{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:47:28Z","timestamp":1784821648407,"version":"3.55.0"},"reference-count":170,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T00:00:00Z","timestamp":1623110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61772377, U20A20177, 61672257, and 91746206"],"award-info":[{"award-number":["61772377, U20A20177, 61672257, and 91746206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Fundamental Research Funds for Central Universities","award":["2042020kf0217"],"award-info":[{"award-number":["2042020kf0217"]}]},{"name":"Opening Foundation of the State Key Laboratory of Integrated Services Networks","award":["ISN21-10"],"award-info":[{"award-number":["ISN21-10"]}]},{"name":"Science and Technology Planning Project of Shenzhen","award":["JCYJ20170818112550194"],"award-info":[{"award-number":["JCYJ20170818112550194"]}]},{"name":"NSF","award":["III-1763325, III-1909323, and SaTC-1930941"],"award-info":[{"award-number":["III-1763325, III-1909323, and SaTC-1930941"]}]},{"name":"ARC DECRA Project","award":["DE200100964"],"award-info":[{"award-number":["DE200100964"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            <jats:bold>Vehicular ad hoc networks<\/jats:bold>\n            (\n            <jats:bold>VANETs<\/jats:bold>\n            ) and the services they support are an essential part of intelligent transportation. Through physical technologies, applications, protocols, and standards, they help to ensure traffic moves efficiently and vehicles operate safely. This article surveys the current state of play in VANETs development. The summarized and classified include the key technologies critical to the field, the resource-management and safety applications needed for smooth operations, the communications and data transmission protocols that support networking, and the theoretical and environmental constructs underpinning research and development, such as graph neural networks and the Internet of Things. Additionally, we identify and discuss several challenges facing VANETs, including poor safety, poor reliability, non-uniform standards, and low intelligence levels. Finally, we touch on hot technologies and techniques, such as reinforcement learning and 5G communications, to provide an outlook for the future of intelligent transportation systems.\n          <\/jats:p>","DOI":"10.1145\/3451984","type":"journal-article","created":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T18:22:54Z","timestamp":1623176574000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":87,"title":["A Comprehensive Survey of the Key Technologies and Challenges Surrounding Vehicular Ad Hoc Networks"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5817-1732","authenticated-orcid":false,"given":"Zhenchang","family":"Xia","sequence":"first","affiliation":[{"name":"Wuhan University, Xidian University, China; Macquarie University, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1371-5801","authenticated-orcid":false,"given":"Jia","family":"Wu","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Libing","family":"Wu","sequence":"additional","affiliation":[{"name":"Wuhan University, Xidian University, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjiao","family":"Chen","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,6,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2017.10.009"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3041754"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC.2017.7986334"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2795381"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2879413"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2823312"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2013.02.036"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3030251"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2011.10.011"},{"key":"e_1_2_1_10_1","volume-title":"The IEEE International Conference on Network Protocols. 1\u20135.","author":"Amaral Pedro","unstructured":"Pedro Amaral , Joao Dinis , Paulo Pinto , Luis Bernardo , Joao Tavares , and Henrique S. Mamede . 2016. Machine learning in software defined networks: Data collection and traffic classification . In The IEEE International Conference on Network Protocols. 1\u20135. Pedro Amaral, Joao Dinis, Paulo Pinto, Luis Bernardo, Joao Tavares, and Henrique S. Mamede. 2016. Machine learning in software defined networks: Data collection and traffic classification. In The IEEE International Conference on Network Protocols. 1\u20135."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/2991309.2991352"},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning. 463\u2013474","author":"Ayoub Alex","year":"2020","unstructured":"Alex Ayoub , Zeyu Jia , Csaba Szepesvari , Mengdi Wang , and Lin Yang . 2020 . Model-based reinforcement learning with value-targeted regression . In Proceedings of the 37th International Conference on Machine Learning. 463\u2013474 . Alex Ayoub, Zeyu Jia, Csaba Szepesvari, Mengdi Wang, and Lin Yang. 2020. Model-based reinforcement learning with value-targeted regression. In Proceedings of the 37th International Conference on Machine Learning. 463\u2013474."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-018-2283-z"},{"key":"e_1_2_1_14_1","volume-title":"et\u00a0al","author":"Battaglia Peter W.","year":"2018","unstructured":"Peter W. Battaglia , Jessica B. Hamrick , Victor Bapst , Alvaro Sanchez-Gonzalez , Vinicius Zambaldi , Mateusz Malinowski , Andrea Tacchetti , David Raposo , Adam Santoro , Ryan Faulkner , et\u00a0al . 2018 . Relational inductive biases, deep learning, and graph networks. arXiv:1806.01261. (2018). https:\/\/arxiv.org\/abs\/1806.01261. Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et\u00a0al. 2018. Relational inductive biases, deep learning, and graph networks. arXiv:1806.01261. (2018). https:\/\/arxiv.org\/abs\/1806.01261."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2600300"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2600300"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449834"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.3032652"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3213232.3213237"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.1900499"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3274895.3274896"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2019.12.054"},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the 32th AAAI Conference on Artificial Intelligence.","author":"Chen Shuo","year":"2018","unstructured":"Shuo Chen , Athirai Irissappane , and Jie Zhang . 2018 . POMDP-based decision making for fast event handling in VANETs . In Proceedings of the 32th AAAI Conference on Artificial Intelligence. Shuo Chen, Athirai Irissappane, and Jie Zhang. 2018. POMDP-based decision making for fast event handling in VANETs. In Proceedings of the 32th AAAI Conference on Artificial Intelligence."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2016.2590302"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2990935"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2017.2750709"},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning. 2048\u20132056","author":"Cobbe Karl","year":"2020","unstructured":"Karl Cobbe , Chris Hesse , Jacob Hilton , and John Schulman . 2020 . Leveraging procedural generation to benchmark reinforcement learning . In Proceedings of the 37th International Conference on Machine Learning. 2048\u20132056 . Karl Cobbe, Chris Hesse, Jacob Hilton, and John Schulman. 2020. Leveraging procedural generation to benchmark reinforcement learning. In Proceedings of the 37th International Conference on Machine Learning. 2048\u20132056."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2016.2611524"},{"key":"e_1_2_1_31_1","volume-title":"Development of a robust cooperative adaptive cruise control with dynamic topology","author":"Cui Lian","year":"2021","unstructured":"Lian Cui , Zheng Chen , Aobo Wang , Jia Hu , and Byungkyu Brian Park . 2021. Development of a robust cooperative adaptive cruise control with dynamic topology . IEEE Transactions on Intelligent Transportation Systems ( 2021 ). Lian Cui, Zheng Chen, Aobo Wang, Jia Hu, and Byungkyu Brian Park. 2021. Development of a robust cooperative adaptive cruise control with dynamic topology. IEEE Transactions on Intelligent Transportation Systems (2021)."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2731901"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157527"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2994909"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/IConAC.2014.6935482"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2707140"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2787574"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1177\/0361198119847473"},{"key":"e_1_2_1_39_1","volume-title":"Lane change prediction with an echo state network and recurrent neural network in the urban area","author":"Griesbach Karoline","year":"2021","unstructured":"Karoline Griesbach , Matthias Beggiato , and Karl Heinz Hoffmann . 2021. Lane change prediction with an echo state network and recurrent neural network in the urban area . IEEE Transactions on Intelligent Transportation Systems ( 2021 ). Karoline Griesbach, Matthias Beggiato, and Karl Heinz Hoffmann. 2021. Lane change prediction with an echo state network and recurrent neural network in the urban area. IEEE Transactions on Intelligent Transportation Systems (2021)."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3056502"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294869"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.vehcom.2017.01.002"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2016.12.013"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2020.2974588"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556288.2557321"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.3390\/s16111834"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2013.0014"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2002.994814"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.vehcom.2021.100334"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOMSTD.001.1900053"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2018.8647989"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2011.061411.00019"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102464"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1800236"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2019.2894816"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102874"},{"key":"e_1_2_1_57_1","unstructured":"John Boaz Lee Ryan A. Rossi Sungchul Kim Nesreen K. Ahmed and Eunyee Koh. 2018. Attention models in graphs: A survey. arXiv:1807.07984.  John Boaz Lee Ryan A. Rossi Sungchul Kim Nesreen K. Ahmed and Eunyee Koh. 2018. Attention models in graphs: A survey. arXiv:1807.07984."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8486398"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2019.102033"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2018.04.006"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2749971"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2017.8123046"},{"key":"e_1_2_1_63_1","unstructured":"Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv:1707.01926.  Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv:1707.01926."},{"key":"e_1_2_1_64_1","first-page":"2475","article-title":"Emergency message broadcast method based on Huffman-like coding","volume":"54","author":"Libing Wu","year":"2017","unstructured":"Wu Libing , Fan Jing , Wang Jing , Nie Lei , and Wang Hao . 2017 . Emergency message broadcast method based on Huffman-like coding . Journal of Computer Research and Development 54 , 11 (2017), 2475 . Wu Libing, Fan Jing, Wang Jing, Nie Lei, and Wang Hao. 2017. Emergency message broadcast method based on Huffman-like coding. Journal of Computer Research and Development 54, 11 (2017), 2475.","journal-title":"Journal of Computer Research and Development"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.10.011"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/961268.961272"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2661962"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2015.2451156"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2018.10.002"},{"key":"e_1_2_1_70_1","volume-title":"Yu","author":"Liu Fanzhen","year":"2020","unstructured":"Fanzhen Liu , Shan Xue , Jia Wu , Chuan Zhou , Wenbin Hu , Cecile Paris , Surya Nepal , Jian Yang , and Philip S . Yu . 2020 . Deep learning for community detection: Progress, challenges and opportunities. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI-20), Christian Bessiere (Ed.). International Joint Conferences on Artificial Intelligence Organization , 4981\u20134987. Survey track. Fanzhen Liu, Shan Xue, Jia Wu, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Jian Yang, and Philip S. Yu. 2020. Deep learning for community detection: Progress, challenges and opportunities. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI-20), Christian Bessiere (Ed.). International Joint Conferences on Artificial Intelligence Organization, 4981\u20134987. Survey track."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2954159"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2936507"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2957520"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.11.026"},{"key":"e_1_2_1_75_1","unstructured":"Mingming Lu Kunfang Zhang Haiying Liu and Naixue Xiong. 2019. Graph hierarchical convolutional recurrent neural network (GHCRNN) for vehicle condition prediction. arXiv:1903.06261.  Mingming Lu Kunfang Zhang Haiying Liu and Naixue Xiong. 2019. Graph hierarchical convolutional recurrent neural network (GHCRNN) for vehicle condition prediction. arXiv:1903.06261."},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2967026"},{"key":"e_1_2_1_77_1","volume-title":"A survey on recent advances in vehicular network security, trust, and privacy","author":"Lu Zhaojun","year":"2018","unstructured":"Zhaojun Lu , Gang Qu , and Zhenglin Liu . 2018. A survey on recent advances in vehicular network security, trust, and privacy . IEEE Transactions on Intelligent Transportation Systems 99 ( 2018 ), 1\u201317. Zhaojun Lu, Gang Qu, and Zhenglin Liu. 2018. A survey on recent advances in vehicular network security, trust, and privacy. IEEE Transactions on Intelligent Transportation Systems99 (2018), 1\u201317."},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2021.102746"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2690577"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2808444"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173808"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2735628"},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.vehcom.2014.05.001"},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITST.2007.4295909"},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2017.2715719"},{"key":"e_1_2_1_86_1","volume-title":"et\u00a0al","author":"Mnih Volodymyr","year":"2015","unstructured":"Volodymyr Mnih , Koray Kavukcuoglu , David Silver , Andrei A. Rusu , Joel Veness , Marc G. Bellemare , Alex Graves , Martin Riedmiller , Andreas K. Fidjeland , Georg Ostrovski , et\u00a0al . 2015 . Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, et\u00a0al. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529."},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2013.11.012"},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2012.142"},{"key":"e_1_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2017.04.001"},{"key":"e_1_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2018.09.011"},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2018.1700389"},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2975048"},{"key":"e_1_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3029864"},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9149371"},{"key":"e_1_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2815009"},{"key":"e_1_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2017.03.006"},{"key":"e_1_2_1_97_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.2986252"},{"key":"e_1_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2918544"},{"key":"e_1_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2019.102481"},{"key":"e_1_2_1_100_1","doi-asserted-by":"publisher","DOI":"10.5555\/2608850.2608956"},{"key":"e_1_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2697041"},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.vehcom.2020.100231"},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2016.2630300"},{"key":"e_1_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2828651"},{"key":"e_1_2_1_105_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2020.2971628"},{"key":"e_1_2_1_106_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449822"},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC.2006.1696637"},{"key":"e_1_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155485"},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2954595"},{"key":"e_1_2_1_110_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2791627"},{"key":"e_1_2_1_111_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2018.12.056"},{"key":"e_1_2_1_112_1","doi-asserted-by":"publisher","DOI":"10.5815\/ijwmt.2017.03.04"},{"key":"e_1_2_1_113_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.04.010"},{"key":"e_1_2_1_114_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299886"},{"key":"e_1_2_1_115_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.12.026"},{"key":"e_1_2_1_116_1","volume-title":"Lin (Eds.)","volume":"33","author":"Wang Haibo","year":"2020","unstructured":"Haibo Wang , Chuan Zhou , Xin Chen , Jia Wu , Shirui Pan , and Jilong Wang . 2020 . Graph stochastic neural networks for semi-supervised learning. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H . Lin (Eds.) , Vol. 33 . Curran Associates, Inc. , 19839\u201319848. Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, and Jilong Wang. 2020. Graph stochastic neural networks for semi-supervised learning. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 19839\u201319848."},{"key":"e_1_2_1_117_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2888904"},{"key":"e_1_2_1_118_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2018.1700364"},{"key":"e_1_2_1_119_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2017.1700200"},{"key":"e_1_2_1_120_1","unstructured":"Xiaoyu Wang Cailian Chen Yang Min Jianping He Bo Yang and Yang Zhang. 2018. Efficient metropolitan traffic prediction based on graph recurrent neural network. arXiv:1811.00740.  Xiaoyu Wang Cailian Chen Yang Min Jianping He Bo Yang and Yang Zhang. 2018. Efficient metropolitan traffic prediction based on graph recurrent neural network. arXiv:1811.00740."},{"key":"e_1_2_1_121_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.2986688"},{"key":"e_1_2_1_122_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3003933"},{"key":"e_1_2_1_123_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2971001"},{"key":"e_1_2_1_124_1","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2009.090202"},{"key":"e_1_2_1_125_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2871606"},{"key":"e_1_2_1_126_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2013.6706818"},{"key":"e_1_2_1_127_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2013.6707028"},{"key":"e_1_2_1_128_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJCAT.2012.047164"},{"key":"e_1_2_1_129_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2327111"},{"key":"e_1_2_1_130_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.2297923"},{"key":"e_1_2_1_131_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2850299"},{"key":"e_1_2_1_132_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2997896"},{"key":"e_1_2_1_133_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.04.012"},{"key":"e_1_2_1_134_1","unstructured":"Yuankai Wu and Huachun Tan. 2016. Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv:1612.01022.  Yuankai Wu and Huachun Tan. 2016. Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv:1612.01022."},{"key":"e_1_2_1_135_1","volume-title":"Yu","author":"Wu Zonghan","year":"2019","unstructured":"Zonghan Wu , Shirui Pan , Fengwen Chen , Guodong Long , Chengqi Zhang , and Philip S . Yu . 2019 . A comprehensive survey on graph neural networks. arXiv:1901.00596. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2019. A comprehensive survey on graph neural networks. arXiv:1901.00596."},{"key":"e_1_2_1_136_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3006199"},{"key":"e_1_2_1_137_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2789466"},{"key":"e_1_2_1_138_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737470"},{"key":"e_1_2_1_139_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2903299"},{"key":"e_1_2_1_140_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-017-2085-8"},{"key":"e_1_2_1_141_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2905522"},{"key":"e_1_2_1_142_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3024860"},{"key":"e_1_2_1_143_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2912427"},{"key":"e_1_2_1_144_1","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence.","author":"Yao Huaxiu","year":"2018","unstructured":"Huaxiu Yao , Fei Wu , Jintao Ke , Xianfeng Tang , Yitian Jia , Siyu Lu , Pinghua Gong , Jieping Ye , and Zhenhui Li . 2018 . Deep multi-view spatial-temporal network for taxi demand prediction . In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence."},{"key":"e_1_2_1_145_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2933888"},{"key":"e_1_2_1_146_1","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2017.1600421"},{"key":"e_1_2_1_147_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304273"},{"key":"e_1_2_1_148_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.87"},{"key":"e_1_2_1_149_1","volume-title":"Pedram Kheirkhah Sangdeh, and Adnan Quadri","author":"Zeng Huacheng","year":"2021","unstructured":"Huacheng Zeng , Hossein Pirayesh , Pedram Kheirkhah Sangdeh, and Adnan Quadri . 2021 . VehCom: Delay-guaranteed message broadcast for large-scale vehicular networks. IEEE Transactions on Wireless Communications ( 2021). Huacheng Zeng, Hossein Pirayesh, Pedram Kheirkhah Sangdeh, and Adnan Quadri. 2021. VehCom: Delay-guaranteed message broadcast for large-scale vehicular networks. IEEE Transactions on Wireless Communications (2021)."},{"key":"e_1_2_1_150_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2872998"},{"key":"e_1_2_1_151_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2504513"},{"key":"e_1_2_1_152_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2016.2613869"},{"key":"e_1_2_1_153_1","volume-title":"GAAN: Gated attention networks for learning on large and spatiotemporal graphs. arXiv:1803.07294.","author":"Zhang Jiani","year":"2018","unstructured":"Jiani Zhang , Xingjian Shi , Junyuan Xie , Hao Ma , Irwin King , and Dit-Yan Yeung . 2018 . GAAN: Gated attention networks for learning on large and spatiotemporal graphs. arXiv:1803.07294. Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung. 2018. GAAN: Gated attention networks for learning on large and spatiotemporal graphs. arXiv:1803.07294."},{"key":"e_1_2_1_154_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298239.3298479"},{"key":"e_1_2_1_155_1","volume-title":"Artificial intelligence inspired transmission scheduling in cognitive vehicular communications and networks","author":"Zhang Ke","year":"2018","unstructured":"Ke Zhang , Supeng Leng , Xin Peng , Li Pan , Sabita Maharjan , and Yan Zhang . 2018. Artificial intelligence inspired transmission scheduling in cognitive vehicular communications and networks . IEEE internet of Things Journal 6, 2 ( 2018 ), 1987\u20131997. Ke Zhang, Supeng Leng, Xin Peng, Li Pan, Sabita Maharjan, and Yan Zhang. 2018. Artificial intelligence inspired transmission scheduling in cognitive vehicular communications and networks. IEEE internet of Things Journal 6, 2 (2018), 1987\u20131997."},{"key":"e_1_2_1_156_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2014.6848116"},{"key":"e_1_2_1_157_1","doi-asserted-by":"publisher","DOI":"10.5555\/3060832.3060946"},{"key":"e_1_2_1_158_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2847699"},{"key":"e_1_2_1_159_1","volume-title":"Cooperative adaptive cruise control with robustness against communication delay: An approach in the space domain","author":"Zhang Yu","year":"2020","unstructured":"Yu Zhang , Yu Bai , Meng Wang , and Jia Hu. 2020. Cooperative adaptive cruise control with robustness against communication delay: An approach in the space domain . IEEE Transactions on Intelligent Transportation Systems ( 2020 ). Yu Zhang, Yu Bai, Meng Wang, and Jia Hu. 2020. Cooperative adaptive cruise control with robustness against communication delay: An approach in the space domain. IEEE Transactions on Intelligent Transportation Systems (2020)."},{"key":"e_1_2_1_160_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.01.007"},{"key":"e_1_2_1_161_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340268"},{"key":"e_1_2_1_162_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2952605"},{"key":"e_1_2_1_163_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3021141"},{"key":"e_1_2_1_164_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2020.05.001"},{"key":"e_1_2_1_165_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2018.2844681"},{"key":"e_1_2_1_166_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2015.2440103"},{"key":"e_1_2_1_167_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112991"},{"key":"e_1_2_1_168_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2020.101966"},{"key":"e_1_2_1_169_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2961937"},{"key":"e_1_2_1_170_1","volume-title":"Lin (Eds.)","volume":"33","author":"Zhu Shichao","year":"2020","unstructured":"Shichao Zhu , Shirui Pan , Chuan Zhou , Jia Wu , Yanan Cao , and Bin Wang . 2020 . Graph geometry interaction learning. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H . Lin (Eds.) , Vol. 33 . Curran Associates, Inc., 7548\u20137558. Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, and Bin Wang. 2020. Graph geometry interaction learning. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 7548\u20137558."}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3451984","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3451984","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3451984","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:03:00Z","timestamp":1750197780000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3451984"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,8]]},"references-count":170,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,8,31]]}},"alternative-id":["10.1145\/3451984"],"URL":"https:\/\/doi.org\/10.1145\/3451984","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,8]]},"assertion":[{"value":"2019-11-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}