{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T18:46:10Z","timestamp":1764701170495,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031683114"},{"type":"electronic","value":"9783031683121"}],"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\/978-3-031-68312-1_7","type":"book-chapter","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T18:01:59Z","timestamp":1723831319000},"page":"95-109","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Understanding Human Mobility Characteristics Through Behavior and\u00a0Corresponding Environmental Information"],"prefix":"10.1007","author":[{"given":"Ryuichi","family":"Sudo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4883-527X","authenticated-orcid":false,"given":"Hiroyuki","family":"Toda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,17]]},"reference":[{"issue":"2","key":"7_CR1","first-page":"1","volume":"54","author":"S Wang","year":"2021","unstructured":"Wang, S., Bao, Z., Culpepper, J.S., Cong, G.: A survey on trajectory data management, analytics, and learning. ACM Comput. Surv. 54(2), 1\u201336 (2021)","journal-title":"ACM Comput. Surv."},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.inffus.2020.08.003","volume":"65","author":"A Belhadi","year":"2021","unstructured":"Belhadi, A., Djenouri, Y., Srivastava, G., Djenouri, D., Lin, J.C.-W., Fortino, G.: Deep learning for pedestrian collective behavior analysis in smart cities: a model of group trajectory outlier detection. Inf. Fusion 65, 13\u201320 (2021)","journal-title":"Inf. Fusion"},{"issue":"3","key":"7_CR3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1080\/15472450.2019.1646132","volume":"25","author":"Y Yao","year":"2021","unstructured":"Yao, Y., Zhao, X., Wu, Y., Zhang, Y., Rong, J.: Clustering driver behavior using dynamic time warping and hidden Markov model. J. Intell. Transp. Syst. 25(3), 249\u2013262 (2021)","journal-title":"J. Intell. Transp. Syst."},{"key":"7_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2021.101619","volume":"87","author":"S Hu","year":"2021","unstructured":"Hu, S., et al.: Urban function classification at road segment level using taxi trajectory data: a graph convolutional neural network approach. Comput. Environ. Urban Syst. 87, 101619 (2021)","journal-title":"Comput. Environ. Urban Syst."},{"key":"7_CR5","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.ins.2020.05.107","volume":"538","author":"C Gao","year":"2020","unstructured":"Gao, C., Zhang, Z., Huang, C., Yin, H., Yang, Q., Shao, J.: Semantic trajectory representation and retrieval via hierarchical embedding. Inf. Sci. 538, 176\u2013192 (2020)","journal-title":"Inf. Sci."},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Dong, W., Yuan, T., Yang, K., Li, C., Zhang, S.: Autoencoder regularized network for driving style representation learning. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 1603\u20131609. AAAI Press (2017)","DOI":"10.24963\/ijcai.2017\/222"},{"issue":"1","key":"7_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-017-0473-y","volume":"7","author":"T Nishimura","year":"2017","unstructured":"Nishimura, T., Nishida, K., Toda, H., Sawada, H.: Social media knows what road it is: quantifying road characteristics with geo-tagged posts. Soc. Netw. Anal. Min. 7(1), 1\u201320 (2017)","journal-title":"Soc. Netw. Anal. Min."},{"issue":"7","key":"7_CR8","doi-asserted-by":"publisher","first-page":"7599","DOI":"10.1007\/s10489-021-02754-z","volume":"52","author":"L He","year":"2022","unstructured":"He, L., Niu, X., Chen, T., Mei, K., Li, M.: Spatio-temporal trajectory anomaly detection based on common sub-sequence. Appl. Intell. 52(7), 7599\u20137621 (2022)","journal-title":"Appl. Intell."},{"key":"7_CR9","unstructured":"Yi, B.-K., Jagadish, H.V., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: Proceedings 14th International Conference on Data Engineering, pp. 201\u2013208 (1998)"},{"issue":"1","key":"7_CR10","doi-asserted-by":"publisher","first-page":"349","DOI":"10.3390\/su12010349","volume":"12","author":"A Crivellari","year":"2020","unstructured":"Crivellari, A., Beinat, E.: LSTM-based deep learning model for predicting individual mobility traces of short-term foreign tourists. Sustainability 12(1), 349 (2020)","journal-title":"Sustainability"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Zhou, S., Li, J., Wang, H., Shang, S., Han, P.: GRLSTM: trajectory similarity computation with graph-based residual LSTM. In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence, AAAI 2023\/IAAI 2023\/EAAI 2023. AAAI Press (2023)","DOI":"10.1609\/aaai.v37i4.25624"},{"key":"7_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/978-3-319-57454-7_13","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Y Endo","year":"2017","unstructured":"Endo, Y., Nishida, K., Toda, H., Sawada, H.: Predicting destinations from partial trajectories using recurrent neural network. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 160\u2013172. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-57454-7_13"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Li, X., Zhao, K., Cong, G., Jensen, C.S., Wei, W.: Deep representation learning for trajectory similarity computation. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 617\u2013628 (2018)","DOI":"10.1109\/ICDE.2018.00062"},{"issue":"1","key":"7_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3314407","volume":"3","author":"S Nair","year":"2019","unstructured":"Nair, S., Javkar, K., Wu, J., Frias-Martinez, V.: Understanding cycling trip purpose and route choice using GPS traces and open data. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(1), 1\u201326 (2019)","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"7_CR15","unstructured":"Dong, W., Li, J., Yao, R., Li, C., Yuan, T., Wang, L.: Characterizing driving styles with deep learning. ArXiv, abs\/1607.03611 (2016)"},{"issue":"3","key":"7_CR16","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1109\/TITS.2013.2262376","volume":"14","author":"L Moreira-Matias","year":"2013","unstructured":"Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.: Predicting taxi - passenger demand using streaming data. IEEE Trans. Intell. Transp. Syst. 14(3), 1393\u20131402 (2013)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"7_CR17","unstructured":"Bernstein, D., Kornhauser, A.L.: An introduction to map matching for personal navigation assistants (1998)"},{"issue":"1","key":"7_CR18","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/S0968-090X(00)00026-7","volume":"8","author":"CE White","year":"2000","unstructured":"White, C.E., Bernstein, D., Kornhauser, A.L.: Some map matching algorithms for personal navigation assistants. Transp. Res. Part C Emerg. Technol. 8(1), 91\u2013108 (2000)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"issue":"5","key":"7_CR19","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189\u20131232 (2001)","journal-title":"Ann. Stat."},{"key":"7_CR20","unstructured":"Moosavi, S., Mahajan, P.D., Parthasarathy, S., Saunders-Chukwu, C., Ramnath, R.: Driving style representation in convolutional recurrent neural network model of driver identification. ArXiv, abs\/2102.05843 (2021)"}],"container-title":["Lecture Notes in Computer Science","Database and Expert Systems Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-68312-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T18:02:40Z","timestamp":1723831360000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-68312-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031683114","9783031683121"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-68312-1_7","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":"17 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Naples","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"26 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"35","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dexa.org\/dexa2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}