{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T11:19:52Z","timestamp":1778498392895,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819203741","type":"print"},{"value":"9789819203758","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-92-0375-8_21","type":"book-chapter","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T11:01:47Z","timestamp":1778497307000},"page":"338-355","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Traversability-Enhanced Long-Range Trajectory Recovery with\u00a0Motion-Variation Modeling"],"prefix":"10.1007","author":[{"given":"Jiafan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiali","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: FODO, pp. 69\u201384 (1993)","DOI":"10.1007\/3-540-57301-1_5"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Bei, X., Zhang, S.: Algorithms for trip-vehicle assignment in ride-sharing. In: AAAI, vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.11298"},{"issue":"3","key":"21_CR3","first-page":"414","volume":"17","author":"Y Chen","year":"2023","unstructured":"Chen, Y., Cong, G., Anda, C.: Teri: an effective framework for trajectory recovery with irregular time intervals. VLDB 17(3), 414\u2013426 (2023)","journal-title":"VLDB"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, H., Sun, W., Zheng, B.: RNTrajRec: road network enhanced trajectory recovery with spatial-temporal transformer. In: ICDE, pp. 829\u2013842 (2023)","DOI":"10.1109\/ICDE55515.2023.00069"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP, pp. 1724\u20131734 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Deng, L., Zhao, Y., Sun, H., Yang, C., Xie, J., Zheng, K.: Fusing local and global mobility patterns for trajectory recovery. In: DASFAA, pp. 448\u2013463 (2023)","DOI":"10.1007\/978-3-031-30637-2_29"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Elshrif, M., Isufaj, K., Mokbel, M.: Network-less trajectory imputation. In: GIS, pp. 1\u201310 (2022)","DOI":"10.1145\/3557915.3560942"},{"issue":"1","key":"21_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BF03018603","volume":"22","author":"M Fr\u00e9chet","year":"1906","unstructured":"Fr\u00e9chet, M.: Sur quelques points du calcul fonctionnel. Rendiconti del Circolo Matematico di Palermo 22(1), 1\u201372 (1906)","journal-title":"Rendiconti del Circolo Matematico di Palermo"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: CVPR, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"21_CR10","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: NeurIPS, pp. 6840\u20136851 (2020)"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Hoteit, S., Secci, S., Sobolevsky, S., Ratti, C., Pujolle, G.: Estimating human trajectories and hotspots through mobile phone data. Comput. Netw. 296\u2013307 (2014)","DOI":"10.1016\/j.comnet.2014.02.011"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Huttenlocher, D., Kedem, K., Kleinberg, J.: On dynamic voronoi diagrams and the minimum hausdorff distance for point sets under Euclidean motion in the plane. In: SCG, pp. 110\u2013119 (1992)","DOI":"10.1145\/142675.142700"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Isufaj, K., Elshrif, M., Abbar, S., Mokbel, M.: GTI: a scalable graph-based trajectory imputation. In: GIS, pp. 1\u201310 (2023)","DOI":"10.1145\/3589132.3625620"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Lan, W., Xu, Y., Zhao, B.: Travel time estimation without road networks: an urban morphological layout representation approach. In: IJCAI, pp. 1772\u20131778 (2019)","DOI":"10.24963\/ijcai.2019\/245"},{"key":"21_CR15","unstructured":"Li, Y., Si, S., Li, G., Hsieh, C.J., Bengio, S.: Learnable Fourier features for multi-dimensional spatial positional encoding. In: NeurIPS, pp. 15816\u201315829 (2021)"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Liu, M., Huang, H., Feng, H., Sun, L., Du, B., Fu, Y.: Pristi: a conditional diffusion framework for spatiotemporal imputation. In: ICDE, pp. 1927\u20131939 (2023)","DOI":"10.1109\/ICDE55515.2023.00150"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Nguyen, T., Nguyen, T., Baraniuk, R.: Mitigating over-smoothing in transformers via regularized nonlocal functionals. In: NeurIPS, pp. 80233\u201380256 (2023)","DOI":"10.52202\/075280-3516"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Qu, L., Li, L., Zhang, Y., Hu, J.: PPCA-based missing data imputation for traffic flow volume: a systematical approach. IEEE Trans. Intell. Transp. Syst. 512\u2013522 (2009)","DOI":"10.1109\/TITS.2009.2026312"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Rao, X., Shang, S., Jiang, R., Han, P., Chen, L.: Seed: bridging sequence and diffusion models for road trajectory generation. In: WWW, pp. 2007\u20132017 (2025)","DOI":"10.1145\/3696410.3714951"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Shen, Y., Wu, W., Mao, J., Tong, Y., Liu, G., Wang, C.: Bridging the gap between sparsity and redundancy: a dual-decoding framework with global context for map inference. arXiv:2509.11731 (2025)","DOI":"10.1145\/3746252.3761537"},{"key":"21_CR21","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NIPS (2017)"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, S., Lin, L., Yang, Y., Wang, S., Wen, H.: Behavior-aware sparse trajectory recovery in last-mile delivery with multi-scale attention fusion. In: CIKM, pp. 4931\u20134938 (2024)","DOI":"10.1145\/3627673.3680079"},{"key":"21_CR23","unstructured":"Wang, J., Wang, H., Wen, H., Min, G., Luo, M.: Trajweaver: trajectory recovery with state propagation diffusion model. arXiv (2024)"},{"issue":"3","key":"21_CR24","first-page":"921","volume":"33","author":"J Wang","year":"2019","unstructured":"Wang, J., Wu, N., Lu, X., Zhao, W., Feng, K.: Deep trajectory recovery with fine-grained calibration using Kalman filter. IEEE Trans. Knowl. Data Eng. 33(3), 921\u2013934 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Wei, T., Lin, Y., Lin, Y., Guo, S., Zhang, L., Wan, H.: Micro-macro spatial-temporal graph-based encoder-decoder for map-constrained trajectory recovery. IEEE Trans. Knowl. Data Eng. 6574\u20136587 (2024)","DOI":"10.1109\/TKDE.2024.3396158"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Wu, W., et al.: CDmap: complementarity and disparity-aware map inference quality enhancement. In: ICDE, pp. 4156\u20134168 (2025)","DOI":"10.1109\/ICDE65448.2025.00310"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Xi, D., Zhuang, F., Liu, Y., Gu, J., Xiong, H., He, Q.: Modelling of bi-directional spatio-temporal dependence and users\u2019 dynamic preferences for missing poi check-in identification. In: AAAI, pp. 5458\u20135465 (2019)","DOI":"10.1609\/aaai.v33i01.33015458"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Xia, T., et al.: AttnMove: history enhanced trajectory recovery via attentional network. In: AAAI, pp. 4494\u20134502 (2021)","DOI":"10.1609\/aaai.v35i5.16577"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Deng, L., Zhao, Y., Chen, J., Xie, J., Zheng, K.: SimiDTR: deep trajectory recovery with enhanced trajectory similarity. In: DASFAA, pp. 431\u2013447 (2023)","DOI":"10.1007\/978-3-031-30637-2_28"},{"key":"21_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Fan, Z., Lv, Z., Song, X., Shibasaki, R.: Long-term vessel trajectory imputation with physics-guided diffusion probabilistic model. In: SIGKDD, pp. 4398\u20134407 (2024)","DOI":"10.1145\/3637528.3672086"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-0375-8_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T11:01:59Z","timestamp":1778497319000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-0375-8_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819203741","9789819203758"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-0375-8_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2026.github.io\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}