi-asserted-by":"crossref","unstructured":"Feng J, Yang Z, Xu F, Yu H, Wang M, Li Y (2020) Learning to simulate human mobility. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 3426\u20133433","DOI":"10.1145\/3394486.3412862"},{"issue":"1","key":"558_CR140","doi-asserted-by":"publisher","first-page":"126","DOI":"10.6339\/21-JDS1004","volume":"19","author":"X Wang","year":"2021","unstructured":"Wang X, Liu X, Lu Z, Yang H (2021) Large scale gps trajectory generation using map based on two stage gan. J Data Sci 19(1):126\u2013141","journal-title":"J Data Sci"},{"key":"558_CR141","doi-asserted-by":"crossref","unstructured":"Jiang W, Zhao WX, Wang J, Jiang J (2023) Continuous trajectory generation based on two-stage gan. In: Proceedings of the AAAI conference on artificial intelligence, vol 37, pp 4374\u20134382","DOI":"10.1609\/aaai.v37i4.25557"},{"key":"558_CR142","unstructured":"Xu N, Trinh L, Rambhatla S, Zeng Z, Chen J, Assefa S, Liu Y (2021) Simulating continuous-time human mobility trajectories. In: Proc. 9th Int. Conf. Learn. Represent, pp 1\u20139"},{"issue":"1","key":"558_CR143","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1080\/15472450.2023.2301691","volume":"29","author":"H Shi","year":"2025","unstructured":"Shi H, Dong S, Wu Y, Nie Q, Zhou Y, Ran B (2025) Generative adversarial network for car following trajectory generation and anomaly detection. J Intell Transp Syst 29(1):53\u201366","journal-title":"J Intell Transp Syst"},{"key":"558_CR144","doi-asserted-by":"publisher","first-page":"111690","DOI":"10.1016\/j.asoc.2024.111690","volume":"160","author":"X Kong","year":"2024","unstructured":"Kong X, Bi J, Chen Q, Shen G, Chin T, Pau G (2024) Traffic trajectory generation via conditional generative adversarial networks for transportation metaverse. Appl Soft Comput 160:111690","journal-title":"Appl Soft Comput"},{"issue":"5","key":"558_CR145","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1080\/13658816.2024.2381146","volume":"39","author":"Z Cao","year":"2025","unstructured":"Cao Z, Liu K, Jin X, Ning L, Yin L, Lu F (2025) Stage: a spatiotemporal-knowledge enhanced multi-task generative adversarial network (gan) for trajectory generation. Int J Geogr Inf Sci 39(5):1100\u20131127","journal-title":"Int J Geogr Inf Sci"},{"issue":"2","key":"558_CR146","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.1109\/TCSS.2023.3235923","volume":"11","author":"G Xiong","year":"2023","unstructured":"Xiong G, Li Z, Zhao M, Zhang Y, Miao Q, Lv Y, Wang F-Y (2023) Trajsgan: A semantic-guiding adversarial network for urban trajectory generation. IEEE Trans Comput Soc Syst 11(2):1733\u20131743","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"558_CR147","doi-asserted-by":"crossref","unstructured":"Zhang X, Li Y, Zhou X, Luo J (2019) Unveiling taxi drivers\u2019 strategies via cgail: Conditional generative adversarial imitation learning. In: 2019 IEEE International Conference on Data Mining (ICDM), pp 1480\u20131485","DOI":"10.1109\/ICDM.2019.00194"},{"key":"558_CR148","doi-asserted-by":"crossref","unstructured":"Wei H, Xu D, Liang J, Li ZJ (2021) How do we move: modeling human movement with system dynamics. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 35, pp 4445\u20134452","DOI":"10.1609\/aaai.v35i5.16571"},{"key":"558_CR149","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal P, Nichol A (2021) Diffusion models beat gans on image synthesis. Adv Neural Inf Process Syst 34:8780\u20138794","journal-title":"Adv Neural Inf Process Syst"},{"key":"558_CR150","doi-asserted-by":"crossref","unstructured":"Zhu Y, Yu JJ, Zhao X, Liu Q, Ye Y, Chen W, Zhang Z, Wei X, Liang Y (2024) Controltraj: Controllable trajectory generation with topology-constrained diffusion model. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 4676\u20134687","DOI":"10.1145\/3637528.3671866"},{"key":"558_CR151","unstructured":"Guo Z, Gao X, Zhou J, Cai X, Shi B (2023) Scenedm: Scene-level multi-agent trajectory generation with consistent diffusion models. arXiv:2311.15736"},{"key":"558_CR152","doi-asserted-by":"crossref","unstructured":"Wang Y, Tang C, Sun L, Rossi S, Xie Y, Peng C, Hannagan T, Sabatini S, Poerio N, Tomizuka M, Zhan W (2024) Optimizing diffusion models for joint trajectory prediction and controllable generation. In: European conference on computer vision, pp 324\u2013341","DOI":"10.1007\/978-3-031-73397-0_19"},{"key":"558_CR153","doi-asserted-by":"crossref","unstructured":"Rao X, Shang S, Jiang R, Han P, Chen L (2025) Seed: Bridging sequence and diffusion models for road trajectory generation. In: Proceedings of the ACM on Web Conference 2025, pp 2007\u20132017","DOI":"10.1145\/3696410.3714951"},{"key":"558_CR154","doi-asserted-by":"crossref","unstructured":"Zhong Z, Rempe D, Xu D, Chen Y, Veer S, Che T, Ray B, Pavone M (2023) Guided conditional diffusion for controllable traffic simulation. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp 3560\u20133566","DOI":"10.1109\/ICRA48891.2023.10161463"},{"key":"558_CR155","doi-asserted-by":"crossref","unstructured":"Zhang Z, Fan Z, Lv Z, Song X, Shibasaki R (2024) Long-term vessel trajectory imputation with physics-guided diffusion probabilistic model. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pp 4398\u20134407","DOI":"10.1145\/3637528.3672086"},{"key":"558_CR156","unstructured":"Xiao Y, Erden MS, Wang C (2025) Controllable latent diffusion for traffic simulation. arXiv:2503.11771"},{"key":"558_CR157","doi-asserted-by":"crossref","unstructured":"Zhou Z, Wang J, Li Y-H, Huang Y-K (2023) Query-centric trajectory prediction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 17863\u201317873","DOI":"10.1109\/CVPR52729.2023.01713"},{"key":"558_CR158","doi-asserted-by":"crossref","unstructured":"Chang M-F, Lambert J, Sangkloy P, Singh J, Bak S, Hartnett A, Wang D, Carr P, Lucey S, Ramanan D, Hays J (2019) Argoverse: 3d tracking and forecasting with rich maps. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8748\u20138757","DOI":"10.1109\/CVPR.2019.00895"},{"key":"558_CR159","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer M, Strohmeier M, Lenders V, Martinovic I, Wilhelm M (2014) Bringing up opensky: a large-scale ads-b sensor network for research. In: IPSN-14 Proceedings of the 13th international symposium on information processing in sensor networks, pp 83\u201394","DOI":"10.1109\/IPSN.2014.6846743"},{"key":"558_CR160","unstructured":"Administration FH (2006) Next generation simulation (ngsim) vehicle trajectories and supporting data. Technical report, U.S. Department of Transportation. https:\/\/ops.fhwa.dot.gov\/trafficanalysistools\/ngsim.htm"},{"key":"558_CR161","doi-asserted-by":"crossref","unstructured":"Sun P, Kretzschmar H, Dotiwalla X, Chouard A, Patnaik V, Tsui P, Guo J, Zhou Y, Chai Y, Caine B, Vasudevan V, Han W, Ngiam J, Zhao H, Timofeev A, Ettinger S, Krivokon M, Gao A, Joshi A, Zhang Y, Shlens J, Chen Z, Anguelov D (2020) Scalability in perception for autonomous driving: Waymo open dataset. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2446\u20132454","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"558_CR162","unstructured":"Wilson B, Qi W, Agarwal T, Lambert J, Singh J, Khandelwal S, Pan B, Kumar R, Hartnett A, Pontes JK, Ramanan D, Carr P, Hays J (2023) Argoverse 2: Next generation datasets for self-driving perception and forecasting. arXiv:2301.00493"},{"key":"558_CR163","doi-asserted-by":"crossref","unstructured":"Caesar H, Bankiti V, Lang AH, Vora S, Liong VE, Xu Q, Krishnan A, Pan Y, Baldan G, Beijbom O (2020) nuscenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11621\u201311631","DOI":"10.1109\/CVPR42600.2020.01164"}],"container-title":["GeoInformatica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-025-00558-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10707-025-00558-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-025-00558-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T05:52:43Z","timestamp":1758952363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10707-025-00558-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,29]]},"references-count":163,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["558"],"URL":"https:\/\/doi.org\/10.1007\/s10707-025-00558-8","relation":{},"ISSN":["1384-6175","1573-7624"],"issn-type":[{"value":"1384-6175","type":"print"},{"value":"1573-7624","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,29]]},"assertion":[{"value":"27 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}