{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:23:35Z","timestamp":1742995415001,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819996391"},{"type":"electronic","value":"9789819996407"}],"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\/978-981-99-9640-7_1","type":"book-chapter","created":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T15:02:38Z","timestamp":1704380558000},"page":"3-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explicit Coordination Based Multi-agent Reinforcement Learning for Intelligent Traffic Signal Control"],"prefix":"10.1007","author":[{"given":"Yixuan","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Che","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifeng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yichuan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,5]]},"reference":[{"key":"1_CR1","unstructured":"Reed, T.: INRIX global traffic scorecard (2019)"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Robertson, D.I., Bretherton, R.D.: Optimizing networks of traffic signals in real time-the SCOOT method. IEEE Trans. Veh. Technol. 40, 11\u201315 (1991)","DOI":"10.1109\/25.69966"},{"key":"1_CR3","unstructured":"Lowrie, P.R.: Scats, sydney co-ordinated adaptive traffic system: a traffic responsive method of controlling urban traffic (1990)"},{"issue":"6","key":"1_CR4","doi-asserted-by":"publisher","first-page":"3440","DOI":"10.1109\/TITS.2015.2461493","volume":"16","author":"B Ye","year":"2015","unstructured":"Ye, B., Weimin, W., Weijie, M.: A two-way arterial signal coordination method with queueing process considered. IEEE Trans. Intell. Transp. Syst. 16(6), 3440\u20133452 (2015)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1_CR5","doi-asserted-by":"publisher","unstructured":"Wiering, M.A., Martijn V.O.: Reinforcement Learning. Adaptation, learning, and optimization, p. 729 (2012). https:\/\/doi.org\/10.1007\/978-3-642-27645-3","DOI":"10.1007\/978-3-642-27645-3"},{"key":"1_CR6","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/978-3-642-14435-6_7","volume-title":"Innovations in Multi-Agent Systems and Applications - 1","author":"L Bu\u015foniu","year":"2010","unstructured":"Bu\u015foniu, L., Babu\u0161ka, R., De Schutter, B.: Multi-agent reinforcement learning: an overview. In: Srinivasan, D., Jain, L.C. (eds.) Innovations in Multi-Agent Systems and Applications - 1, pp. 183\u2013221. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-14435-6_7"},{"issue":"3","key":"1_CR7","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1109\/TITS.2013.2255286","volume":"14","author":"S El-Tantawy","year":"2013","unstructured":"El-Tantawy, S., Baher, A., Hossam, A.: Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC): methodology and large-scale application on downtown Toronto. IEEE Trans. Intell. Transp. Syst. 14(3), 1140\u20131150 (2013)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"3","key":"1_CR8","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1109\/TITS.2019.2901791","volume":"21","author":"T Chu","year":"2019","unstructured":"Chu, T., Wang, J., Codec\u00e0, L., et al.: Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Trans. Intell. Transp. Syst. 21(3), 1086\u20131095 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"104165","DOI":"10.1016\/j.engappai.2021.104165","volume":"100","author":"J Liu","year":"2021","unstructured":"Liu, J., Zhang, H., Fu, Z., et al.: Learning scalable multi-agent coordination by spatial differentiation for traffic signal control. Eng. Appl. Artif. Intell. Appl. Artif. Intell. 100, 104165 (2021)","journal-title":"Eng. Appl. Artif. Intell. Appl. Artif. Intell."},{"key":"1_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2021.102374","volume":"123","author":"W Zhao","year":"2022","unstructured":"Zhao, W., et al.: IPDALight: Intensity-and phase duration-aware traffic signal control based on reinforcement learning. J. Syst. Architect. 123, 102374 (2022)","journal-title":"J. Syst. Architect."},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Wei, H., et al.: CoLight: learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (2019)","DOI":"10.1145\/3357384.3357902"},{"key":"1_CR12","unstructured":"Zhu, L., et al.: Meta variationally intrinsic motivated reinforcement learning for decentralized traffic signal control. arXiv preprint\u00a0arXiv:2101.00746 (2021)"},{"key":"1_CR13","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.trc.2013.08.014","volume":"36","author":"P Varaiya","year":"2013","unstructured":"Varaiya, P.: Max pressure control of a network of signalized intersections. Trans. Res. C Emerg. Technol. 36, 177\u2013195 (2013)","journal-title":"Trans. Res. C Emerg. Technol."},{"key":"1_CR14","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-1-4471-5113-5_3","volume-title":"Advances in Applied Self-Organizing Systems","author":"S-B Cools","year":"2013","unstructured":"Cools, S.-B., Gershenson, C., D\u2019Hooghe, B.: Self-organizing traffic lights: a realistic simulation. In: Prokopenko, M. (ed.) Advances in Applied Self-Organizing Systems, pp. 45\u201355. Springer, London (2013). https:\/\/doi.org\/10.1007\/978-1-4471-5113-5_3"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Wei, H., et al.: IntelliLight: a reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018)","DOI":"10.1145\/3219819.3220096"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Wei, H., et al.: PressLight: learning max pressure control to coordinate traffic signals in arterial network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)","DOI":"10.1145\/3292500.3330949"},{"key":"1_CR17","unstructured":"Zhang, L., et al.: Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control. In: International Conference on Machine Learning. PMLR (2022)"},{"key":"1_CR18","unstructured":"Ma, J., Feng, W.: Feudal multi-agent deep reinforcement learning for traffic signal control. In: Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (2020)"},{"key":"1_CR19","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Mehul, D., Guillaume, S.: Multi-agent traffic signal control via distributed RL with spatial and temporal feature extraction. In: Melo, F.S., Fang, F. (eds.) Autonomous Agents and Multiagent Systems. Best and Visionary Papers: AAMAS 2022 Workshops, Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20179-0_7","DOI":"10.1007\/978-3-031-20179-0_7"},{"key":"1_CR20","unstructured":"Zheng, G., et al.: Diagnosing reinforcement learning for traffic signal control. arXiv preprint arXiv:1905.04716 (2019)"},{"key":"1_CR21","first-page":"4079","volume":"33","author":"A Oroojlooy","year":"2020","unstructured":"Oroojlooy, A., et al.: AttendLight: universal attention-based reinforcement learning model for traffic signal control. Adv. Neural Inform. Process. Syst. 33, 4079\u20134090 (2020)","journal-title":"Adv. Neural Inform. Process. Syst."}],"container-title":["Communications in Computer and Information Science","Computer Supported Cooperative Work and Social Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-9640-7_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T15:13:53Z","timestamp":1704381233000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-9640-7_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819996391","9789819996407"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-9640-7_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"5 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ChineseCSCW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF Conference on Computer Supported Cooperative Work  and Social Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Harbin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 August 2023","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":"chinesecscw2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.scholat.com\/ccscw\/2023","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes. Microsoft CMT.","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"221","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"54","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}