{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T08:55:24Z","timestamp":1730278524509,"version":"3.28.0"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T00:00:00Z","timestamp":1695513600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T00:00:00Z","timestamp":1695513600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,24]]},"DOI":"10.1109\/itsc57777.2023.10421998","type":"proceedings-article","created":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T23:32:39Z","timestamp":1707867159000},"page":"1215-1220","source":"Crossref","is-referenced-by-count":0,"title":["Metro Ridership Forecasting using Inter-Station-Aware Transformer Networks"],"prefix":"10.1109","author":[{"given":"Khaled","family":"Saleh","sequence":"first","affiliation":[{"name":"School of Information and Physical Sciences, University of Newcastle,Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adriana-Simona","family":"Mihaita","sequence":"additional","affiliation":[{"name":"University of Technology,Faculty of Engineering and IT,Sydney,Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuming","family":"Ou","sequence":"additional","affiliation":[{"name":"University of Technology,Faculty of Engineering and IT,Sydney,Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tust.2020.103783"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2019.106076"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.02.005"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9925939"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271757"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.08.005"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3036057"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.03.085"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2902405"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/317"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"volume-title":"Rail digital twin and deep learning for passenger flow prediction using mobile data","year":"2023","author":"Ou","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC45102.2020.9294606"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC55140.2022.9921763"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/B978-0-323-90769-9.00010-4","article-title":"Chapter 14 - big data processing and analysis on the impact of covid-19 on public transport delay","volume-title":"Data Science for COVID-19","author":"Ou","year":"2022"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2711046"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8060243"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"issue":"16","key":"ref21","first-page":"359","article-title":"Using dynamic time warping to find patterns in time series","volume-title":"KDD workshop","volume":"10","author":"Berndt"},{"key":"ref22","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03819-2"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/s22197495"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC55140.2022.9921978"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/274"},{"key":"ref27","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"Li","year":"2018","journal-title":"ICLR 2018"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2013.00021"}],"event":{"name":"2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)","start":{"date-parts":[[2023,9,24]]},"location":"Bilbao, Spain","end":{"date-parts":[[2023,9,28]]}},"container-title":["2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10420842\/10420843\/10421998.pdf?arnumber=10421998","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:37:58Z","timestamp":1710362278000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10421998\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,24]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/itsc57777.2023.10421998","relation":{},"subject":[],"published":{"date-parts":[[2023,9,24]]}}}