{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T23:07:37Z","timestamp":1764198457777,"version":"3.46.0"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819549627"},{"type":"electronic","value":"9789819549634"}],"license":[{"start":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T00:00:00Z","timestamp":1763769600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T00:00:00Z","timestamp":1763769600000},"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\/978-981-95-4963-4_19","type":"book-chapter","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T17:06:53Z","timestamp":1763744813000},"page":"226-238","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TAG: Temporal Attention Graph for\u00a0Heterogeneous Traffic Trajectory Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0316-9691","authenticated-orcid":false,"given":"Vishal A.","family":"Patel","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8050-5388","authenticated-orcid":false,"given":"Yi","family":"Guo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0201-4409","authenticated-orcid":false,"given":"Laurence","family":"Park","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8284-2062","authenticated-orcid":false,"given":"Oliver","family":"Obst","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,22]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 961\u2013971. IEEE, Las Vegas (2016)","DOI":"10.1109\/CVPR.2016.110"},{"issue":"1","key":"19_CR2","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1109\/TIV.2023.3293088","volume":"9","author":"M Awan","year":"2024","unstructured":"Awan, M., Shin, J., Whangbo, T.K.: Trajectory prediction of heterogeneous traffic agents with collision vigilance and avoidance. IEEE Trans. Intell. Veh. 9(1), 93\u2013102 (2024)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"113816","DOI":"10.1016\/j.eswa.2020.113816","volume":"165","author":"C Badue","year":"2021","unstructured":"Badue, C., et al.: Self-driving cars: a survey. Expert Syst. Appl. 165, 113816 (2021)","journal-title":"Expert Syst. Appl."},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Burov, V., et al.: Development of the automotive radar for the systems of adaptive cruise control and automatic emergency breaking. In: 2019 International Conference on Engineering and Telecommunication (EnT), pp.\u00a01\u20137. IEEE, Dolgoprudny (2019)","DOI":"10.1109\/EnT47717.2019.9030600"},{"key":"19_CR5","doi-asserted-by":"publisher","first-page":"102301","DOI":"10.1016\/j.jairtraman.2022.102301","volume":"106","author":"K Cai","year":"2023","unstructured":"Cai, K., Shen, Z., Luo, X., Li, Y.: Temporal attention aware dual-graph convolution network for air traffic flow prediction. J. Air Transp. Manag. 106, 102301 (2023)","journal-title":"J. Air Transp. Manag."},{"issue":"3","key":"19_CR6","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1111\/cdev.13469","volume":"92","author":"KP Darby","year":"2021","unstructured":"Darby, K.P., Deng, S.W., Walther, D.B., Sloutsky, V.M.: The development of attention to objects and scenes: from object-biased to unbiased. Child Dev. 92(3), 1173\u20131186 (2021)","journal-title":"Child Dev."},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Dong, Y., Wang, L., Zhou, S., Hua, G.: Sparse instance conditioned multimodal trajectory prediction. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9729\u20139738. IEEE, Paris (2023)","DOI":"10.1109\/ICCV51070.2023.00895"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.: Social GAN: socially acceptable trajectories with generative adversarial networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2255\u20132264. IEEE, Salt Lake City (2018)","DOI":"10.1109\/CVPR.2018.00240"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Huang, X., et al.: The ApolloScape dataset for autonomous driving. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1067\u201310676. IEEE, Salt Lake City (2018)","DOI":"10.1109\/CVPRW.2018.00141"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Leal-Taixe, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., Savarese, S.: Learning an image-based motion context for multiple people tracking. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3542\u20133549. IEEE, Columbus (2014)","DOI":"10.1109\/CVPR.2014.453"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Li, R., Katsigiannis, S., Kim, T.K., Shum, H.P.H.: BP-SGCN: behavioral pseudo-label informed sparse graph convolution network for pedestrian and heterogeneous trajectory prediction. IEEE Trans. Neural Netw. Learn. Syst. 1\u201315 (2025)","DOI":"10.1109\/TNNLS.2025.3545268"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Li, R., Qiao, T., Katsigiannis, S., Zhu, Z., Shum, H.P.H.: Unified spatial-temporal edge-enhanced graph networks for pedestrian trajectory prediction. IEEE Trans. Circuits Syst. Video Technol. 1 (2025)","DOI":"10.1109\/TCSVT.2025.3539522"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Li, X., Ying, X., Chuah, M.C.: GRIP++: enhanced graph-based interaction-aware trajectory prediction for autonomous driving (2020). arXiv:1907.07792 [cs]","DOI":"10.1109\/ITSC.2019.8917228"},{"issue":"3","key":"19_CR14","doi-asserted-by":"publisher","first-page":"2862","DOI":"10.1007\/s10489-022-03524-1","volume":"53","author":"J Lian","year":"2023","unstructured":"Lian, J., Ren, W., Li, L., Zhou, Y., Zhou, B.: PTP-STGCN: pedestrian trajectory prediction based on a spatio-temporal graph convolutional neural network. Appl. Intell. 53(3), 2862\u20132878 (2023)","journal-title":"Appl. Intell."},{"issue":"11","key":"19_CR15","doi-asserted-by":"publisher","first-page":"16504","DOI":"10.1109\/TNNLS.2023.3294998","volume":"35","author":"R Liang","year":"2024","unstructured":"Liang, R., Li, Y., Zhou, J., Li, X.: STGlow: a flow-based generative framework with dual-graphormer for pedestrian trajectory prediction. IEEE Trans. Neural Netw. Learn. Syst. 35(11), 16504\u201316517 (2024)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"11","key":"19_CR16","doi-asserted-by":"publisher","first-page":"5633","DOI":"10.1109\/TKDE.2024.3390765","volume":"36","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Li, B., Wang, X., Sammut, C., Yao, L.: Attention-aware social graph transformer networks for stochastic trajectory prediction. IEEE Trans. Knowl. Data Eng. 36(11), 5633\u20135646 (2024)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Ma, Y., Zhu, X., Zhang, S., Yang, R., Wang, W., Manocha, D.: TrafficPredict: trajectory prediction for heterogeneous traffic-agents. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 6120\u20136127 (2019)","DOI":"10.1609\/aaai.v33i01.33016120"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Qian, K., Elhoseiny, M., Claudel, C.: Social-STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14412\u201314420. IEEE, Seattle (2020)","DOI":"10.1109\/CVPR42600.2020.01443"},{"issue":"5","key":"19_CR19","doi-asserted-by":"publisher","first-page":"99","DOI":"10.3390\/jimaging9050099","volume":"9","author":"Y Nokihara","year":"2023","unstructured":"Nokihara, Y., Hachiuma, R., Hori, R., Saito, H.: Future prediction of shuttlecock trajectory in badminton using player\u2019s information. J. Imaging 9(5), 99 (2023)","journal-title":"J. Imaging"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Pellegrini, S., Ess, A., Schindler, K., Van\u00a0Gool, L.: You\u2019ll never walk alone: modeling social behavior for multi-target tracking. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 261\u2013268. IEEE, Kyoto (2009)","DOI":"10.1109\/ICCV.2009.5459260"},{"issue":"2","key":"19_CR21","doi-asserted-by":"publisher","first-page":"138","DOI":"10.26599\/JICV.2023.9210036","volume":"7","author":"Z Sheng","year":"2024","unstructured":"Sheng, Z., Huang, Z., Chen, S.: Kinematics-aware multigraph attention network with residual learning for heterogeneous trajectory prediction. J. Intell. Connect. Veh. 7(2), 138\u2013150 (2024)","journal-title":"J. Intell. Connect. Veh."},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Wu, Y., Wang, L., Zhou, S., Duan, J., Hua, G., Tang, W.: Multi-stream representation learning for pedestrian trajectory prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 3, pp. 2875\u20132882 (2023)","DOI":"10.1609\/aaai.v37i3.25389"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Xu, P., Hayet, J.B., Karamouzas, I.: SocialVAE: human trajectory prediction using timewise latents. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022. LNCS, vol. 13664, pp. 511\u2013528. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-19772-7_30"},{"key":"19_CR24","doi-asserted-by":"publisher","first-page":"109361","DOI":"10.1016\/j.compeleceng.2024.109361","volume":"118","author":"Y Xu","year":"2024","unstructured":"Xu, Y., et al.: STI-TP: a spatio-temporal interleaved model for multi-modal trajectory prediction of heterogeneous traffic agents. Comput. Electr. Eng. 118, 109361 (2024)","journal-title":"Comput. Electr. Eng."},{"issue":"6","key":"19_CR25","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.3390\/buildings13061502","volume":"13","author":"Q Yang","year":"2023","unstructured":"Yang, Q., Mei, Q., Fan, C., Ma, M., Li, X.: Environment-aware worker trajectory prediction using surveillance camera in modular construction facilities. Buildings 13(6), 1502 (2023)","journal-title":"Buildings"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhao, L., Dong, C., Wu, L., Zheng, L.: AI-TP: attention-based interaction-aware trajectory prediction for autonomous driving. IEEE Trans. Intell. Veh. 1 (2022)","DOI":"10.1109\/TIV.2022.3155236"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, L., Li, P., Chen, J., Shen, S.: Trajectory prediction with graph-based dual-scale context fusion. In: 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 11374\u201311381 (2022). iSSN: 2153-0866","DOI":"10.1109\/IROS47612.2022.9981923"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Zhao, H., Wildes, R.P.: Where are you heading? Dynamic trajectory prediction with expert goal examples. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 7609\u20137618. IEEE, Montreal (2021)","DOI":"10.1109\/ICCV48922.2021.00753"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Qian, D., Ren, D., Xia, H.: StarNet: pedestrian trajectory prediction using deep neural network in star topology. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8075\u20138080. IEEE, Macau (2019)","DOI":"10.1109\/IROS40897.2019.8967811"}],"container-title":["Lecture Notes in Computer Science","Multi-disciplinary Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4963-4_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T23:03:05Z","timestamp":1764198185000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4963-4_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,22]]},"ISBN":["9789819549627","9789819549634"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4963-4_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,11,22]]},"assertion":[{"value":"22 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIWAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multi-disciplinary Trends in Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miwai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miwai25.miwai.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}