{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:31:03Z","timestamp":1742981463303,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819755516"},{"type":"electronic","value":"9789819755523"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-5552-3_5","type":"book-chapter","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:04:15Z","timestamp":1727679855000},"page":"70-85","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MIPM: A Multidimensional Information Perception Model for\u00a0Estimating Time of\u00a0Arrival on\u00a0Real Road Networks"],"prefix":"10.1007","author":[{"given":"Tangpeng","family":"Dan","sequence":"first","affiliation":[]},{"given":"Xiao","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Yingtao","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Haojie","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Xiaofeng","family":"Meng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"5_CR1","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. ArXiv abs\/1803.01271 (2018)"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Z., et al.: Interpreting trajectories from multiple views: a hierarchical self-attention network for estimating the time of arrival. In: ACM SIGKDD, pp. 2771\u20132779 (2022)","DOI":"10.1145\/3534678.3539051"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Cheng, H.T., et\u00a0al.: Wide & deep learning for recommender systems. In: DLRS, pp. 7\u201310 (2016)","DOI":"10.1145\/2988450.2988454"},{"issue":"12","key":"5_CR4","doi-asserted-by":"publisher","first-page":"23721","DOI":"10.1109\/TITS.2022.3203432","volume":"23","author":"T Dan","year":"2022","unstructured":"Dan, T., Luo, C., Li, Y., Guan, Z., Meng, X.: Lg-tree: an efficient labeled index for shortest distance search on massive road networks. IEEE Trans. Intell. Transp. Syst. 23(12), 23721\u201323735 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Dan, T., Pan, X., Zheng, B., Meng, X.: Double hierarchical labeling shortest distance querying in time-dependent road networks. In: IEEE ICDE, pp. 2077\u20132089. IEEE (2023)","DOI":"10.1109\/ICDE55515.2023.00161"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Duan, K., Keerthi, S.S., Chu, W., Shevade, S.K., Poo, A.N.: Multi-category classification by soft-max combination of binary classifiers. In: International Workshop on Multiple Classifier Systems, pp. 125\u2013134. Springer (2003)","DOI":"10.1007\/3-540-44938-8_13"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Fang, Z., Long, Q., Song, G., Xie, K.: Spatial-temporal graph ode networks for traffic flow forecasting. ACM SIGKDD (2021)","DOI":"10.1145\/3447548.3467430"},{"issue":"1","key":"5_CR8","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.tranpol.2006.07.001","volume":"14","author":"JW Grotenhuis","year":"2007","unstructured":"Grotenhuis, J.W., Wiegmans, B.W., Rietveld, P.: The desired quality of integrated multimodal travel information in public transport: customer needs for time and effort savings. Transp. Policy 14(1), 27\u201338 (2007)","journal-title":"Transp. Policy"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Han, J., Moraga, C.: The influence of the sigmoid function parameters on the speed of backpropagation learning. In: International workshop on artificial neural networks, pp. 195\u2013201. Springer (1995)","DOI":"10.1007\/3-540-59497-3_175"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Han, L., Du, B., Sun, L., Fu, Y., Lv, Y., Xiong, H.: Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. ACM SIGKDD (2021)","DOI":"10.1145\/3447548.3467275"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Harrington, A., Cahill, V.: Route profiling: putting context to work. In: ACM Symposium on Applied Computing, pp. 1567\u20131573 (2004)","DOI":"10.1145\/967900.968214"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Hong, H., et al.: Heteta: heterogeneous information network embedding for estimating time of arrival. In: ACM SIGKDD, pp. 2444\u20132454 (2020)","DOI":"10.1145\/3394486.3403294"},{"key":"5_CR13","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.trb.2013.03.008","volume":"53","author":"E Jenelius","year":"2013","unstructured":"Jenelius, E., Koutsopoulos, H.N.: Travel time estimation for urban road networks using low frequency probe vehicle data. Transp. Res. Part B: Methodological 53, 64\u201381 (2013)","journal-title":"Transp. Res. Part B: Methodological"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Ji, J., Wang, J., Huang, C., Wu, J., Xu, B., Wu, Z., Zhang, J., Zheng, Y.: Spatio-temporal self-supervised learning for traffic flow prediction. In: AAAI (2022)","DOI":"10.1609\/aaai.v37i4.25555"},{"key":"5_CR15","unstructured":"Jiang, W., Luo, J.: Graph neural network for traffic forecasting: A survey. ArXiv abs\/2101.11174 (2021)"},{"key":"5_CR16","unstructured":"Jindal, I., Chen, X., Nokleby, M., Ye, J., et\u00a0al.: A unified neural network approach for estimating travel time and distance for a taxi trip. arXiv preprint arXiv:1710.04350 (2017)"},{"key":"5_CR17","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. NIPS 25, 1097\u20131105 (2012)","journal-title":"NIPS"},{"key":"5_CR18","unstructured":"Kyunghyun\u00a0Cho, Bart van\u00a0Merrienboer, D.B., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. In: SSST@EMNLP, pp. 103\u2013111 (2014)"},{"key":"5_CR19","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Graph convolutional recurrent neural network: Data-driven traffic forecasting. ArXiv abs\/1707.01926 (2017)"},{"key":"5_CR20","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Sun, Y., et al.: Fma-eta: estimating travel time entirely based on ffn with attention. In: IEEE ICASSP, pp. 3355\u20133359. IEEE (2021)","DOI":"10.1109\/ICASSP39728.2021.9414054"},{"key":"5_CR22","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. ArXiv abs\/1409.3215 (2014)"},{"key":"5_CR23","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhang, J., Cao, W., Li, J., Zheng, Y.: When will you arrive? estimating travel time based on deep neural networks. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11877"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Wang, H., Kuo, Y.H., Kifer, D., Li, Z.: A simple baseline for travel time estimation using large-scale trip data. In: ACM SIGSPATIAL, p.\u00a061. Association for Computing Machinery (2016)","DOI":"10.1145\/2996913.2996943"},{"issue":"2","key":"5_CR26","first-page":"1","volume":"10","author":"H Wang","year":"2019","unstructured":"Wang, H., Tang, X., Kuo, Y.H., Kifer, D., Li, Z.: A simple baseline for travel time estimation using large-scale trip data. ACM TIST 10(2), 1\u201322 (2019)","journal-title":"ACM TIST"},{"issue":"4","key":"5_CR27","first-page":"3712","volume":"35","author":"T Wang","year":"2022","unstructured":"Wang, T., Luo, H., Bao, Z., Duan, L.: Dynamic ridesharing with minimal regret: towards an enhanced engagement among three stakeholders. IEEE TKDE 35(4), 3712\u20133726 (2022)","journal-title":"IEEE TKDE"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Y., Aste, T.: Sparsification and filtering for spatial-temporal gnn in multivariate time-series. ArXiv abs\/2203.03991 (2022)","DOI":"10.1145\/3533271.3561678"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Z., Fu, K., Ye, J.: Learning to estimate the travel time. In: ACM SIGKDD, pp. 858\u2013866 (2018)","DOI":"10.1145\/3219819.3219900"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)","DOI":"10.24963\/ijcai.2018\/505"},{"issue":"10","key":"5_CR32","first-page":"2390","volume":"25","author":"NJ Yuan","year":"2012","unstructured":"Yuan, N.J., Zheng, Y., Zhang, L., Xie, X.: T-finder: a recommender system for finding passengers and vacant taxis. IEEE TKDE 25(10), 2390\u20132403 (2012)","journal-title":"IEEE TKDE"},{"key":"5_CR33","first-page":"22343","volume":"23","author":"K Zhang","year":"2022","unstructured":"Zhang, K., Feng, X., Wu, L., He, Z.: Trajectory prediction for autonomous driving using spatial-temporal graph attention transformer. IEEE TITS 23, 22343\u201322353 (2022)","journal-title":"IEEE TITS"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, X., Huang, C., Xu, Y., Xia, L.: Spatial-temporal convolutional graph attention networks for citywide traffic flow forecasting. ACM CIKM (2020)","DOI":"10.1145\/3340531.3411941"},{"key":"5_CR35","first-page":"3848","volume":"21","author":"L Zhao","year":"2020","unstructured":"Zhao, L., et al.: T-gcn: a temporal graph convolutional network for traffic prediction. IEEE TITS 21, 3848\u20133858 (2020)","journal-title":"IEEE TITS"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Zheng, C., Fan, X., Wang, C., Qi, J.: Gman: A graph multi-attention network for traffic prediction. In: AAAI, vol.\u00a034, pp. 1234\u20131241 (2020)","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Zhuang, D., Wang, S., Koutsopoulos, H.N., Zhao, J.: Uncertainty quantification of sparse travel demand prediction with spatial-temporal graph neural networks. ACM SIGKDD (2022)","DOI":"10.1145\/3534678.3539093"}],"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\/978-981-97-5552-3_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:06:15Z","timestamp":1727679975000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5552-3_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755516","9789819755523"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5552-3_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 October 2024","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":"Gifu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}