{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:43:47Z","timestamp":1783701827209,"version":"3.55.0"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52372314"],"award-info":[{"award-number":["52372314"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1109\/tits.2025.3525538","type":"journal-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T18:45:42Z","timestamp":1737053142000},"page":"7021-7035","source":"Crossref","is-referenced-by-count":13,"title":["Soft Actor-Critic Deep Reinforcement Learning for Train Timetable Collaborative Optimization of Large-Scale Urban Rail Transit Network Under Dynamic Demand"],"prefix":"10.1109","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2621-6578","authenticated-orcid":false,"given":"Longhui","family":"Wen","sequence":"first","affiliation":[{"name":"Intelligent Transportation System Research Center, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4631-6062","authenticated-orcid":false,"given":"Liyang","family":"Hu","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3225-0576","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Intelligent Transportation System Research Center, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3412-0831","authenticated-orcid":false,"given":"Gang","family":"Ren","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Intelligent Transportation System Research Center, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2016.2549282"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2013.08.016"},{"key":"ref3","volume-title":"Urban Rail Transit 2022 Annual Statistical and Analytical Report","year":"2023"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2021.02.059"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2021.10.002"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/tnse.2022.3168871"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2023.103142"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2022.05.001"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2023.104278"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2018.1488132"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/tnn.1998.712192"},{"key":"ref12","article-title":"Agent57: Outperforming the Atari human benchmark","author":"Badia","year":"2020","journal-title":"arXiv:2003.13350"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1812.05905"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-7784-8"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/0305-0548(95)00032-1"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2015.2415513"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2022.103679"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2015.03.004"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2018.02.013"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2021.102975"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107858"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1287\/trsc.1070.0200"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2020.03.034"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2018.2829165"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2020.08.005"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2023.02.015"},{"key":"ref27","article-title":"Playing Atari with deep reinforcement learning","author":"Mnih","year":"2013","journal-title":"arXiv:1312.5602"},{"key":"ref28","article-title":"Continuous control with deep reinforcement learning","author":"Lillicrap","year":"2015","journal-title":"arXiv:1509.02971"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"ref30","article-title":"Dueling network architectures for deep reinforcement learning","author":"Wang","year":"2015","journal-title":"arXiv:1511.06581"},{"key":"ref31","article-title":"Actor-critic algorithms","volume-title":"Advances in Neural Information Processing Systems","author":"Konda","year":"1999"},{"key":"ref32","first-page":"387","article-title":"Deterministic policy gradient algorithms","volume-title":"Proc. 31st Int. Conf. Mach. Learn.","author":"Silver"},{"key":"ref33","article-title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","author":"Haarnoja","year":"2018","journal-title":"arXiv:1801.01290"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1061\/jtepbs.teeng-8407"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2019.2963785"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2023.3319135"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2016.01.004"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2021.3063399"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2021.3131637"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2021.09.016"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/s11116-023-10432-x"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2018.03.012"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2022.103964"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1142\/10553"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1680\/ipeds.1952.11259"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/s0191-2615(03)00026-2"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/case56687.2023.10260453"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1080\/23249935.2016.1166158"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2018.2818182"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.125599"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2022.3215095"},{"key":"ref52","article-title":"Decision transformer: Reinforcement learning via sequence modeling","author":"Chen","year":"2021","journal-title":"arXiv:2106.01345"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2022.3151651"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2020.3030164"},{"key":"ref55","article-title":"Addressing function approximation error in actor-critic methods","author":"Fujimoto","year":"2018","journal-title":"arXiv:1802.09477"},{"key":"ref56","article-title":"Differential evolution: A review of more than two decades of research","volume":"90","author":"Pant","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2018.2871347"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12300"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6979\/10988578\/10844003.pdf?arnumber=10844003","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T17:06:11Z","timestamp":1746551171000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10844003\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":58,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tits.2025.3525538","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]}}}