{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T09:43:48Z","timestamp":1773740628697,"version":"3.50.1"},"reference-count":269,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>Developing smart cities is vital for ensuring sustainable development and improving human well-being. One critical aspect of building smart cities is designing intelligent methods to address various decision-making problems that arise in urban areas. As machine learning techniques continue to advance rapidly, a growing body of research has been focused on utilizing these methods to achieve intelligent urban decision-making. In this survey, we conduct a systematic literature review on the application of machine learning methods in urban decision-making, with a focus on planning, transportation, and healthcare. First, we provide a taxonomy based on typical applications of machine learning methods for urban decision-making. We then present background knowledge on these tasks and the machine learning techniques that have been adopted to solve them. Next, we examine the challenges and advantages of applying machine learning in urban decision-making, including issues related to urban complexity, urban heterogeneity, and computational cost. Afterward and primarily, we elaborate on the existing machine learning methods that aim at solving urban decision-making tasks in planning, transportation, and healthcare, highlighting their strengths and limitations. Finally, we discuss open problems and the future directions of applying machine learning to enable intelligent urban decision-making, such as developing foundation models and combining reinforcement learning algorithms with human feedback. We hope this survey can help researchers in related fields understand the recent progress made in existing works, and inspire novel applications of machine learning in smart cities.<\/jats:p>","DOI":"10.1145\/3695986","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T10:55:18Z","timestamp":1732272918000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["A Survey of Machine Learning for Urban Decision Making: Applications in Planning, Transportation, and Healthcare"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1837-6730","authenticated-orcid":false,"given":"Yu","family":"Zheng","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7109-3588","authenticated-orcid":false,"given":"Qianyue","family":"Hao","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9175-6255","authenticated-orcid":false,"given":"Jingwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4916-2763","authenticated-orcid":false,"given":"Changzheng","family":"Gao","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5855-7093","authenticated-orcid":false,"given":"Jinwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0419-5514","authenticated-orcid":false,"given":"Depeng","family":"Jin","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5617-1659","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12516"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"112128","DOI":"10.1016\/j.rser.2022.112128","article-title":"Data-driven probabilistic machine learning in sustainable smart energy\/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm","volume":"160","author":"Ahmad Tanveer","year":"2022","unstructured":"Tanveer Ahmad, Rafal Madonski, Dongdong Zhang, Chao Huang, and Asad Mujeeb. 2022. Data-driven probabilistic machine learning in sustainable smart energy\/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews 160 (2022), 112128.","journal-title":"Renewable and Sustainable Energy Reviews"},{"issue":"1","key":"e_1_3_1_4_2","first-page":"1","article-title":"Designing bike networks using the concept of network clusters","volume":"3","author":"Akbarzadeh Meisam","year":"2018","unstructured":"Meisam Akbarzadeh, Syed Sina Mohri, and Ehsan Yazdian. 2018. Designing bike networks using the concept of network clusters. Applied Network Science 3, 1 (2018), 1\u201321.","journal-title":"Applied Network Science"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"5699","DOI":"10.32604\/cmc.2022.024431","article-title":"Deep reinforcement learning enabled smart city recycling waste object classification","volume":"71","author":"Duhayyim Mesfer Al","year":"2022","unstructured":"Mesfer Al Duhayyim, Taiseer Abdalla Elfadil Eisa, Fahd N. Al-Wesabi, Abdelzahir Abdelmaboud, Manar Ahmed Hamza, Abu Sarwar Zamani, Mohammed Rizwanullah, and Radwa Marzouk. 2022. Deep reinforcement learning enabled smart city recycling waste object classification. Computational Materials and Continua 71 (2022), 5699\u20135715.","journal-title":"Computational Materials and Continua"},{"issue":"3","key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1080\/00401706.1971.10488811","article-title":"Mean square error of prediction as a criterion for selecting variables","volume":"13","author":"Allen David M.","year":"1971","unstructured":"David M. Allen. 1971. Mean square error of prediction as a criterion for selecting variables. Technometrics 13, 3 (1971), 469\u2013475.","journal-title":"Technometrics"},{"key":"e_1_3_1_7_2","first-page":"699","volume-title":"Proceedings of the International Conference On Machine Learning","author":"Allen-Zhu Zeyuan","year":"2016","unstructured":"Zeyuan Allen-Zhu and Elad Hazan. 2016. Variance reduction for faster non-convex optimization. In Proceedings of the International Conference On Machine Learning. PMLR, 699\u2013707."},{"issue":"1","key":"e_1_3_1_8_2","first-page":"1","article-title":"Estimating worldwide effects of non-pharmaceutical interventions on COVID-19 incidence and population mobility patterns using a multiple-event study","volume":"11","author":"Askitas Nikolaos","year":"2021","unstructured":"Nikolaos Askitas, Konstantinos Tatsiramos, and Bertrand Verheyden. 2021. Estimating worldwide effects of non-pharmaceutical interventions on COVID-19 incidence and population mobility patterns using a multiple-event study. Scientific Reports 11, 1 (2021), 1\u201313.","journal-title":"Scientific Reports"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"100303","DOI":"10.1016\/j.cosrev.2020.100303","article-title":"Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions","volume":"38","author":"Atitallah Safa Ben","year":"2020","unstructured":"Safa Ben Atitallah, Maha Driss, Wadii Boulila, and Henda Ben Gh\u00e9zala. 2020. Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review 38 (2020), 100303.","journal-title":"Computer Science Review"},{"key":"e_1_3_1_10_2","unstructured":"Raghav Awasthi Keerat Kaur Guliani Arshita Bhatt Mehrab Singh Gill Aditya Nagori Ponnurangam Kumaraguru and Tavpritesh Sethi. 2020. VacSIM: Learning effective strategies for COVID-19 vaccine distribution using reinforcement learning. arXiv:2009.06602. Retrieved from https:\/\/arxiv.org\/abs\/2009.06602"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","first-page":"104540","DOI":"10.1016\/j.trc.2024.104540","article-title":"Personalized dynamic pricing policy for electric vehicles: Reinforcement learning approach","volume":"161","author":"Bae Sangjun","year":"2024","unstructured":"Sangjun Bae, Bal\u00e1zs Kulcs\u00e1r, and S\u00e9bastien Gros. 2024. Personalized dynamic pricing policy for electric vehicles: Reinforcement learning approach. Transportation Research Part C: Emerging Technologies 161 (2024), 104540.","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"e_1_3_1_12_2","first-page":"1377","volume-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Bao Jie","year":"2017","unstructured":"Jie Bao, Tianfu He, Sijie Ruan, Yanhua Li, and Yu Zheng. 2017. Planning bike lanes based on sharing-bikes\u2019 trajectories. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1377\u20131386."},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","first-page":"107922","DOI":"10.1016\/j.ress.2021.107922","article-title":"Multi-modal urban transit network design considering reliability: Multi-objective bi-level optimization","volume":"216","author":"Barahimi Amir Hossein","year":"2021","unstructured":"Amir Hossein Barahimi, Alireza Eydi, and Abdolah Aghaie. 2021. Multi-modal urban transit network design considering reliability: Multi-objective bi-level optimization. Reliability Engineering and System Safety 216 (2021), 107922.","journal-title":"Reliability Engineering and System Safety"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1007\/s10458-008-9062-9","article-title":"Opportunities for multiagent systems and multiagent reinforcement learning in traffic control","volume":"18","author":"Bazzan Ana LC","year":"2009","unstructured":"Ana LC Bazzan. 2009. Opportunities for multiagent systems and multiagent reinforcement learning in traffic control. Autonomous Agents and Multi-Agent Systems 18 (2009), 342\u2013375.","journal-title":"Autonomous Agents and Multi-Agent Systems"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa324"},{"issue":"5","key":"e_1_3_1_16_2","first-page":"1","article-title":"Application of reinforcement learning for effective vaccination strategies of coronavirus disease 2019 (COVID-19)","volume":"136","author":"Beigi Alireza","year":"2021","unstructured":"Alireza Beigi, Amin Yousefpour, Amirreza Yasami, JF G\u00f3mez-Aguilar, Stelios Bekiros, and Hadi Jahanshahi. 2021. Application of reinforcement learning for effective vaccination strategies of coronavirus disease 2019 (COVID-19). The European Physical Journal Plus 136, 5 (2021), 1\u201322.","journal-title":"The European Physical Journal Plus"},{"issue":"2","key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.ejor.2020.07.063","article-title":"Machine learning for combinatorial optimization: a methodological tour d\u2019horizon","volume":"290","author":"Bengio Yoshua","year":"2021","unstructured":"Yoshua Bengio, Andrea Lodi, and Antoine Prouvost. 2021. Machine learning for combinatorial optimization: a methodological tour d\u2019horizon. European Journal of Operational Research 290, 2 (2021), 405\u2013421.","journal-title":"European Journal of Operational Research"},{"issue":"1","key":"e_1_3_1_18_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s10107-003-0396-4","article-title":"Robust discrete optimization and network flows","volume":"98","author":"Bertsimas Dimitris","year":"2003","unstructured":"Dimitris Bertsimas and Melvyn Sim. 2003. Robust discrete optimization and network flows. Mathematical Programming 98, 1 (2003), 49\u201371.","journal-title":"Mathematical Programming"},{"key":"e_1_3_1_19_2","volume-title":"Introduction to linear optimization","author":"Bertsimas Dimitris","year":"1997","unstructured":"Dimitris Bertsimas and John N. Tsitsiklis. 1997. Introduction to linear optimization. Vol. 6. Athena Scientific Belmont, MA."},{"key":"e_1_3_1_20_2","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1145\/3351095.3375624","volume-title":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","author":"Bhatt Umang","year":"2020","unstructured":"Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, Jos\u00e9 MF Moura, and Peter Eckersley. 2020. Explainable machine learning in deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 648\u2013657."},{"key":"e_1_3_1_21_2","first-page":"31226","article-title":"Learning generalizable models for vehicle routing problems via knowledge distillation","volume":"35","author":"Bi Jieyi","year":"2022","unstructured":"Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, and Yeow Meng Chee. 2022. Learning generalizable models for vehicle routing problems via knowledge distillation. Advances in Neural Information Processing Systems 35 (2022), 31226\u201331238.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_22_2","first-page":"595","volume-title":"Proceedings of the CPAIOR","author":"Bonami Pierre","year":"2018","unstructured":"Pierre Bonami, Andrea Lodi, and Giulia Zarpellon. 2018. Learning a classification of mixed-integer quadratic programming problems. In Proceedings of the CPAIOR. Springer, 595\u2013604."},{"issue":"10","key":"e_1_3_1_23_2","first-page":"2065","article-title":"Reinforcement learning-based real-time control of coastal urban stormwater systems to mitigate flooding and improve water quality","volume":"8","author":"Bowes Benjamin D.","year":"2022","unstructured":"Benjamin D. Bowes, Cheng Wang, Mehmet B. Ercan, Teresa B. Culver, Peter A. Beling, and Jonathan L. Goodall. 2022. Reinforcement learning-based real-time control of coastal urban stormwater systems to mitigate flooding and improve water quality. Environmental Science: Water Research and Technology 8, 10 (2022), 2065\u20132086.","journal-title":"Environmental Science: Water Research and Technology"},{"key":"e_1_3_1_24_2","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex Optimization","author":"Boyd Stephen P.","year":"2004","unstructured":"Stephen P. Boyd and Lieven Vandenberghe. 2004. Convex Optimization. Cambridge University Press."},{"key":"e_1_3_1_25_2","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.cie.2015.12.007","article-title":"The vehicle routing problem: State of the art classification and review","volume":"99","author":"Braekers Kris","year":"2016","unstructured":"Kris Braekers, Katrien Ramaekers, and Inneke Van Nieuwenhuyse. 2016. The vehicle routing problem: State of the art classification and review. Computers and Industrial Engineering 99 (2016), 300\u2013313.","journal-title":"Computers and Industrial Engineering"},{"issue":"3","key":"e_1_3_1_26_2","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1561\/2200000050","article-title":"Convex optimization: Algorithms and complexity","volume":"8","author":"Bubeck S\u00e9bastien","year":"2015","unstructured":"S\u00e9bastien Bubeck et\u00a0al. 2015. Convex optimization: Algorithms and complexity. Foundations and Trends\u00ae in Machine Learning 8, 3-4 (2015), 231\u2013357.","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1613\/jair.1.12228","article-title":"A survey on the explainability of supervised machine learning","volume":"70","author":"Burkart Nadia","year":"2021","unstructured":"Nadia Burkart and Marco F. Huber. 2021. A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research 70 (2021), 245\u2013317.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"e_1_3_1_28_2","first-page":"1","article-title":"A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization","author":"Bushaj Sabah","year":"2022","unstructured":"Sabah Bushaj, Xuecheng Yin, Arjeta Beqiri, Donald Andrews, and \u0130 Esra B\u00fcy\u00fcktahtak\u0131n. 2022. A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization. Annals of Operations Research (2022), 1\u201333.","journal-title":"Annals of Operations Research"},{"issue":"2","key":"e_1_3_1_29_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2666003","article-title":"Rich vehicle routing problem: Survey","volume":"47","author":"Caceres-Cruz Jose","year":"2014","unstructured":"Jose Caceres-Cruz, Pol Arias, Daniel Guimarans, Daniel Riera, and Angel A. Juan. 2014. Rich vehicle routing problem: Survey. ACM Computing Surveys 47, 2 (2014), 1\u201328.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1145\/1143844.1143865","volume-title":"Proceedings of the 23rd International Conference on Machine Learning","author":"Caruana Rich","year":"2006","unstructured":"Rich Caruana and Alexandru Niculescu-Mizil. 2006. An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd International Conference on Machine Learning. 161\u2013168."},{"key":"e_1_3_1_31_2","unstructured":"Noe Casas. 2017. Deep deterministic policy gradient for urban traffic light control. arXiv:1703.09035. Retrieved from https:\/\/arxiv.org\/abs\/1703.09035"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.trpro.2022.02.007","article-title":"Bike network design: An approach based on micro-mobility geo-referenced data","volume":"62","author":"Castiglione Marisdea","year":"2022","unstructured":"Marisdea Castiglione, Rosita De Vincentis, Marialisa Nigro, and Vittorio Rega. 2022. Bike network design: An approach based on micro-mobility geo-referenced data. Transportation Research Procedia 62 (2022), 51\u201358.","journal-title":"Transportation Research Procedia"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1080\/08839514.2022.2031821"},{"issue":"12","key":"e_1_3_1_34_2","article-title":"Effects of population mobility on the COVID-19 spread in Brazil","volume":"16","author":"Chagas Eduarda TC","year":"2021","unstructured":"Eduarda TC Chagas, Pedro H. Barros, Isadora Cardoso-Pereira, Igor V. Ponte, Pablo Ximenes, Fl\u00e1vio Figueiredo, Fabricio Murai, Ana Paula Couto da Silva, Jussara M. Almeida, Antonio AF Loureiro, et\u00a0al. 2021. Effects of population mobility on the COVID-19 spread in Brazil. PloS One 16, 12 (2021).","journal-title":"PloS One"},{"key":"e_1_3_1_35_2","first-page":"3414","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Chen Chacha","year":"2020","unstructured":"Chacha Chen, Hua Wei, Nan Xu, Guanjie Zheng, Ming Yang, Yuanhao Xiong, Kai Xu, and Zhenhui Li. 2020. Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control. In Proceedings of the AAAI Conference on Artificial Intelligence. 3414\u20133421."},{"key":"e_1_3_1_36_2","doi-asserted-by":"crossref","first-page":"103272","DOI":"10.1016\/j.trc.2021.103272","article-title":"Spatial-temporal pricing for ride-sourcing platform with reinforcement learning","volume":"130","author":"Chen Chuqiao","year":"2021","unstructured":"Chuqiao Chen, Fugen Yao, Dong Mo, Jiangtao Zhu, and Xiqun Michael Chen. 2021. Spatial-temporal pricing for ride-sourcing platform with reinforcement learning. Transportation Research Part C: Emerging Technologies 130 (2021), 103272.","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"e_1_3_1_37_2","first-page":"61","volume-title":"Proceedings of the 2019 IEEE International Conference on Data Mining","author":"Chen Haipeng","year":"2019","unstructured":"Haipeng Chen, Yan Jiao, Zhiwei Qin, Xiaocheng Tang, Hao Li, Bo An, Hongtu Zhu, and Jieping Ye. 2019. InBEDE: Integrating contextual bandit with TD learning for joint pricing and dispatch of ride-hailing platforms. In Proceedings of the 2019 IEEE International Conference on Data Mining. IEEE, 61\u201370."},{"issue":"2","key":"e_1_3_1_38_2","first-page":"1","article-title":"Dynamic planning of bicycle stations in dockless public bicycle-sharing system using gated graph neural network","author":"Chen Jianguo","year":"2021","unstructured":"Jianguo Chen, Kenli Li, Keqin Li, Philip S. Yu, and Zeng Zeng. 2021. Dynamic planning of bicycle stations in dockless public bicycle-sharing system using gated graph neural network. ACM Transactions on Intelligent Systems and Technology12, 2 (2021), 1\u201322.","journal-title":"ACM Transactions on Intelligent Systems and Technology12"},{"key":"e_1_3_1_39_2","doi-asserted-by":"crossref","first-page":"130498","DOI":"10.1016\/j.chemosphere.2021.130498","article-title":"Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning","volume":"279","author":"Chen Kehua","year":"2021","unstructured":"Kehua Chen, Hongcheng Wang, Borja Valverde-P\u00e9rez, Siyuan Zhai, Luca Vezzaro, and Aijie Wang. 2021. Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning. Chemosphere 279 (2021), 130498.","journal-title":"Chemosphere"},{"issue":"5","key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/TETCI.2019.2907718","article-title":"A survey on an emerging area: Deep learning for smart city data","volume":"3","author":"Chen Qi","year":"2019","unstructured":"Qi Chen, Wei Wang, Fangyu Wu, Suparna De, Ruili Wang, Bailing Zhang, and Xin Huang. 2019. A survey on an emerging area: Deep learning for smart city data. IEEE Transactions on Emerging Topics in Computational Intelligence 3, 5 (2019), 392\u2013410.","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.enbuild.2018.03.051","article-title":"Optimal control of HVAC and window systems for natural ventilation through reinforcement learning","volume":"169","author":"Chen Yujiao","year":"2018","unstructured":"Yujiao Chen, Leslie K. Norford, Holly W. Samuelson, and Ali Malkawi. 2018. Optimal control of HVAC and window systems for natural ventilation through reinforcement learning. Energy and Buildings 169 (2018), 195\u2013205.","journal-title":"Energy and Buildings"},{"key":"e_1_3_1_42_2","article-title":"Deep reinforcement learning from human preferences","volume":"30","author":"Christiano Paul F.","year":"2017","unstructured":"Paul F. Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"3","key":"e_1_3_1_43_2","first-page":"1086","article-title":"Multi-agent deep reinforcement learning for large-scale traffic signal control","volume":"21","author":"Chu Tianshu","year":"2019","unstructured":"Tianshu Chu, Jie Wang, Lara Codec\u00e0, and Zhaojian Li. 2019. Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Transactions on Intelligent Transportation Systems 21, 3 (2019), 1086\u20131095.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_44_2","first-page":"1377","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Cui Jiaxu","year":"2019","unstructured":"Jiaxu Cui, Bo Yang, and Xia Hu. 2019. Deep Bayesian optimization on attributed graphs. In Proceedings of the AAAI Conference on Artificial Intelligence. 1377\u20131384."},{"key":"e_1_3_1_45_2","article-title":"Scalable and Parallel Deep Bayesian Optimization on Attributed Graphs","author":"Cui Jiaxu","year":"2020","unstructured":"Jiaxu Cui, Bo Yang, Bingyi Sun, Xia Hu, and Jiming Liu. 2020. Scalable and Parallel Deep Bayesian Optimization on Attributed Graphs. IEEE Transactions on Neural Networks and Learning Systems (2020).","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_1_46_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/978-3-031-00832-0_3","volume-title":"Proceedings of the High-Dimensional Optimization and Probability: With a View Towards Data Science","author":"Danilova Marina","year":"2022","unstructured":"Marina Danilova, Pavel Dvurechensky, Alexander Gasnikov, Eduard Gorbunov, Sergey Guminov, Dmitry Kamzolov, and Innokentiy Shibaev. 2022. Recent theoretical advances in non-convex optimization. In Proceedings of the High-Dimensional Optimization and Probability: With a View Towards Data Science. Springer, 79\u2013163."},{"key":"e_1_3_1_47_2","first-page":"1","volume-title":"Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems","author":"Darwish Ahmed","year":"2020","unstructured":"Ahmed Darwish, Momen Khalil, and Karim Badawi. 2020. Optimising public bus transit networks using deep reinforcement learning. In Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems. IEEE, 1\u20137."},{"issue":"2","key":"e_1_3_1_48_2","first-page":"1","article-title":"Machine learning for smart building applications: Review and taxonomy","author":"Djenouri Djamel","year":"2019","unstructured":"Djamel Djenouri, Roufaida Laidi, Youcef Djenouri, and Ilangko Balasingham. 2019. Machine learning for smart building applications: Review and taxonomy. ACM Computing Surveys52, 2 (2019), 1\u201336.","journal-title":"ACM Computing Surveys52"},{"issue":"2","key":"e_1_3_1_49_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1162\/106454699568728","article-title":"Ant algorithms for discrete optimization","volume":"5","author":"Dorigo Marco","year":"1999","unstructured":"Marco Dorigo, Gianni Di Caro, and Luca M Gambardella. 1999. Ant algorithms for discrete optimization. Artificial Life 5, 2 (1999), 137\u2013172.","journal-title":"Artificial Life"},{"issue":"8","key":"e_1_3_1_50_2","first-page":"e385\u2013e387","article-title":"The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK","volume":"2","author":"Drake Thomas M.","year":"2020","unstructured":"Thomas M. Drake, Annemarie B. Docherty, Thomas G. Weiser, Steven Yule, Aziz Sheikh, and Ewen M. Harrison. 2020. The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK. The Lancet Digital Health 2, 8 (2020), e385\u2013e387.","journal-title":"The Lancet Digital Health"},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"119065","DOI":"10.1016\/j.ins.2023.119065","article-title":"HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control","volume":"640","author":"Du Xinqi","year":"2023","unstructured":"Xinqi Du, Hechang Chen, Bo Yang, Cheng Long, and Songwei Zhao. 2023. HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control. Information Sciences 640 (2023), 119065.","journal-title":"Information Sciences"},{"key":"e_1_3_1_52_2","doi-asserted-by":"crossref","first-page":"3054","DOI":"10.1145\/3394486.3403356","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Duan Lu","year":"2020","unstructured":"Lu Duan, Yang Zhan, Haoyuan Hu, Yu Gong, Jiangwen Wei, Xiaodong Zhang, and Yinghui Xu. 2020. Efficiently solving the practical vehicle routing problem: A novel joint learning approach. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 3054\u20133063."},{"key":"e_1_3_1_53_2","doi-asserted-by":"crossref","first-page":"104174","DOI":"10.1016\/j.ijmedinf.2020.104174","article-title":"A simulation-optimisation approach for hospital beds allocation","volume":"141","author":"Oliveira BRP e","year":"2020","unstructured":"BRP e Oliveira, JA De Vasconcelos, JFF Almeida, and LR Pinto. 2020. A simulation-optimisation approach for hospital beds allocation. International Journal of Medical Informatics 141 (2020), 104174.","journal-title":"International Journal of Medical Informatics"},{"issue":"6509","key":"e_1_3_1_54_2","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1126\/science.abe2803","article-title":"An ethical framework for global vaccine allocation","volume":"369","author":"Emanuel Ezekiel J.","year":"2020","unstructured":"Ezekiel J. Emanuel, Govind Persad, Adam Kern, Allen Buchanan, C\u00e9cile Fabre, Daniel Halliday, Joseph Heath, Lisa Herzog, RJ Leland, Ephrem T. Lemango, et\u00a0al. 2020. An ethical framework for global vaccine allocation. Science 369, 6509 (2020), 1309\u20131312.","journal-title":"Science"},{"key":"e_1_3_1_55_2","doi-asserted-by":"crossref","first-page":"101284","DOI":"10.1016\/j.uclim.2022.101284","article-title":"Using Machine Learning to estimate the impact of different modes of transport and traffic restriction strategies on urban air quality","volume":"45","author":"Fabregat Alexandre","year":"2022","unstructured":"Alexandre Fabregat, Anton Vernet, Marc Vernet, Llu\u00eds V\u00e1zquez, and Josep A. Ferr\u00e9. 2022. Using Machine Learning to estimate the impact of different modes of transport and traffic restriction strategies on urban air quality. Urban Climate 45 (2022), 101284.","journal-title":"Urban Climate"},{"key":"e_1_3_1_56_2","doi-asserted-by":"crossref","first-page":"101853","DOI":"10.1016\/j.compenvurbsys.2022.101853","article-title":"A framework for human-computer interactive street network design based on a multi-stage deep learning approach","volume":"96","author":"Fang Zhou","year":"2022","unstructured":"Zhou Fang, Jiaxin Qi, Lubin Fan, Jianqiang Huang, Ying Jin, and Tianren Yang. 2022. A framework for human-computer interactive street network design based on a multi-stage deep learning approach. Computers, Environment and Urban Systems 96 (2022), 101853.","journal-title":"Computers, Environment and Urban Systems"},{"issue":"2","key":"e_1_3_1_57_2","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.ejor.2013.01.001","article-title":"A review of urban transportation network design problems","volume":"229","author":"Farahani Reza Zanjirani","year":"2013","unstructured":"Reza Zanjirani Farahani, Elnaz Miandoabchi, Wai Yuen Szeto, and Hannaneh Rashidi. 2013. A review of urban transportation network design problems. European Journal of Operational Research 229, 2 (2013), 281\u2013302.","journal-title":"European Journal of Operational Research"},{"key":"e_1_3_1_58_2","first-page":"118973","article-title":"The role of deep learning in urban water management: A critical review","author":"Fu Guangtao","year":"2022","unstructured":"Guangtao Fu, Yiwen Jin, Siao Sun, Zhiguo Yuan, and David Butler. 2022. The role of deep learning in urban water management: A critical review. Water Research (2022), 118973.","journal-title":"Water Research"},{"issue":"2","key":"e_1_3_1_59_2","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/0191-2615(84)90029-8","article-title":"A modified Frank-Wolfe algorithm for solving the traffic assignment problem","volume":"18","author":"Fukushima Masao","year":"1984","unstructured":"Masao Fukushima. 1984. A modified Frank-Wolfe algorithm for solving the traffic assignment problem. Transportation Research Part B: Methodological 18, 2 (1984), 169\u2013177.","journal-title":"Transportation Research Part B: Methodological"},{"key":"e_1_3_1_60_2","unstructured":"Wade Genders and Saiedeh Razavi. 2016. Using a deep reinforcement learning agent for traffic signal control. arXiv:1611.01142. Retrieved from https:\/\/arxiv.org\/abs\/1611.01142"},{"issue":"1","key":"e_1_3_1_61_2","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/MITS.2019.2962159","article-title":"A deep reinforcement learning approach to ride-sharing vehicle dispatching in autonomous mobility-on-demand systems","volume":"14","author":"Guo Ge","year":"2020","unstructured":"Ge Guo and Yangguang Xu. 2020. A deep reinforcement learning approach to ride-sharing vehicle dispatching in autonomous mobility-on-demand systems. IEEE Intelligent Transportation Systems Magazine 14, 1 (2020), 128\u2013140.","journal-title":"IEEE Intelligent Transportation Systems Magazine"},{"key":"e_1_3_1_62_2","first-page":"1","volume-title":"Proceedings of the 4th ACM Computer Science in Cars Symposium","author":"Haliem Marina","year":"2020","unstructured":"Marina Haliem, Ganapathy Mani, Vaneet Aggarwal, and Bharat Bhargava. 2020. A distributed model-free ride-sharing algorithm with pricing using deep reinforcement learning. In Proceedings of the 4th ACM Computer Science in Cars Symposium. 1\u201310."},{"key":"e_1_3_1_63_2","first-page":"2968","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Han Benjamin","year":"2022","unstructured":"Benjamin Han, Hyungjun Lee, and S\u00e9bastien Martin. 2022. Real-Time rideshare driver supply values using online reinforcement learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2968\u20132976."},{"key":"e_1_3_1_64_2","first-page":"4684","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Hao Qianyue","year":"2022","unstructured":"Qianyue Hao, Wenzhen Huang, Fengli Xu, Kun Tang, and Yong Li. 2022. Reinforcement learning enhances the experts: Large-scale COVID-19 vaccine allocation with multi-factor contact network. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 4684\u20134694."},{"key":"e_1_3_1_65_2","first-page":"2955","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Hao Qianyue","year":"2021","unstructured":"Qianyue Hao, Fengli Xu, Lin Chen, Pan Hui, and Yong Li. 2021. Hierarchical reinforcement learning for scarce medical resource allocation with imperfect information. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2955\u20132963."},{"issue":"1","key":"e_1_3_1_66_2","first-page":"1","article-title":"Hierarchical multi-agent model for reinforced medical resource allocation with imperfect information","volume":"14","author":"Hao Qianyue","year":"2022","unstructured":"Qianyue Hao, Fengli Xu, Lin Chen, Pan Hui, and Yong Li. 2022. Hierarchical multi-agent model for reinforced medical resource allocation with imperfect information. ACM Transactions on Intelligent Systems and Technology 14, 1 (2022), 1\u201327.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"issue":"1","key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"100","DOI":"10.2307\/2346830","article-title":"A K-means clustering algorithm","volume":"28","author":"Hartigan John A.","year":"1979","unstructured":"John A. Hartigan, Manchek A. Wong, et\u00a0al. 1979. A K-means clustering algorithm. Applied Statistics 28, 1 (1979), 100\u2013108.","journal-title":"Applied Statistics"},{"issue":"6495","key":"e_1_3_1_68_2","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1126\/science.abb6144","article-title":"Which interventions work best in a pandemic?","volume":"368","author":"Haushofer Johannes","year":"2020","unstructured":"Johannes Haushofer and C. Jessica E. Metcalf. 2020. Which interventions work best in a pandemic? Science 368, 6495 (2020), 1063\u20131065.","journal-title":"Science"},{"issue":"8","key":"e_1_3_1_69_2","first-page":"1529","article-title":"Interactive bike lane planning using sharing bikes\u2019 trajectories","volume":"32","author":"He Tianfu","year":"2019","unstructured":"Tianfu He, Jie Bao, Sijie Ruan, Ruiyuan Li, Yanhua Li, Hui He, and Yu Zheng. 2019. Interactive bike lane planning using sharing bikes\u2019 trajectories. IEEE Transactions on Knowledge and Data Engineering 32, 8 (2019), 1529\u20131542.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_70_2","doi-asserted-by":"crossref","first-page":"109440","DOI":"10.1016\/j.enbuild.2019.109440","article-title":"A deep reinforcement learning-based autonomous ventilation control system for smart indoor air quality management in a subway station","volume":"202","author":"Heo SungKu","year":"2019","unstructured":"SungKu Heo, KiJeon Nam, Jorge Loy-Benitez, Qian Li, SeungChul Lee, and ChangKyoo Yoo. 2019. A deep reinforcement learning-based autonomous ventilation control system for smart indoor air quality management in a subway station. Energy and Buildings 202 (2019), 109440.","journal-title":"Energy and Buildings"},{"key":"e_1_3_1_71_2","doi-asserted-by":"crossref","first-page":"106685","DOI":"10.1016\/j.knosys.2020.106685","article-title":"Explainability in deep reinforcement learning","volume":"214","author":"Heuillet Alexandre","year":"2021","unstructured":"Alexandre Heuillet, Fabien Couthouis, and Natalia D\u00edaz-Rodr\u00edguez. 2021. Explainability in deep reinforcement learning. Knowledge-Based Systems 214 (2021), 106685.","journal-title":"Knowledge-Based Systems"},{"key":"e_1_3_1_72_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hottung Andr\u00e9","year":"2020","unstructured":"Andr\u00e9 Hottung, Bhanu Bhandari, and Kevin Tierney. 2020. Learning a latent search space for routing problems using variational autoencoders. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_73_2","doi-asserted-by":"crossref","first-page":"105","DOI":"10.3934\/mbe.2020006","article-title":"Deep reinforcement learning based valve scheduling for pollution isolation in water distribution network","volume":"17","author":"Hu Chengyu","year":"2020","unstructured":"Chengyu Hu, Junyi Cai, Deze Zeng, Xuesong Yan, Wenyin Gong, and Ling Wang. 2020. Deep reinforcement learning based valve scheduling for pollution isolation in water distribution network. Mathematical Biosciences and Engineering 17 (2020), 105\u2013121.","journal-title":"Mathematical Biosciences and Engineering"},{"key":"e_1_3_1_74_2","doi-asserted-by":"crossref","first-page":"123611","DOI":"10.1016\/j.jclepro.2020.123611","article-title":"Novel leakage detection and water loss management of urban water supply network using multiscale neural networks","volume":"278","author":"Hu Xuan","year":"2021","unstructured":"Xuan Hu, Yongming Han, Bin Yu, Zhiqiang Geng, and Jinzhen Fan. 2021. Novel leakage detection and water loss management of urban water supply network using multiscale neural networks. Journal of Cleaner Production 278 (2021), 123611.","journal-title":"Journal of Cleaner Production"},{"key":"e_1_3_1_75_2","doi-asserted-by":"crossref","first-page":"102412","DOI":"10.1016\/j.ijdrr.2021.102412","article-title":"A systematic review of prediction methods for emergency management","volume":"62","author":"Huang Di","year":"2021","unstructured":"Di Huang, Shuaian Wang, and Zhiyuan Liu. 2021. A systematic review of prediction methods for emergency management. International Journal of Disaster Risk Reduction 62 (2021), 102412.","journal-title":"International Journal of Disaster Risk Reduction"},{"issue":"3","key":"e_1_3_1_76_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3474841","article-title":"Deep reinforcement learning-based trajectory pricing on ride-hailing platforms","volume":"13","author":"Huang Jianbin","year":"2022","unstructured":"Jianbin Huang, Longji Huang, Meijuan Liu, He Li, Qinglin Tan, Xiaoke Ma, Jiangtao Cui, and De-Shuang Huang. 2022. Deep reinforcement learning-based trajectory pricing on ride-hailing platforms. ACM Transactions on Intelligent Systems and Technology 13, 3 (2022), 1\u201319.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_3_1_77_2","article-title":"Multi-agent mix hierarchical deep reinforcement learning for large-scale fleet management","author":"Huang Xiaohui","year":"2023","unstructured":"Xiaohui Huang, Jiahao Ling, Xiaofei Yang, Xiong Zhang, and Kaiming Yang. 2023. Multi-agent mix hierarchical deep reinforcement learning for large-scale fleet management. IEEE Transactions on Intelligent Transportation Systems (2023).","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"12","key":"e_1_3_1_78_2","doi-asserted-by":"crossref","first-page":"23858","DOI":"10.1109\/TITS.2022.3196835","article-title":"A novel data-driven approach for solving the electric vehicle charging station location-routing problem","volume":"23","author":"Hung Ying-Chao","year":"2022","unstructured":"Ying-Chao Hung and George Michailidis. 2022. A novel data-driven approach for solving the electric vehicle charging station location-routing problem. IEEE Transactions on Intelligent Transportation Systems 23, 12 (2022), 23858\u201323868.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"10","key":"e_1_3_1_79_2","doi-asserted-by":"crossref","first-page":"3806","DOI":"10.1109\/TITS.2019.2909109","article-title":"Online vehicle routing with neural combinatorial optimization and deep reinforcement learning","volume":"20","author":"James JQ","year":"2019","unstructured":"JQ James, Wen Yu, and Jiatao Gu. 2019. Online vehicle routing with neural combinatorial optimization and deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems 20, 10 (2019), 3806\u20133817.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_80_2","doi-asserted-by":"crossref","first-page":"129973","DOI":"10.1016\/j.jhydrol.2023.129973","article-title":"Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning","volume":"624","author":"Jeung Minhyuk","year":"2023","unstructured":"Minhyuk Jeung, Jiyi Jang, Kwangsik Yoon, and Sang-Soo Baek. 2023. Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning. Journal of Hydrology 624 (2023), 129973.","journal-title":"Journal of Hydrology"},{"key":"e_1_3_1_81_2","article-title":"Ensemble-based deep reinforcement learning for vehicle routing problems under distribution shift","volume":"36","author":"Jiang Yuan","year":"2024","unstructured":"Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Wen Song, and Jie Zhang. 2024. Ensemble-based deep reinforcement learning for vehicle routing problems under distribution shift. Advances in Neural Information Processing Systems 36 (2024).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_82_2","first-page":"984","volume-title":"Proceedings of the Uncertainty in Artificial Intelligence","author":"Jiang Yuan","year":"2023","unstructured":"Yuan Jiang, Zhiguang Cao, Yaoxin Wu, and Jie Zhang. 2023. Multi-view graph contrastive learning for solving vehicle routing problems. In Proceedings of the Uncertainty in Artificial Intelligence. PMLR, 984\u2013994."},{"key":"e_1_3_1_83_2","first-page":"1983","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Jin Jiarui","year":"2019","unstructured":"Jiarui Jin, Ming Zhou, Weinan Zhang, Minne Li, Zilong Guo, Zhiwei Qin, Yan Jiao, Xiaocheng Tang, Chenxi Wang, Jun Wang, et\u00a0al. 2019. Coride: joint order dispatching and fleet management for multi-scale ride-hailing platforms. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1983\u20131992."},{"issue":"1","key":"e_1_3_1_84_2","first-page":"91","article-title":"A dynamic and deadline-oriented road pricing mechanism for urban traffic management","volume":"27","author":"Jin Jiahui","year":"2021","unstructured":"Jiahui Jin, Xiaoxuan Zhu, Biwei Wu, Jinghui Zhang, and Yuxiang Wang. 2021. A dynamic and deadline-oriented road pricing mechanism for urban traffic management. Tsinghua Science and Technology 27, 1 (2021), 91\u2013102.","journal-title":"Tsinghua Science and Technology"},{"key":"e_1_3_1_85_2","first-page":"8132","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Jin Yan","year":"2023","unstructured":"Yan Jin, Yuandong Ding, Xuanhao Pan, Kun He, Li Zhao, Tao Qin, Lei Song, and Jiang Bian. 2023. Pointerformer: Deep reinforced multi-pointer transformer for the traveling salesman problem. In Proceedings of the AAAI Conference on Artificial Intelligence. 8132\u20138140."},{"key":"e_1_3_1_86_2","unstructured":"Chaitanya K. Joshi Thomas Laurent and Xavier Bresson. 2019. An efficient graph convolutional network technique for the travelling salesman problem. arXiv:1906.01227. Retrieved from https:\/\/arxiv.org\/abs\/1906.01227"},{"issue":"5","key":"e_1_3_1_87_2","first-page":"2280","article-title":"Learning to delay in ride-sourcing systems: a multi-agent deep reinforcement learning framework","volume":"34","author":"Ke Jintao","year":"2020","unstructured":"Jintao Ke, Feng Xiao, Hai Yang, and Jieping Ye. 2020. Learning to delay in ride-sourcing systems: a multi-agent deep reinforcement learning framework. IEEE Transactions on Knowledge and Data Engineering 34, 5 (2020), 2280\u20132292.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_88_2","first-page":"1","article-title":"Optimising lockdown policies for epidemic control using reinforcement learning: An AI-driven control approach compatible with existing disease and network models","author":"Khadilkar Harshad","year":"2020","unstructured":"Harshad Khadilkar, Tanuja Ganu, and D Seetharam. 2020. Optimising lockdown policies for epidemic control using reinforcement learning: An AI-driven control approach compatible with existing disease and network models. Transactions of the Indian National Academy of Engineering (2020), 1\u20134.","journal-title":"Transactions of the Indian National Academy of Engineering"},{"key":"e_1_3_1_89_2","volume-title":"ICLR","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR."},{"issue":"1","key":"e_1_3_1_90_2","article-title":"Bankable permits for the control of environmental pollution","volume":"64","author":"Kling Catherine","year":"1997","unstructured":"Catherine Kling and Jonathan Rubin. 1997. Bankable permits for the control of environmental pollution. Journal of Public Economics 64, 1 (1997).","journal-title":"Journal of Public Economics"},{"key":"e_1_3_1_91_2","volume-title":"ICLR","author":"Kool Wouter","year":"2018","unstructured":"Wouter Kool, Herke van Hoof, and Max Welling. 2018. Attention, Learn to Solve Routing Problems!. In ICLR."},{"issue":"10","key":"e_1_3_1_92_2","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1038\/s41562-022-01383-x","article-title":"Human-centred mechanism design with Democratic AI","volume":"6","author":"Koster Raphael","year":"2022","unstructured":"Raphael Koster, Jan Balaguer, Andrea Tacchetti, Ari Weinstein, Tina Zhu, Oliver Hauser, Duncan Williams, Lucy Campbell-Gillingham, Phoebe Thacker, Matthew Botvinick, et\u00a0al. 2022. Human-centred mechanism design with Democratic AI. Nature Human Behaviour 6, 10 (2022), 1398\u20131407.","journal-title":"Nature Human Behaviour"},{"issue":"1","key":"e_1_3_1_93_2","first-page":"3","article-title":"Supervised machine learning: A review of classification techniques","volume":"160","author":"Kotsiantis Sotiris B.","year":"2007","unstructured":"Sotiris B. Kotsiantis, Ioannis Zaharakis, P Pintelas, et\u00a0al. 2007. Supervised machine learning: A review of classification techniques. Emerging artificial Intelligence Applications in Computer Engineering 160, 1 (2007), 3\u201324.","journal-title":"Emerging artificial Intelligence Applications in Computer Engineering"},{"key":"e_1_3_1_94_2","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1007\/978-3-319-59776-8_16","volume-title":"Integration of AI and OR Techniques in Constraint Programming: 14th International Conference, CPAIOR 2017, Padua, Italy, June 5-8, 2017, Proceedings 14","author":"Kruber Markus","year":"2017","unstructured":"Markus Kruber, Marco E. L\u00fcbbecke, and Axel Parmentier. 2017. Learning when to use a decomposition. In Integration of AI and OR Techniques in Constraint Programming: 14th International Conference, CPAIOR 2017, Padua, Italy, June 5-8, 2017, Proceedings 14. Springer, 202\u2013210."},{"issue":"5","key":"e_1_3_1_95_2","doi-asserted-by":"crossref","first-page":"e0251550","DOI":"10.1371\/journal.pone.0251550","article-title":"Deep reinforcement learning approaches for global public health strategies for COVID-19 pandemic","volume":"16","author":"Kwak Gloria Hyunjung","year":"2021","unstructured":"Gloria Hyunjung Kwak, Lowell Ling, and Pan Hui. 2021. Deep reinforcement learning approaches for global public health strategies for COVID-19 pandemic. PloS one 16, 5 (2021), e0251550.","journal-title":"PloS one"},{"key":"e_1_3_1_96_2","first-page":"21188","article-title":"Pomo: Policy optimization with multiple optima for reinforcement learning","volume":"33","author":"Kwon Yeong-Dae","year":"2020","unstructured":"Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, and Seungjai Min. 2020. Pomo: Policy optimization with multiple optima for reinforcement learning. Advances in Neural Information Processing Systems 33 (2020), 21188\u201321198.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"e_1_3_1_97_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/JPROC.2022.3223186","article-title":"Machine learning for emergency management: A survey and future outlook","volume":"111","author":"Kyrkou Christos","year":"2022","unstructured":"Christos Kyrkou, Panayiotis Kolios, Theocharis Theocharides, and Marios Polycarpou. 2022. Machine learning for emergency management: A survey and future outlook. Proc. IEEE 111, 1 (2022), 19\u201341.","journal-title":"Proc. IEEE"},{"key":"e_1_3_1_98_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Lastras-Monta\u00f1o Luis Alfonso","year":"2019","unstructured":"Luis Alfonso Lastras-Monta\u00f1o. 2019. Information theoretic lower bounds on negative log likelihood. In Proceedings of the International Conference on Learning Representations."},{"issue":"1","key":"e_1_3_1_99_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.3141\/1882-23","article-title":"Taxi dispatch system based on current demands and real-time traffic conditions","volume":"1882","author":"Lee Der-Horng","year":"2004","unstructured":"Der-Horng Lee, Hao Wang, Ruey Long Cheu, and Siew Hoon Teo. 2004. Taxi dispatch system based on current demands and real-time traffic conditions. Transportation Research Record 1882, 1 (2004), 193\u2013200.","journal-title":"Transportation Research Record"},{"key":"e_1_3_1_100_2","doi-asserted-by":"crossref","first-page":"102848","DOI":"10.1016\/j.trb.2023.102848","article-title":"Scalable reinforcement learning approaches for dynamic pricing in ride-hailing systems","volume":"178","author":"Lei Zengxiang","year":"2023","unstructured":"Zengxiang Lei and Satish V. Ukkusuri. 2023. Scalable reinforcement learning approaches for dynamic pricing in ride-hailing systems. Transportation Research Part B: Methodological 178 (2023), 102848.","journal-title":"Transportation Research Part B: Methodological"},{"key":"e_1_3_1_101_2","article-title":"Balancing efficiency and fairness in on-demand ridesourcing","volume":"32","author":"Lesmana Nixie S.","year":"2019","unstructured":"Nixie S. Lesmana, Xuan Zhang, and Xiaohui Bei. 2019. Balancing efficiency and fairness in on-demand ridesourcing. NeurIPS 32 (2019).","journal-title":"NeurIPS"},{"key":"e_1_3_1_102_2","doi-asserted-by":"crossref","first-page":"104620","DOI":"10.1016\/j.landusepol.2020.104620","article-title":"An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen, China","volume":"95","author":"Li Feixue","year":"2020","unstructured":"Feixue Li, Zhifeng Li, Honghua Chen, Zhenjie Chen, and Manchun Li. 2020. An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen, China. Land use Policy 95 (2020), 104620.","journal-title":"Land use Policy"},{"issue":"1","key":"e_1_3_1_103_2","doi-asserted-by":"crossref","first-page":"e0010101","DOI":"10.1371\/journal.pntd.0010101","article-title":"Effects of vaccination and non-pharmaceutical interventions and their lag times on the COVID-19 pandemic: Comparison of eight countries","volume":"16","author":"Li Hao","year":"2022","unstructured":"Hao Li, Luqi Wang, Mengxi Zhang, Yihan Lu, and Weibing Wang. 2022. Effects of vaccination and non-pharmaceutical interventions and their lag times on the COVID-19 pandemic: Comparison of eight countries. PLoS neglected tropical diseases 16, 1 (2022), e0010101.","journal-title":"PLoS neglected tropical diseases"},{"issue":"12","key":"e_1_3_1_104_2","first-page":"13572","article-title":"Deep reinforcement learning for solving the heterogeneous capacitated vehicle routing problem","volume":"52","author":"Li Jingwen","year":"2021","unstructured":"Jingwen Li, Yining Ma, Ruize Gao, Zhiguang Cao, Andrew Lim, Wen Song, and Jie Zhang. 2021. Deep reinforcement learning for solving the heterogeneous capacitated vehicle routing problem. IEEE Transactions on Cybernetics 52, 12 (2021), 13572\u201313585.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"3","key":"e_1_3_1_105_2","first-page":"2306","article-title":"Heterogeneous attentions for solving pickup and delivery problem via deep reinforcement learning","volume":"23","author":"Li Jingwen","year":"2021","unstructured":"Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, and Jie Zhang. 2021. Heterogeneous attentions for solving pickup and delivery problem via deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems 23, 3 (2021), 2306\u20132315.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"3","key":"e_1_3_1_106_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","article-title":"Federated learning: Challenges, methods, and future directions","volume":"37","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37, 3 (2020), 50\u201360.","journal-title":"IEEE Signal Processing Magazine"},{"key":"e_1_3_1_107_2","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s11869-020-00948-x","article-title":"A novel ensemble reinforcement learning gated unit model for daily PM2. 5 forecasting","volume":"14","author":"Li Yanfei","year":"2021","unstructured":"Yanfei Li, Zheyu Liu, and Hui Liu. 2021. A novel ensemble reinforcement learning gated unit model for daily PM2. 5 forecasting. Air Quality, Atmosphere and Health 14 (2021), 443\u2013453.","journal-title":"Air Quality, Atmosphere and Health"},{"key":"e_1_3_1_108_2","first-page":"1724","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Li Yexin","year":"2018","unstructured":"Yexin Li, Yu Zheng, and Qiang Yang. 2018. Dynamic bike reposition: A spatio-temporal reinforcement learning approach. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1724\u20131733."},{"key":"e_1_3_1_109_2","first-page":"510","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Li Yexin","year":"2019","unstructured":"Yexin Li, Yu Zheng, and Qiang Yang. 2019. Efficient and effective express via contextual cooperative reinforcement learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 510\u2013519."},{"key":"e_1_3_1_110_2","first-page":"805","volume-title":"Proceedings of the 29th ACM International Conference on Information and Knowledge Management","author":"Li Yexin","year":"2020","unstructured":"Yexin Li, Yu Zheng, and Qiang Yang. 2020. Cooperative multi-agent reinforcement learning in express system. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 805\u2013814."},{"issue":"9","key":"e_1_3_1_111_2","doi-asserted-by":"crossref","first-page":"4742","DOI":"10.1109\/TNNLS.2021.3060187","article-title":"An integrated reinforcement learning and centralized programming approach for online taxi dispatching","volume":"33","author":"Liang Enming","year":"2021","unstructured":"Enming Liang, Kexin Wen, William HK Lam, Agachai Sumalee, and Renxin Zhong. 2021. An integrated reinforcement learning and centralized programming approach for online taxi dispatching. IEEE Transactions on Neural Networks and Learning Systems 33, 9 (2021), 4742\u20134756.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"4","key":"e_1_3_1_112_2","doi-asserted-by":"crossref","first-page":"775","DOI":"10.3390\/electronics13040775","article-title":"Fairness-aware dynamic ride-hailing matching based on reinforcement learning","volume":"13","author":"Liang Yuan","year":"2024","unstructured":"Yuan Liang. 2024. Fairness-aware dynamic ride-hailing matching based on reinforcement learning. Electronics 13, 4 (2024), 775.","journal-title":"Electronics"},{"key":"e_1_3_1_113_2","doi-asserted-by":"crossref","first-page":"108710","DOI":"10.1016\/j.ecolind.2022.108710","article-title":"Land use optimization of rural production\u2013living\u2013ecological space at different scales based on the BP\u2013ANN and CLUE\u2013S models","volume":"137","author":"Liao Guitang","year":"2022","unstructured":"Guitang Liao, Peng He, Xuesong Gao, Zhengyu Lin, Chengyi Huang, Wei Zhou, Ouping Deng, Chenghua Xu, and Liangji Deng. 2022. Land use optimization of rural production\u2013living\u2013ecological space at different scales based on the BP\u2013ANN and CLUE\u2013S models. Ecological Indicators 137 (2022), 108710.","journal-title":"Ecological Indicators"},{"issue":"5","key":"e_1_3_1_114_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1145\/769800.769806","article-title":"Real-time taxi dispatching using global positioning systems","volume":"46","author":"Liao Ziqi","year":"2003","unstructured":"Ziqi Liao. 2003. Real-time taxi dispatching using global positioning systems. Communication of the ACM 46, 5 (2003), 81\u201383.","journal-title":"Communication of the ACM"},{"key":"e_1_3_1_115_2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/978-3-030-67670-4_10","volume-title":"Proceedings of the ECML PKDD.","volume":"12461","author":"Libin Pieter J. K.","year":"2020","unstructured":"Pieter J. K. Libin, Arno Moonens, Timothy Verstraeten, Fabian Perez-Sanjines, Niel Hens, Philippe Lemey, and Ann Now\u00e9. 2020. Deep reinforcement learning for large-scale epidemic control. In Proceedings of the ECML PKDD.Lecture Notes in Computer Science, Vol. 12461,Springer, 155\u2013170."},{"issue":"8","key":"e_1_3_1_116_2","doi-asserted-by":"crossref","first-page":"11528","DOI":"10.1109\/TITS.2021.3105232","article-title":"Deep reinforcement learning for the electric vehicle routing problem with time windows","volume":"23","author":"Lin Bo","year":"2021","unstructured":"Bo Lin, Bissan Ghaddar, and Jatin Nathwani. 2021. Deep reinforcement learning for the electric vehicle routing problem with time windows. IEEE Transactions on Intelligent Transportation Systems 23, 8 (2021), 11528\u201311538.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_117_2","first-page":"1774","volume-title":"Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining","author":"Lin Kaixiang","year":"2018","unstructured":"Kaixiang Lin, Renyu Zhao, Zhe Xu, and Jiayu Zhou. 2018. Efficient large-scale fleet management via multi-agent deep reinforcement learning. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining. 1774\u20131783."},{"key":"e_1_3_1_118_2","first-page":"4938","volume-title":"Proceedings of the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium","author":"Liu Fang","year":"2020","unstructured":"Fang Liu and Weilun Sun. 2020. Urban residential area sprawl simulation of metropolitan \u201cSuburbanization\u201d Trend in Beijing. In Proceedings of the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 4938\u20134942."},{"key":"e_1_3_1_119_2","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.jclepro.2019.05.388","article-title":"Data-driven intelligent location of public charging stations for electric vehicles","volume":"232","author":"Liu Qi","year":"2019","unstructured":"Qi Liu, Jiahao Liu, Weiwei Le, Zhaoxia Guo, and Zhenggang He. 2019. Data-driven intelligent location of public charging stations for electric vehicles. Journal of Cleaner Production 232 (2019), 531\u2013541.","journal-title":"Journal of Cleaner Production"},{"key":"e_1_3_1_120_2","first-page":"395","volume-title":"Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems","author":"Liu Tianjiao","year":"2022","unstructured":"Tianjiao Liu, Qiang Wang, Wenqi Zhang, and Chen Xu. 2022. CoRLNF: Joint spatio-temporal pricing and fleet management for ride-hailing platforms. In Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems. IEEE, 395\u2013401."},{"key":"e_1_3_1_121_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/3557990.3567586","volume-title":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs","author":"Liu Yu","year":"2022","unstructured":"Yu Liu, Jingtao Ding, and Yong Li. 2022. Developing knowledge graph based system for urban computing. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs. 3\u20137."},{"key":"e_1_3_1_122_2","first-page":"1","volume-title":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","author":"Liu Yu","year":"2023","unstructured":"Yu Liu, Jingtao Ding, and Yong Li. 2023. KnowSite: Leveraging urban knowledge graph for site selection. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems. 1\u201312."},{"key":"e_1_3_1_123_2","first-page":"199","volume-title":"Proceedings of the IJCAI","author":"Liu Yilin","year":"2023","unstructured":"Yilin Liu, Guiyang Luo, Quan Yuan, Jinglin Li, Lei Jin, Bo Chen, and Rui Pan. 2023. GPLight: Grouped multi-agent reinforcement learning for large-scale traffic signal control. In Proceedings of the IJCAI. 199\u2013207."},{"key":"e_1_3_1_124_2","doi-asserted-by":"crossref","first-page":"102694","DOI":"10.1016\/j.tre.2022.102694","article-title":"Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform","volume":"161","author":"Liu Yang","year":"2022","unstructured":"Yang Liu, Fanyou Wu, Cheng Lyu, Shen Li, Jieping Ye, and Xiaobo Qu. 2022. Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform. Transportation Research Part E: Logistics and Transportation Review 161 (2022), 102694.","journal-title":"Transportation Research Part E: Logistics and Transportation Review"},{"issue":"3","key":"e_1_3_1_125_2","first-page":"1996","article-title":"Context-aware taxi dispatching at city-scale using deep reinforcement learning","volume":"23","author":"Liu Zhidan","year":"2020","unstructured":"Zhidan Liu, Jiangzhou Li, and Kaishun Wu. 2020. Context-aware taxi dispatching at city-scale using deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems 23, 3 (2020), 1996\u20132009.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_126_2","doi-asserted-by":"crossref","first-page":"126848","DOI":"10.1016\/j.cej.2020.126848","article-title":"Applications of carbon dots in environmental pollution control: A review","volume":"406","author":"Long Caicheng","year":"2021","unstructured":"Caicheng Long, Zixin Jiang, Jingfang Shangguan, Taiping Qing, Peng Zhang, and Bo Feng. 2021. Applications of carbon dots in environmental pollution control: A review. Chemical Engineering Journal 406 (2021), 126848.","journal-title":"Chemical Engineering Journal"},{"key":"e_1_3_1_127_2","first-page":"4264","volume-title":"Proceedings of the 31st ACM International Conference on Information and Knowledge Management","author":"Lou Yican","year":"2022","unstructured":"Yican Lou, Jia Wu, and Yunchuan Ran. 2022. Meta-reinforcement learning for multiple traffic signals control. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management. 4264\u20134268."},{"key":"e_1_3_1_128_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Lu Hao","year":"2019","unstructured":"Hao Lu, Xingwen Zhang, and Shuang Yang. 2019. A learning-based iterative method for solving vehicle routing problems. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_129_2","first-page":"1283","volume-title":"Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems","author":"Lu Jiaming","year":"2024","unstructured":"Jiaming Lu, Jingqing Ruan, Haoyuan Jiang, Ziyue Li, Hangyu Mao, and Rui Zhao. 2024. DuaLight: Enhancing traffic signal control by leveraging scenario-specific and scenario-shared knowledge. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 1283\u20131291."},{"key":"e_1_3_1_130_2","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.trc.2019.02.006","article-title":"CB-Planner: A bus line planning framework for customized bus systems","volume":"101","author":"Lyu Yan","year":"2019","unstructured":"Yan Lyu, Chi-Yin Chow, Victor CS Lee, Joseph KY Ng, Yanhua Li, and Jia Zeng. 2019. CB-Planner: A bus line planning framework for customized bus systems. Transportation Research Part C: Emerging Technologies 101 (2019), 233\u2013253.","journal-title":"Transportation Research Part C: Emerging Technologies"},{"issue":"2","key":"e_1_3_1_131_2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1145\/3440968.3440975","article-title":"Spatio-temporal pricing for ridesharing platforms","volume":"18","author":"Ma Hongyao","year":"2020","unstructured":"Hongyao Ma, Fei Fang, and David C. Parkes. 2020. Spatio-temporal pricing for ridesharing platforms. ACM SIGecom Exchanges 18, 2 (2020), 53\u201357.","journal-title":"ACM SIGecom Exchanges"},{"issue":"2","key":"e_1_3_1_132_2","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1287\/opre.2021.2178","article-title":"Spatio-temporal pricing for ridesharing platforms","volume":"70","author":"Ma Hongyao","year":"2022","unstructured":"Hongyao Ma, Fei Fang, and David C. Parkes. 2022. Spatio-temporal pricing for ridesharing platforms. Operations Research 70, 2 (2022), 1025\u20131041.","journal-title":"Operations Research"},{"key":"e_1_3_1_133_2","volume-title":"Proceedings of the AAAI Workshop on Deep Learning on Graphs: Methodologies and Applications","author":"Ma Qiang","year":"2020","unstructured":"Qiang Ma, Suwen Ge, Danyang He, Darshan Thaker, and Iddo Drori. 2020. Combinatorial optimization by graph pointer networks and hierarchical reinforcement learning. In Proceedings of the AAAI Workshop on Deep Learning on Graphs: Methodologies and Applications."},{"key":"e_1_3_1_134_2","article-title":"Learning to search feasible and infeasible regions of routing problems with flexible neural k-opt","volume":"36","author":"Ma Yining","year":"2024","unstructured":"Yining Ma, Zhiguang Cao, and Yeow Meng Chee. 2024. Learning to search feasible and infeasible regions of routing problems with flexible neural k-opt. Advances in Neural Information Processing Systems 36 (2024).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_135_2","first-page":"23609","article-title":"A hierarchical reinforcement learning based optimization framework for large-scale dynamic pickup and delivery problems","volume":"34","author":"Ma Yi","year":"2021","unstructured":"Yi Ma, Xiaotian Hao, Jianye Hao, Jiawen Lu, Xing Liu, Tong Xialiang, Mingxuan Yuan, Zhigang Li, Jie Tang, and Zhaopeng Meng. 2021. A hierarchical reinforcement learning based optimization framework for large-scale dynamic pickup and delivery problems. Advances in Neural Information Processing Systems 34 (2021), 23609\u201323620.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_136_2","first-page":"23","volume-title":"Proceedings of the 31st International Joint Conference on Artificial Intelligence Vienna, Austria","author":"MA Yining","year":"2022","unstructured":"Yining MA, Jingwen LI, Zhiguang CAO, Wen SONG, Hongliang GUO, Yuejiao GONG, and Meng Chee CHEE. 2022. Efficient neural neighborhood search for pickup and delivery problems.(2022). In Proceedings of the 31st International Joint Conference on Artificial Intelligence Vienna, Austria. 23\u201329."},{"key":"e_1_3_1_137_2","first-page":"11096","article-title":"Learning to iteratively solve routing problems with dual-aspect collaborative transformer","volume":"34","author":"Ma Yining","year":"2021","unstructured":"Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, and Jing Tang. 2021. Learning to iteratively solve routing problems with dual-aspect collaborative transformer. Advances in Neural Information Processing Systems 34 (2021), 11096\u201311107.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_138_2","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1145\/1102351.1102422","volume-title":"Proceedings of the 22nd International Conference On Machine Learning","author":"Mannor Shie","year":"2005","unstructured":"Shie Mannor, Dori Peleg, and Reuven Rubinstein. 2005. The cross entropy method for classification. In Proceedings of the 22nd International Conference On Machine Learning. 561\u2013568."},{"issue":"1","key":"e_1_3_1_139_2","doi-asserted-by":"crossref","first-page":"3449","DOI":"10.1038\/s41467-021-23761-1","article-title":"Optimizing vaccine allocation for COVID-19 vaccines shows the potential role of single-dose vaccination","volume":"12","author":"Matrajt Laura","year":"2021","unstructured":"Laura Matrajt, Julia Eaton, Tiffany Leung, Dobromir Dimitrov, Joshua T Schiffer, David A Swan, and Holly Janes. 2021. Optimizing vaccine allocation for COVID-19 vaccines shows the potential role of single-dose vaccination. Nature Communications 12, 1 (2021), 3449.","journal-title":"Nature Communications"},{"issue":"7","key":"e_1_3_1_140_2","doi-asserted-by":"crossref","first-page":"e0008412","DOI":"10.1371\/journal.pntd.0008412","article-title":"Global resource shortages during COVID-19: Bad news for low-income countries","volume":"14","author":"McMahon Devon E.","year":"2020","unstructured":"Devon E. McMahon, Gregory A. Peters, Louise C. Ivers, and Esther E. Freeman. 2020. Global resource shortages during COVID-19: Bad news for low-income countries. PLoS Neglected Tropical Diseases 14, 7 (2020), e0008412.","journal-title":"PLoS Neglected Tropical Diseases"},{"key":"e_1_3_1_141_2","doi-asserted-by":"crossref","first-page":"111719","DOI":"10.1016\/j.rser.2021.111719","article-title":"Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options","volume":"153","author":"Metais Marc-Olivier","year":"2022","unstructured":"Marc-Olivier Metais, O Jouini, Yannick Perez, Ja\u00e2far Berrada, and Emilia Suomalainen. 2022. Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options. Renewable and Sustainable Energy Reviews 153 (2022), 111719.","journal-title":"Renewable and Sustainable Energy Reviews"},{"issue":"12","key":"e_1_3_1_142_2","article-title":"A small community model for the transmission of infectious diseases: Comparison of school closure as an intervention in individual-based models of an influenza pandemic","volume":"3","author":"Milne George J.","year":"2008","unstructured":"George J. Milne, Joel K Kelso, Heath A. Kelly, Simon T. Huband, and Jodie McVernon. 2008. A small community model for the transmission of infectious diseases: Comparison of school closure as an intervention in individual-based models of an influenza pandemic. PloS one 3, 12 (2008).","journal-title":"PloS one"},{"issue":"7540","key":"e_1_3_1_143_2","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","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\u2013533.","journal-title":"Nature"},{"issue":"7","key":"e_1_3_1_144_2","doi-asserted-by":"crossref","first-page":"e0254012","DOI":"10.1371\/journal.pone.0254012","article-title":"Early centralized isolation strategy for all confirmed cases of COVID-19 remains a core intervention to disrupt the pandemic spreading significantly","volume":"16","author":"Nam Nguyen Hai","year":"2021","unstructured":"Nguyen Hai Nam, Phan Thi My Tien, Le Van Truong, Toka Aziz El-Ramly, Pham Gia Anh, Nguyen Thi Hien, El Marabea Mahmoud, Mennatullah Mohamed Eltaras, Sarah Abd Elaziz Khader, Mohammed Salah Desokey, et\u00a0al. 2021. Early centralized isolation strategy for all confirmed cases of COVID-19 remains a core intervention to disrupt the pandemic spreading significantly. PloS One 16, 7 (2021), e0254012.","journal-title":"PloS One"},{"key":"e_1_3_1_145_2","article-title":"Reinforcement learning for solving the vehicle routing problem","volume":"31","author":"Nazari Mohammadreza","year":"2018","unstructured":"Mohammadreza Nazari, Afshin Oroojlooy, Lawrence Snyder, and Martin Tak\u00e1c. 2018. Reinforcement learning for solving the vehicle routing problem. Advances in Neural Information Processing Systems 31 (2018).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_146_2","first-page":"543","volume-title":"Doklady an USSR","author":"Nesterov Yurii","year":"1983","unstructured":"Yurii Nesterov. 1983. A method for unconstrained convex minimization problem with the rate of convergence O (1\/k^2). In Doklady an USSR 269, (1983), 543\u2013547."},{"key":"e_1_3_1_147_2","first-page":"323","volume-title":"Proceedings of the Advanced Materials Research","author":"Ni Yuan Min","year":"2014","unstructured":"Yuan Min Ni and Lei Li. 2014. Garbage incineration and intelligent fusion strategy of secondary pollution control. In Proceedings of the Advanced Materials Research. Trans Tech Publ, 323\u2013328."},{"issue":"1","key":"e_1_3_1_148_2","first-page":"1","article-title":"Exploring optimal control of epidemic spread using reinforcement learning","volume":"10","author":"Ohi Abu Quwsar","year":"2020","unstructured":"Abu Quwsar Ohi, MF Mridha, Muhammad Mostafa Monowar, Md Hamid, et\u00a0al. 2020. Exploring optimal control of epidemic spread using reinforcement learning. Scientific Reports 10, 1 (2020), 1\u201319.","journal-title":"Scientific Reports"},{"key":"e_1_3_1_149_2","doi-asserted-by":"crossref","first-page":"102640","DOI":"10.1016\/j.trc.2020.102640","article-title":"A data science framework for planning the growth of bicycle infrastructures","volume":"115","author":"Olmos Luis E.","year":"2020","unstructured":"Luis E. Olmos, Maria Sol Tadeo, Dimitris Vlachogiannis, Fahad Alhasoun, Xavier Espinet Alegre, Catalina Ochoa, Felipe Targa, and Marta C. Gonz\u00e1lez. 2020. A data science framework for planning the growth of bicycle infrastructures. Transportation Research Part C: Emerging Technologies 115 (2020), 102640.","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"e_1_3_1_150_2","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang Long","year":"2022","unstructured":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et\u00a0al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems 35 (2022), 27730\u201327744.","journal-title":"Advances in neural information processing systems"},{"issue":"1","key":"e_1_3_1_151_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1287\/stsy.2019.0037","article-title":"Dynamic matching for real-time ride sharing","volume":"10","author":"\u00d6zkan Erhun","year":"2020","unstructured":"Erhun \u00d6zkan and Amy R. Ward. 2020. Dynamic matching for real-time ride sharing. Stochastic Systems 10, 1 (2020), 29\u201370.","journal-title":"Stochastic Systems"},{"key":"e_1_3_1_152_2","doi-asserted-by":"crossref","first-page":"102676","DOI":"10.1016\/j.bspc.2021.102676","article-title":"Reinforcement learning-based decision support system for COVID-19","volume":"68","author":"Padmanabhan Regina","year":"2021","unstructured":"Regina Padmanabhan, Nader Meskin, Tamer Khattab, Mujahed Shraim, and Mohammed Al-Hitmi. 2021. Reinforcement learning-based decision support system for COVID-19. Biomedical Signal Processing and Control 68 (2021), 102676.","journal-title":"Biomedical Signal Processing and Control"},{"key":"e_1_3_1_153_2","first-page":"9345","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Pan Xuanhao","year":"2023","unstructured":"Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song, and Jiang Bian. 2023. H-tsp: Hierarchically solving the large-scale traveling salesman problem. In Proceedings of the AAAI Conference on Artificial Intelligence. 9345\u20139353."},{"issue":"6","key":"e_1_3_1_154_2","first-page":"e2012606\u2013e20126","article-title":"Variation in ventilator allocation guidelines by US state during the coronavirus disease 2019 pandemic: a systematic review","volume":"3","author":"Piscitello Gina M.","year":"2020","unstructured":"Gina M. Piscitello, Esha M. Kapania, William D. Miller, Juan C. Rojas, Mark Siegler, and William F. Parker. 2020. Variation in ventilator allocation guidelines by US state during the coronavirus disease 2019 pandemic: a systematic review. JAMA Network Open 3, 6 (2020), e2012606\u2013e2012606.","journal-title":"JAMA Network Open"},{"key":"e_1_3_1_155_2","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/978-3-030-57321-8_5","volume-title":"Proceedings of the International Cross-domain Conference for Machine Learning and Knowledge Extraction","author":"Puiutta Erika","year":"2020","unstructured":"Erika Puiutta and Eric MSP Veith. 2020. Explainable reinforcement learning: A survey. In Proceedings of the International Cross-domain Conference for Machine Learning and Knowledge Extraction. Springer, 77\u201395."},{"key":"e_1_3_1_156_2","doi-asserted-by":"crossref","first-page":"103239","DOI":"10.1016\/j.trc.2021.103239","article-title":"Optimizing matching time intervals for ride-hailing services using reinforcement learning","volume":"129","author":"Qin Guoyang","year":"2021","unstructured":"Guoyang Qin, Qi Luo, Yafeng Yin, Jian Sun, and Jieping Ye. 2021. Optimizing matching time intervals for ride-hailing services using reinforcement learning. Transportation Research Part C: Emerging Technologies 129 (2021), 103239.","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"e_1_3_1_157_2","first-page":"4578","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Qin Yiming","year":"2024","unstructured":"Yiming Qin, Nanxuan Zhao, Bin Sheng, and Rynson WH Lau. 2024. Text2City: One-stage text-driven urban layout regeneration. In Proceedings of the AAAI Conference on Artificial Intelligence. 4578\u20134586."},{"issue":"9","key":"e_1_3_1_158_2","first-page":"3351","article-title":"Clustering passenger trip data for the potential passenger investigation and line design of customized commuter bus","volume":"20","author":"Qiu Guo","year":"2018","unstructured":"Guo Qiu, Rui Song, Shiwei He, Wangtu Xu, and Min Jiang. 2018. Clustering passenger trip data for the potential passenger investigation and line design of customized commuter bus. IEEE Transactions on Intelligent Transportation Systems 20, 9 (2018), 3351\u20133360.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_159_2","first-page":"4568","volume-title":"Proceedings of the IJCAI","author":"Qiu Wei","year":"2019","unstructured":"Wei Qiu, Haipeng Chen, and Bo An. 2019. Dynamic electronic toll collection via multi-agent deep reinforcement learning with edge-based graph convolutional networks. In Proceedings of the IJCAI. 4568\u20134574."},{"key":"e_1_3_1_160_2","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10107-002-0323-0","article-title":"On the capacitated vehicle routing problem","volume":"94","author":"Ralphs Ted K.","year":"2003","unstructured":"Ted K. Ralphs, Leonid Kopman, William R. Pulleyblank, and Leslie E. Trotter. 2003. On the capacitated vehicle routing problem. Mathematical Programming 94 (2003), 343\u2013359.","journal-title":"Mathematical Programming"},{"key":"e_1_3_1_161_2","volume-title":"Environmental Pollution Control Engineering","author":"Rao CS","year":"2007","unstructured":"CS Rao. 2007. Environmental Pollution Control Engineering. New Age International."},{"key":"e_1_3_1_162_2","article-title":"A generalist agent","author":"Reed Scott","year":"2022","unstructured":"Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio G\u00f3mez Colmenarejo, Alexander Novikov, Gabriel Barth-maron, Mai Gim\u00e9nez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas. 2022. A generalist agent. Transactions on Machine Learning Research (2022). Retrieved from https:\/\/openreview.net\/forum?id=1ikK0kHjvjFeatured Certification, Outstanding Certification.","journal-title":"Transactions on Machine Learning Research"},{"key":"e_1_3_1_163_2","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.1145\/3292500.3330988","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Rizzo Stefano Giovanni","year":"2019","unstructured":"Stefano Giovanni Rizzo, Giovanna Vantini, and Sanjay Chawla. 2019. Time critic policy gradient methods for traffic signal control in complex and congested scenarios. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1654\u20131664."},{"key":"e_1_3_1_164_2","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1214\/aoms\/1177729586","article-title":"A stochastic approximation method","author":"Robbins Herbert","year":"1951","unstructured":"Herbert Robbins and Sutton Monro. 1951. A stochastic approximation method. The Annals of Mathematical Statistics (1951), 400\u2013407.","journal-title":"The Annals of Mathematical Statistics"},{"key":"e_1_3_1_165_2","volume-title":"Proceedings of the KDD","author":"Ruan Jingqing","year":"2024","unstructured":"Jingqing Ruan, Ziyue Li, Hua Wei, Haoyuan Jiang, Jiaming Lu, Xuantang Xiong, Hangyu Mao, and Rui Zhao. 2024. CoSLight: Co-optimizing collaborator selection and decision-making to enhance traffic signal control. In Proceedings of the KDD."},{"key":"e_1_3_1_166_2","first-page":"3838","volume-title":"Proceedings of the KDD","author":"Eshkevari Soheil Sadeghi","year":"2022","unstructured":"Soheil Sadeghi Eshkevari, Xiaocheng Tang, Zhiwei Qin, Jinhan Mei, Cheng Zhang, Qianying Meng, and Jia Xu. 2022. Reinforcement learning in the wild: Scalable RL dispatching algorithm deployed in ridehailing marketplace. In Proceedings of the KDD. 3838\u20133848."},{"key":"e_1_3_1_167_2","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.trpro.2021.01.041","article-title":"A reinforcement learning-based dynamic congestion pricing method for the morning commute problems","volume":"52","author":"Sato Kimihiro","year":"2021","unstructured":"Kimihiro Sato, Toru Seo, and Takashi Fuse. 2021. A reinforcement learning-based dynamic congestion pricing method for the morning commute problems. Transportation Research Procedia 52 (2021), 347\u2013355.","journal-title":"Transportation Research Procedia"},{"key":"e_1_3_1_168_2","article-title":"Optimizing control of waste incineration plants using reinforcement learning and digital twins","author":"Schlappa Martin","year":"2022","unstructured":"Martin Schlappa, Jonas Hegemann, and Stefan Spinler. 2022. Optimizing control of waste incineration plants using reinforcement learning and digital twins. IEEE Transactions on Engineering Management (2022).","journal-title":"IEEE Transactions on Engineering Management"},{"key":"e_1_3_1_169_2","first-page":"1889","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Schulman John","year":"2015","unstructured":"John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. 2015. Trust region policy optimization. In Proceedings of the International Conference on Machine Learning. PMLR, 1889\u20131897."},{"key":"e_1_3_1_170_2","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv:1707.06347. Retrieved from https:\/\/arxiv.org\/abs\/1707.06347"},{"key":"e_1_3_1_171_2","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.jss.2020.11.062","article-title":"A closer look into global hospital beds capacity and resource shortages during the COVID-19 pandemic","volume":"260","author":"Sen-Crowe Brendon","year":"2021","unstructured":"Brendon Sen-Crowe, Mason Sutherland, Mark McKenney, and Adel Elkbuli. 2021. A closer look into global hospital beds capacity and resource shortages during the COVID-19 pandemic. Journal of Surgical Research 260 (2021), 56\u201363.","journal-title":"Journal of Surgical Research"},{"issue":"3","key":"e_1_3_1_172_2","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/TASE.2009.2028577","article-title":"A collaborative multiagent taxi-dispatch system","volume":"7","author":"Seow Kiam Tian","year":"2009","unstructured":"Kiam Tian Seow, Nam Hai Dang, and Der-Horng Lee. 2009. A collaborative multiagent taxi-dispatch system. IEEE Transactions on Automation science and engineering 7, 3 (2009), 607\u2013616.","journal-title":"IEEE Transactions on Automation science and engineering"},{"key":"e_1_3_1_173_2","doi-asserted-by":"crossref","first-page":"110556","DOI":"10.1016\/j.buildenv.2023.110556","article-title":"Developing smart air purifier control strategies for better IAQ and energy efficiency using reinforcement learning","volume":"242","author":"Shang Wenzhe","year":"2023","unstructured":"Wenzhe Shang, Junjie Liu, Congcong Wang, Jiayu Li, and Xilei Dai. 2023. Developing smart air purifier control strategies for better IAQ and energy efficiency using reinforcement learning. Building and Environment 242 (2023), 110556.","journal-title":"Building and Environment"},{"key":"e_1_3_1_174_2","first-page":"1355","volume-title":"Proceedings of the 29th ACM International Conference on Information and Knowledge Management","author":"Shen Wei","year":"2020","unstructured":"Wei Shen, Xiaonan He, Chuheng Zhang, Qiang Ni, Wanchun Dou, and Yan Wang. 2020. Auxiliary-task based deep reinforcement learning for participant selection problem in mobile crowdsourcing. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 1355\u20131364."},{"key":"e_1_3_1_175_2","first-page":"3549","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Shi Dingyuan","year":"2021","unstructured":"Dingyuan Shi, Yongxin Tong, Zimu Zhou, Bingchen Song, Weifeng Lv, and Qiang Yang. 2021. Learning to assign: Towards fair task assignment in large-scale ride hailing. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3549\u20133557."},{"key":"e_1_3_1_176_2","doi-asserted-by":"crossref","first-page":"102738","DOI":"10.1016\/j.trc.2020.102738","article-title":"Reward design for driver repositioning using multi-agent reinforcement learning","volume":"119","author":"Shou Zhenyu","year":"2020","unstructured":"Zhenyu Shou and Xuan Di. 2020. Reward design for driver repositioning using multi-agent reinforcement learning. Transportation Research Part C: Emerging Technologies 119 (2020), 102738.","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"e_1_3_1_177_2","first-page":"387","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Silver David","year":"2014","unstructured":"David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller. 2014. Deterministic policy gradient algorithms. In Proceedings of the International Conference on Machine Learning. PMLR, 387\u2013395."},{"key":"e_1_3_1_178_2","doi-asserted-by":"crossref","first-page":"124757","DOI":"10.1016\/j.jhydrol.2020.124757","article-title":"Water and treated wastewater allocation in urban areas considering social attachments","volume":"585","author":"Skardi Mohammad Javad Emami","year":"2020","unstructured":"Mohammad Javad Emami Skardi, Reza Kerachian, and Ali Abdolhay. 2020. Water and treated wastewater allocation in urban areas considering social attachments. Journal of Hydrology 585 (2020), 124757.","journal-title":"Journal of Hydrology"},{"issue":"11","key":"e_1_3_1_179_2","doi-asserted-by":"crossref","first-page":"1818","DOI":"10.3390\/electronics9111818","article-title":"An application of reinforced learning-based dynamic pricing for improvement of ridesharing platform service in Seoul","volume":"9","author":"Song Jaein","year":"2020","unstructured":"Jaein Song, Yun Ji Cho, Min Hee Kang, and Kee Yeon Hwang. 2020. An application of reinforced learning-based dynamic pricing for improvement of ridesharing platform service in Seoul. Electronics 9, 11 (2020), 1818.","journal-title":"Electronics"},{"issue":"2","key":"e_1_3_1_180_2","article-title":"Big data and emergency management: concepts, methodologies, and applications","volume":"8","author":"Song Xuan","year":"2020","unstructured":"Xuan Song, Haoran Zhang, Rajendra Akerkar, Huawei Huang, Song Guo, Lei Zhong, Yusheng Ji, Andreas L. Opdahl, Hemant Purohit, Andr\u00e9 Skupin, et\u00a0al. 2020. Big data and emergency management: concepts, methodologies, and applications. IEEE Transactions on Big Data 8, 2 (2020).","journal-title":"IEEE Transactions on Big Data"},{"key":"e_1_3_1_181_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Song Xuan","year":"2014","unstructured":"Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, and Ryosuke Shibasaki. 2014. Intelligent system for urban emergency management during large-scale disaster. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"issue":"1","key":"e_1_3_1_182_2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s42949-021-00030-0","article-title":"Early pandemic COVID-19 case growth rates increase with city size","volume":"1","author":"Stier Andrew J.","year":"2021","unstructured":"Andrew J. Stier, Marc G Berman, and Lu\u00eds MA Bettencourt. 2021. Early pandemic COVID-19 case growth rates increase with city size. npj Urban Sustainability 1, 1 (2021), 31.","journal-title":"npj Urban Sustainability"},{"key":"e_1_3_1_183_2","unstructured":"Hongyuan Su Yu Zheng Jingtao Ding Depeng Jin and Yong Li. 2019. Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning. arXiv:2409.01588. Retrieved from https:\/\/arxiv.org\/abs\/2409.01588"},{"key":"e_1_3_1_184_2","first-page":"650","volume-title":"Companion Proceedings of the ACM on Web Conference 2024","author":"Su Hongyuan","year":"2024","unstructured":"Hongyuan Su, Yu Zheng, Jingtao Ding, Depeng Jin, and Yong Li. 2024. MetroGNN: Metro network expansion with reinforcement learning. In Companion Proceedings of the ACM on Web Conference 2024. 650\u2013653."},{"issue":"8","key":"e_1_3_1_185_2","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1007\/s13042-022-01516-8","article-title":"Learning to optimise general TSP instances","volume":"13","author":"Sultana Nasrin","year":"2022","unstructured":"Nasrin Sultana, Jeffrey Chan, Tabinda Sarwar, and AK Qin. 2022. Learning to optimise general TSP instances. International Journal of Machine Learning and Cybernetics 13, 8 (2022), 2213\u20132228.","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"e_1_3_1_186_2","article-title":"Optimizing long-term efficiency and fairness in ride-hailing under budget constraint via joint order dispatching and driver repositioning","author":"Sun Jiahui","year":"2024","unstructured":"Jiahui Sun, Haiming Jin, Zhaoxing Yang, and Lu Su. 2024. Optimizing long-term efficiency and fairness in ride-hailing under budget constraint via joint order dispatching and driver repositioning. IEEE Transactions on Knowledge and Data Engineering (2024).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_187_2","first-page":"3950","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Sun Jiahui","year":"2022","unstructured":"Jiahui Sun, Haiming Jin, Zhaoxing Yang, Lu Su, and Xinbing Wang. 2022. Optimizing long-term efficiency and fairness in ride-hailing via joint order dispatching and driver repositioning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3950\u20133960."},{"key":"e_1_3_1_188_2","article-title":"Policy gradient methods for reinforcement learning with function approximation","volume":"12","author":"Sutton Richard S.","year":"1999","unstructured":"Richard S. Sutton, David McAllester, Satinder Singh, and Yishay Mansour. 1999. Policy gradient methods for reinforcement learning with function approximation. Advances in Neural Information Processing Systems 12 (1999).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_189_2","doi-asserted-by":"crossref","first-page":"112405","DOI":"10.1016\/j.chaos.2022.112405","article-title":"A new ensemble spatio-temporal PM2. 5 prediction method based on graph attention recursive networks and reinforcement learning","volume":"162","author":"Tan Jing","year":"2022","unstructured":"Jing Tan, Hui Liu, Yanfei Li, Shi Yin, and Chengqing Yu. 2022. A new ensemble spatio-temporal PM2. 5 prediction method based on graph attention recursive networks and reinforcement learning. Chaos, Solitons and Fractals 162 (2022), 112405.","journal-title":"Chaos, Solitons and Fractals"},{"key":"e_1_3_1_190_2","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1145\/3292500.3330724","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Tang Xiaocheng","year":"2019","unstructured":"Xiaocheng Tang, Zhiwei Qin, Fan Zhang, Zhaodong Wang, Zhe Xu, Yintai Ma, Hongtu Zhu, and Jieping Ye. 2019. A deep value-network based approach for multi-driver order dispatching. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1780\u20131790."},{"key":"e_1_3_1_191_2","doi-asserted-by":"crossref","first-page":"3605","DOI":"10.1145\/3447548.3467096","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Tang Xiaocheng","year":"2021","unstructured":"Xiaocheng Tang, Fan Zhang, Zhiwei Qin, Yansheng Wang, Dingyuan Shi, Bingchen Song, Yongxin Tong, Hongtu Zhu, and Jieping Ye. 2021. Value function is all you need: A unified learning framework for ride hailing platforms. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3605\u20133615."},{"issue":"1","key":"e_1_3_1_192_2","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1038\/s41467-021-21385-z","article-title":"Harnessing peak transmission around symptom onset for non-pharmaceutical intervention and containment of the COVID-19 pandemic","volume":"12","author":"Tian Liang","year":"2021","unstructured":"Liang Tian, Xuefei Li, Fei Qi, Qian-Yuan Tang, Viola Tang, Jiang Liu, Zhiyuan Li, Xingye Cheng, Xuanxuan Li, Yingchen Shi, et\u00a0al. 2021. Harnessing peak transmission around symptom onset for non-pharmaceutical intervention and containment of the COVID-19 pandemic. Nature Communications 12, 1 (2021), 1147.","journal-title":"Nature Communications"},{"key":"e_1_3_1_193_2","doi-asserted-by":"crossref","first-page":"120912","DOI":"10.1016\/j.watres.2023.120912","article-title":"Improving the interpretability of deep reinforcement learning in urban drainage system operation","volume":"249","author":"Tian Wenchong","year":"2024","unstructured":"Wenchong Tian, Guangtao Fu, Kunlun Xin, Zhiyu Zhang, and Zhenliang Liao. 2024. Improving the interpretability of deep reinforcement learning in urban drainage system operation. Water Research 249 (2024), 120912.","journal-title":"Water Research"},{"issue":"10","key":"e_1_3_1_194_2","doi-asserted-by":"crossref","first-page":"9812","DOI":"10.1109\/TKDE.2021.3127077","article-title":"Combinatorial optimization meets reinforcement learning: Effective taxi order dispatching at large-scale","volume":"35","author":"Tong Yongxin","year":"2021","unstructured":"Yongxin Tong, Dingyuan Shi, Yi Xu, Weifeng Lv, Zhiwei Qin, and Xiaocheng Tang. 2021. Combinatorial optimization meets reinforcement learning: Effective taxi order dispatching at large-scale. IEEE Transactions on Knowledge and Data Engineering 35, 10 (2021), 9812\u20139823.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_195_2","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale et\u00a0al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv:2307.09288. Retrieved from https:\/\/arxiv.org\/abs\/2307.09288"},{"key":"e_1_3_1_196_2","first-page":"7","volume-title":"Proceedings of the 2022 7th International Conference on Data Science and Machine Learning Applications","author":"Trad Fouad","year":"2022","unstructured":"Fouad Trad and Salah El Falou. 2022. Towards using deep reinforcement learning for better COVID-19 vaccine distribution strategies. In Proceedings of the 2022 7th International Conference on Data Science and Machine Learning Applications. IEEE, 7\u201312."},{"issue":"2","key":"e_1_3_1_197_2","article-title":"Pollutants of textile industry wastewater and assessment of its discharge limits by water quality standards","volume":"7","author":"T\u00fcfekci Ne\u015fe","year":"2007","unstructured":"Ne\u015fe T\u00fcfekci, N\u00fcket Sivri, and \u0130smail Toroz. 2007. Pollutants of textile industry wastewater and assessment of its discharge limits by water quality standards. Turkish Journal of Fisheries and Aquatic Sciences 7, 2 (2007).","journal-title":"Turkish Journal of Fisheries and Aquatic Sciences"},{"key":"e_1_3_1_198_2","doi-asserted-by":"crossref","first-page":"102829","DOI":"10.1016\/j.trc.2020.102829","article-title":"Dynamic pricing and fleet management for electric autonomous mobility on demand systems","volume":"121","author":"Turan Berkay","year":"2020","unstructured":"Berkay Turan, Ramtin Pedarsani, and Mahnoosh Alizadeh. 2020. Dynamic pricing and fleet management for electric autonomous mobility on demand systems. Transportation Research Part C: Emerging Technologies 121 (2020), 102829.","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"e_1_3_1_199_2","volume-title":"Public health","author":"Turnock Bernard","year":"2012","unstructured":"Bernard Turnock. 2012. Public health. Jones and Bartlett Publishers."},{"key":"e_1_3_1_200_2","first-page":"21","article-title":"Coordinated deep reinforcement learners for traffic light control","volume":"8","author":"Pol Elise Van der","year":"2016","unstructured":"Elise Van der Pol and Frans A. Oliehoek. 2016. Coordinated deep reinforcement learners for traffic light control. Proceedings of Learning, Inference and Control of Multi-Agent Systems (at NIPS 2016) 8 (2016), 21\u201338.","journal-title":"Proceedings of Learning, Inference and Control of Multi-Agent Systems (at NIPS 2016)"},{"key":"e_1_3_1_201_2","article-title":"A data-driven optimization of large-scale dry port location using the hybrid approach of data mining and complex network theory","volume":"134","author":"Nguyen Truong Van","year":"2020","unstructured":"Truong Van Nguyen, Jie Zhang, Li Zhou, Meng Meng, and Yong He. 2020. A data-driven optimization of large-scale dry port location using the hybrid approach of data mining and complex network theory. Transportation Research Part E: Logistics and Transportation Review 134 (2020).","journal-title":"Transportation Research Part E: Logistics and Transportation Review"},{"key":"e_1_3_1_202_2","volume-title":"Environmental Pollution and Control","author":"Vesilind P. Aarne","year":"2013","unstructured":"P. Aarne Vesilind, J. Jeffrey Peirce, and Ruth F. Weiner. 2013. Environmental Pollution and Control. Elsevier."},{"key":"e_1_3_1_203_2","article-title":"Pointer networks","volume":"28","author":"Vinyals Oriol","year":"2015","unstructured":"Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer networks. Advances in neural information processing systems 28 (2015).","journal-title":"Advances in neural information processing systems"},{"key":"e_1_3_1_204_2","doi-asserted-by":"crossref","first-page":"3992","DOI":"10.1145\/3534678.3539154","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Wahl Leonie von","year":"2022","unstructured":"Leonie von Wahl, Nicolas Tempelmeier, Ashutosh Sao, and Elena Demidova. 2022. Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3992\u20134000."},{"key":"e_1_3_1_205_2","first-page":"1634","volume-title":"Proceedings of the KDD","author":"Wan Runzhe","year":"2021","unstructured":"Runzhe Wan, Xinyu Zhang, and Rui Song. 2021. Multi-objective model-based reinforcement learning for infectious disease control. In Proceedings of the KDD. Feida Zhu, Beng Chin Ooi, and Chunyan Miao (Eds.), ACM, 1634\u20131644."},{"key":"e_1_3_1_206_2","unstructured":"Arthur Wang and Berkay Turan. 2022. Multi-agent renforcement learning for dynamic pricing and fleet management in autonomous mobility-on-demand systems. International Foundation for Telemetering."},{"key":"e_1_3_1_207_2","doi-asserted-by":"crossref","first-page":"115655","DOI":"10.1109\/ACCESS.2020.3003750","article-title":"Risk-aware identification of highly suspected COVID-19 cases in social IoT: A joint graph theory and reinforcement learning approach","volume":"8","author":"Wang Bowen","year":"2020","unstructured":"Bowen Wang, Yanjing Sun, Trung Q. Duong, Long Dinh Nguyen, and Lajos Hanzo. 2020. Risk-aware identification of highly suspected COVID-19 cases in social IoT: A joint graph theory and reinforcement learning approach. IEEE Access 8 (2020), 115655\u2013115661.","journal-title":"IEEE Access"},{"issue":"1","key":"e_1_3_1_208_2","article-title":"Automated urban planning for reimagining city configuration via adversarial learning: quantification, generation, and evaluation","volume":"9","author":"Wang Dongjie","year":"2023","unstructured":"Dongjie Wang, Yanjie Fu, Kunpeng Liu, Fanglan Chen, Pengyang Wang, and Chang-Tien Lu. 2023. Automated urban planning for reimagining city configuration via adversarial learning: quantification, generation, and evaluation. ACM Transactions on Spatial Algorithms and Systems 9, 1 (2023).","journal-title":"ACM Transactions on Spatial Algorithms and Systems"},{"key":"e_1_3_1_209_2","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1145\/3397536.3422268","volume-title":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","author":"Wang Dongjie","year":"2020","unstructured":"Dongjie Wang, Yanjie Fu, Pengyang Wang, Bo Huang, and Chang-Tien Lu. 2020. Reimagining city configuration: Automated urban planning via adversarial learning. In Proceedings of the 28th International Conference on Advances in Geographic Information Systems. 497\u2013506."},{"key":"e_1_3_1_210_2","first-page":"4660","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Wang Dongjie","year":"2023","unstructured":"Dongjie Wang, Lingfei Wu, Denghui Zhang, Jingbo Zhou, Leilei Sun, and Yanjie Fu. 2023. Human-instructed deep hierarchical generative learning for automated urban planning. In Proceedings of the AAAI Conference on Artificial Intelligence. 4660\u20134667."},{"issue":"4","key":"e_1_3_1_211_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3494993","article-title":"Spatio-temporal urban knowledge graph enabled mobility prediction","volume":"5","author":"Wang Huandong","year":"2021","unstructured":"Huandong Wang, Qiaohong Yu, Yu Liu, Depeng Jin, and Yong Li. 2021. Spatio-temporal urban knowledge graph enabled mobility prediction. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 5, 4 (2021), 1\u201324.","journal-title":"Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies"},{"key":"e_1_3_1_212_2","doi-asserted-by":"crossref","first-page":"103925","DOI":"10.1016\/j.cities.2022.103925","article-title":"Unsupervised machine learning in urban studies: A systematic review of applications","volume":"129","author":"Wang Jing","year":"2022","unstructured":"Jing Wang and Filip Biljecki. 2022. Unsupervised machine learning in urban studies: A systematic review of applications. Cities 129 (2022), 103925.","journal-title":"Cities"},{"key":"e_1_3_1_213_2","first-page":"3271","article-title":"Multi-agent reinforcement learning for active voltage control on power distribution networks","volume":"34","author":"Wang Jianhong","year":"2021","unstructured":"Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, and Tim C. Green. 2021. Multi-agent reinforcement learning for active voltage control on power distribution networks. Advances in Neural Information Processing Systems 34 (2021), 3271\u20133284.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_214_2","first-page":"1","article-title":"A data-driven system for cooperative-bus route planning based on generative adversarial network and metric learning","author":"Wang Jiguang","year":"2022","unstructured":"Jiguang Wang, Yilun Zhang, Xinjie Xing, Yuanzhu Zhan, Wai Kin Victor Chan, and Sunil Tiwari. 2022. A data-driven system for cooperative-bus route planning based on generative adversarial network and metric learning. Annals of Operations Research (2022), 1\u201327.","journal-title":"Annals of Operations Research"},{"key":"e_1_3_1_215_2","first-page":"21453","article-title":"A bi-level framework for learning to solve combinatorial optimization on graphs","volume":"34","author":"Wang Runzhong","year":"2021","unstructured":"Runzhong Wang, Zhigang Hua, Gan Liu, Jiayi Zhang, Junchi Yan, Feng Qi, Shuang Yang, Jun Zhou, and Xiaokang Yang. 2021. A bi-level framework for learning to solve combinatorial optimization on graphs. Advances in Neural Information Processing Systems 34 (2021), 21453\u201321466.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_216_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Wang Runzhong","year":"2023","unstructured":"Runzhong Wang, Li Shen, Yiting Chen, Xiaokang Yang, Dacheng Tao, and Junchi Yan. 2023. Towards one-shot neural combinatorial solvers: Theoretical and empirical notes on the cardinality-constrained case. In Proceedings of the 11th International Conference on Learning Representations."},{"issue":"1","key":"e_1_3_1_217_2","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/TCYB.2020.3015811","article-title":"Large-scale traffic signal control using a novel multiagent reinforcement learning","volume":"51","author":"Wang Xiaoqiang","year":"2020","unstructured":"Xiaoqiang Wang, Liangjun Ke, Zhimin Qiao, and Xinghua Chai. 2020. Large-scale traffic signal control using a novel multiagent reinforcement learning. IEEE transactions on Cybernetics 51, 1 (2020), 174\u2013187.","journal-title":"IEEE transactions on Cybernetics"},{"key":"e_1_3_1_218_2","first-page":"323","volume-title":"Proceedings of the 2018 3rd International Conference on Advances in Materials, Mechatronics and Civil Engineering","author":"Wang Yunqian","year":"2018","unstructured":"Yunqian Wang. 2018. Optimization on fire station location selection for fire emergency vehicles using K-means algorithm. In Proceedings of the 2018 3rd International Conference on Advances in Materials, Mechatronics and Civil Engineering. Atlantis Press, 323\u2013333."},{"key":"e_1_3_1_219_2","first-page":"3545","volume-title":"Proceedings of the CIKM","author":"Wang Yiheng","year":"2022","unstructured":"Yiheng Wang, Hexi Jin, and Guanjie Zheng. 2022. CTRL: Cooperative Traffic Tolling via Reinforcement Learning. In Proceedings of the CIKM. 3545\u20133554."},{"key":"e_1_3_1_220_2","doi-asserted-by":"crossref","first-page":"4079","DOI":"10.1145\/3534678.3539047","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Wang Yansheng","year":"2022","unstructured":"Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, and Weifeng Lv. 2022. Fed-LTD: Towards cross-platform ride hailing via federated learning to dispatch. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4079\u20134089."},{"issue":"6","key":"e_1_3_1_221_2","doi-asserted-by":"crossref","first-page":"2228","DOI":"10.1109\/TMC.2020.3033782","article-title":"STMARL: A spatio-temporal multi-agent reinforcement learning approach for cooperative traffic light control","volume":"21","author":"Wang Yanan","year":"2020","unstructured":"Yanan Wang, Tong Xu, Xin Niu, Chang Tan, Enhong Chen, and Hui Xiong. 2020. STMARL: A spatio-temporal multi-agent reinforcement learning approach for cooperative traffic light control. IEEE Transactions on Mobile Computing 21, 6 (2020), 2228\u20132242.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_3_1_222_2","first-page":"617","volume-title":"Proceedings of the 2018 IEEE International Conference on Data Mining","author":"Wang Zhaodong","year":"2018","unstructured":"Zhaodong Wang, Zhiwei Qin, Xiaocheng Tang, Jieping Ye, and Hongtu Zhu. 2018. Deep reinforcement learning with knowledge transfer for online rides order dispatching. In Proceedings of the 2018 IEEE International Conference on Data Mining. IEEE, 617\u2013626."},{"issue":"3","key":"e_1_3_1_223_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/BF00992698","article-title":"Q-learning","volume":"8","author":"Watkins Christopher JCH","year":"1992","unstructured":"Christopher JCH Watkins and Peter Dayan. 1992. Q-learning. Machine Learning 8, 3 (1992), 279\u2013292.","journal-title":"Machine Learning"},{"key":"e_1_3_1_224_2","first-page":"1290","volume-title":"Proceedings of the KDD","author":"Wei Hua","year":"2019","unstructured":"Hua Wei, Chacha Chen, Guanjie Zheng, Kan Wu, Vikash Gayah, Kai Xu, and Zhenhui Li. 2019. Presslight: Learning max pressure control to coordinate traffic signals in arterial network. In Proceedings of the KDD. 1290\u20131298."},{"key":"e_1_3_1_225_2","first-page":"1913","volume-title":"Proceedings of the CIKM","author":"Wei Hua","year":"2019","unstructured":"Hua Wei, Nan Xu, Huichu Zhang, Guanjie Zheng, Xinshi Zang, Chacha Chen, Weinan Zhang, Yanmin Zhu, Kai Xu, and Zhenhui Li. 2019. Colight: Learning network-level cooperation for traffic signal control. In Proceedings of the CIKM. 1913\u20131922."},{"key":"e_1_3_1_226_2","article-title":"A reinforcement learning and prediction-based lookahead policy for vehicle repositioning in online ride-hailing systems","author":"Wei Honghao","year":"2023","unstructured":"Honghao Wei, Zixian Yang, Xin Liu, Zhiwei Qin, Xiaocheng Tang, and Lei Ying. 2023. A reinforcement learning and prediction-based lookahead policy for vehicle repositioning in online ride-hailing systems. IEEE Transactions on Intelligent Transportation Systems (2023).","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"2","key":"e_1_3_1_227_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1145\/3447556.3447565","article-title":"Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation","volume":"22","author":"Wei Hua","year":"2021","unstructured":"Hua Wei, Guanjie Zheng, Vikash Gayah, and Zhenhui Li. 2021. Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22, 2 (2021), 12\u201318.","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"e_1_3_1_228_2","first-page":"2496","volume-title":"Proceedings of the KDD","author":"Wei Hua","year":"2018","unstructured":"Hua Wei, Guanjie Zheng, Huaxiu Yao, and Zhenhui Li. 2018. Intellilight: A reinforcement learning approach for intelligent traffic light control. In Proceedings of the KDD. 2496\u20132505."},{"key":"e_1_3_1_229_2","first-page":"2646","volume-title":"Proceedings of the KDD","author":"Wei Yu","year":"2020","unstructured":"Yu Wei, Minjia Mao, Xi Zhao, Jianhua Zou, and Ping An. 2020. City metro network expansion with reinforcement learning. In Proceedings of the KDD. 2646\u20132656."},{"key":"e_1_3_1_230_2","doi-asserted-by":"crossref","DOI":"10.5772\/intechopen.103984","article-title":"On Realization of intelligent decision making in the real world: A foundation decision model perspective","volume":"2","author":"Wen Ying","year":"2023","unstructured":"Ying Wen, Ziyu Wan, Ming Zhou, Shufang Hou, Zhe Cao, Chenyang Le, Jingxiao Chen, Zheng Tian, Weinan Zhang, and Jun Wang. 2023. On Realization of intelligent decision making in the real world: A foundation decision model perspective. CAAI Artificial Intelligence Research 2 (2023).","journal-title":"CAAI Artificial Intelligence Research"},{"issue":"2","key":"e_1_3_1_231_2","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1109\/TVCG.2020.3030458","article-title":"Towards better bus networks: A visual analytics approach","volume":"27","author":"Weng Di","year":"2020","unstructured":"Di Weng, Chengbo Zheng, Zikun Deng, Mingze Ma, Jie Bao, Yu Zheng, Mingliang Xu, and Yingcai Wu. 2020. Towards better bus networks: A visual analytics approach. IEEE Transactions on Visualization and Computer Graphics 27, 2 (2020), 817\u2013827.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"issue":"1","key":"e_1_3_1_232_2","article-title":"Principal component analysis","volume":"2","author":"Wold Svante","year":"1987","unstructured":"Svante Wold, Kim Esbensen, and Paul Geladi. 1987. Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2, 1-3 (1987).","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"e_1_3_1_233_2","doi-asserted-by":"publisher","DOI":"10.3390\/info12020066"},{"key":"e_1_3_1_234_2","article-title":"Automated pricing agents in the on-demand economy","author":"Wu Tony","year":"2016","unstructured":"Tony Wu, Anthony D. Joseph, and Stuart J. Russell. 2016. Automated pricing agents in the on-demand economy. University of California at Berkeley: Berkeley, CA, USA (2016).","journal-title":"University of California at Berkeley: Berkeley, CA, USA"},{"issue":"8","key":"e_1_3_1_235_2","doi-asserted-by":"crossref","first-page":"8243","DOI":"10.1109\/TVT.2020.2997896","article-title":"Multi-agent deep reinforcement learning for urban traffic light control in vehicular networks","volume":"69","author":"Wu Tong","year":"2020","unstructured":"Tong Wu, Pan Zhou, Kai Liu, Yali Yuan, Xiumin Wang, Huawei Huang, and Dapeng Oliver Wu. 2020. Multi-agent deep reinforcement learning for urban traffic light control in vehicular networks. IEEE Transactions on Vehicular Technology 69, 8 (2020), 8243\u20138256.","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"9","key":"e_1_3_1_236_2","first-page":"5057","article-title":"Learning improvement heuristics for solving routing problems","volume":"33","author":"Wu Yaoxin","year":"2021","unstructured":"Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, and Andrew Lim. 2021. Learning improvement heuristics for solving routing problems. IEEE Transactions on Neural Networks and Learning Systems 33, 9 (2021), 5057\u20135069.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_1_237_2","first-page":"12042","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Xin Liang","year":"2021","unstructured":"Liang Xin, Wen Song, Zhiguang Cao, and Jie Zhang. 2021. Multi-decoder attention model with embedding glimpse for solving vehicle routing problems. In Proceedings of the AAAI Conference on Artificial Intelligence. 12042\u201312049."},{"key":"e_1_3_1_238_2","doi-asserted-by":"crossref","first-page":"102280","DOI":"10.1016\/j.omega.2020.102280","article-title":"Data-driven decision and analytics of collection and delivery point location problems for online retailers","volume":"100","author":"Xu Xianhao","year":"2021","unstructured":"Xianhao Xu, Yaohan Shen, Wanying Amanda Chen, Yeming Gong, and Hongwei Wang. 2021. Data-driven decision and analytics of collection and delivery point location problems for online retailers. Omega 100 (2021), 102280.","journal-title":"Omega"},{"key":"e_1_3_1_239_2","first-page":"905","volume-title":"Proceedings of the KDD","author":"Xu Zhe","year":"2018","unstructured":"Zhe Xu, Zhixin Li, Qingwen Guan, Dingshui Zhang, Qiang Li, Junxiao Nan, Chunyang Liu, Wei Bian, and Jieping Ye. 2018. Large-scale order dispatch in on-demand ride-hailing platforms: A learning and planning approach. In Proceedings of the KDD. 905\u2013913."},{"issue":"8","key":"e_1_3_1_240_2","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1002\/nav.21872","article-title":"Dynamic pricing and matching in ride-hailing platforms","volume":"67","author":"Yan Chiwei","year":"2020","unstructured":"Chiwei Yan, Helin Zhu, Nikita Korolko, and Dawn Woodard. 2020. Dynamic pricing and matching in ride-hailing platforms. Naval Research Logistics 67, 8 (2020), 705\u2013724.","journal-title":"Naval Research Logistics"},{"issue":"1","key":"e_1_3_1_241_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3141\/1857-10","article-title":"Optimal toll design in second-best link-based congestion pricing","volume":"1857","author":"Yang Hai","year":"2003","unstructured":"Hai Yang and Xiaoning Zhang. 2003. Optimal toll design in second-best link-based congestion pricing. Transportation Research Record 1857, 1 (2003), 85\u201392.","journal-title":"Transportation Research Record"},{"issue":"8","key":"e_1_3_1_242_2","article-title":"Reinforcement-learning-based tracking control of waste water treatment process under realistic system conditions and control performance requirements","volume":"52","author":"Yang Qinmin","year":"2021","unstructured":"Qinmin Yang, Weiwei Cao, Wenchao Meng, and Jennie Si. 2021. Reinforcement-learning-based tracking control of waste water treatment process under realistic system conditions and control performance requirements. IEEE Transactions on Systems, Man, and Cybernetics: Systems 52, 8 (2021).","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"e_1_3_1_243_2","first-page":"565","volume-title":"Proceedings of the 2020 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining","author":"Yang Zhou","year":"2020","unstructured":"Zhou Yang, Long Nguyen, Jiazhen Zhu, Zhenhe Pan, Jia Li, and Fang Jin. 2020. Coordinating disaster emergency response with heuristic reinforcement learning. In Proceedings of the 2020 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 565\u2013572."},{"key":"e_1_3_1_244_2","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.cie.2019.07.020","article-title":"Optimal mathematical programming for the warehouse location problem with Euclidean distance linearization","volume":"136","author":"You Meng","year":"2019","unstructured":"Meng You, Yiyong Xiao, Siyue Zhang, Pei Yang, and Shenghan Zhou. 2019. Optimal mathematical programming for the warehouse location problem with Euclidean distance linearization. Computers and Industrial Engineering 136 (2019), 70\u201379.","journal-title":"Computers and Industrial Engineering"},{"key":"e_1_3_1_245_2","first-page":"1","article-title":"An ensemble convolutional reinforcement learning gate network for metro station PM2. 5 forecasting","author":"Yu Chengqing","year":"2023","unstructured":"Chengqing Yu, Guangxi Yan, Kaiyi Ruan, Xinwei Liu, Chengming Yu, and Xiwei Mi. 2023. An ensemble convolutional reinforcement learning gate network for metro station PM2. 5 forecasting. Stochastic Environmental Research and Risk Assessment (2023), 1\u201316.","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"issue":"15","key":"e_1_3_1_246_2","doi-asserted-by":"crossref","first-page":"12046","DOI":"10.1109\/JIOT.2021.3078462","article-title":"A review of deep reinforcement learning for smart building energy management","volume":"8","author":"Yu Liang","year":"2021","unstructured":"Liang Yu, Shuqi Qin, Meng Zhang, Chao Shen, Tao Jiang, and Xiaohong Guan. 2021. A review of deep reinforcement learning for smart building energy management. IEEE Internet of Things Journal 8, 15 (2021), 12046\u201312063.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_1_247_2","volume-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Yuan Yuan","year":"2024","unstructured":"Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, and Yong Li. 2024. UniST: A prompt-empowered universal model for urban spatio-temporal prediction. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining."},{"key":"e_1_3_1_248_2","volume-title":"Proceedings of the AAAI","author":"Zang Xinshi","year":"2020","unstructured":"Xinshi Zang, Huaxiu Yao, Guanjie Zheng, Nan Xu, Kai Xu, and Zhenhui Li. 2020. Metalight: Value-based meta-reinforcement learning for traffic signal control. In Proceedings of the AAAI."},{"key":"e_1_3_1_249_2","first-page":"401","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zhang Hongbo","year":"2024","unstructured":"Hongbo Zhang, Guang Wang, Xu Wang, Zhengyang Zhou, Chen Zhang, Zheng Dong, and Yang Wang. 2024. NondBREM: Nondeterministic offline reinforcement learning for large-scale order dispatching. In Proceedings of the AAAI Conference on Artificial Intelligence. 401\u2013409."},{"key":"e_1_3_1_250_2","first-page":"1","volume-title":"Proceedings of the 30th International Conference on Advances in Geographic Information Systems","author":"Zhang Jun","year":"2022","unstructured":"Jun Zhang, Depeng Jin, and Yong Li. 2022. Mirage: An efficient and extensible city simulation framework (systems paper). In Proceedings of the 30th International Conference on Advances in Geographic Information Systems. 1\u20134."},{"key":"e_1_3_1_251_2","doi-asserted-by":"crossref","first-page":"102861","DOI":"10.1016\/j.trc.2020.102861","article-title":"Multi-vehicle routing problems with soft time windows: A multi-agent reinforcement learning approach","volume":"121","author":"Zhang Ke","year":"2020","unstructured":"Ke Zhang, Fang He, Zhengchao Zhang, Xi Lin, and Meng Li. 2020. Multi-vehicle routing problems with soft time windows: A multi-agent reinforcement learning approach. Transportation Research Part C: Emerging Technologies 121 (2020), 102861.","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"e_1_3_1_252_2","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.1145\/3097983.3098138","volume-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Zhang Lingyu","year":"2017","unstructured":"Lingyu Zhang, Tao Hu, Yue Min, Guobin Wu, Junying Zhang, Pengcheng Feng, Pinghua Gong, and Jieping Ye. 2017. A taxi order dispatch model based on combinatorial optimization. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2151\u20132159."},{"issue":"2","key":"e_1_3_1_253_2","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s12351-023-00756-y","article-title":"Bi-objective bi-level optimization for integrating lane-level closure and reversal in redesigning transportation networks","volume":"23","author":"Zhang Qiang","year":"2023","unstructured":"Qiang Zhang, Shi Qiang Liu, and Andrea D\u2019Ariano. 2023. Bi-objective bi-level optimization for integrating lane-level closure and reversal in redesigning transportation networks. Operational Research 23, 2 (2023), 23.","journal-title":"Operational Research"},{"key":"e_1_3_1_254_2","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1145\/3534678.3539416","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Zhang Weijia","year":"2022","unstructured":"Weijia Zhang, Hao Liu, Jindong Han, Yong Ge, and Hui Xiong. 2022. Multi-agent graph convolutional reinforcement learning for dynamic electric vehicle charging pricing. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2471\u20132481."},{"key":"e_1_3_1_255_2","article-title":"Electric fence planning for dockless bike-sharing services","volume":"206","author":"Zhang Yongping","year":"2019","unstructured":"Yongping Zhang, Diao Lin, and Zhifu Mi. 2019. Electric fence planning for dockless bike-sharing services. Journal of Cleaner Production 206 (2019).","journal-title":"Journal of Cleaner Production"},{"key":"e_1_3_1_256_2","first-page":"737","volume-title":"Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence","author":"Zhao Wenshuai","year":"2020","unstructured":"Wenshuai Zhao, Jorge Pe\u00f1a Queralta, and Tomi Westerlund. 2020. Sim-to-real transfer in deep reinforcement learning for robotics: a survey. In Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence. IEEE, 737\u2013744."},{"key":"e_1_3_1_257_2","first-page":"1","article-title":"Deep Q networks-based optimization of emergency resource scheduling for urban public health events","author":"Zhao Xianli","year":"2022","unstructured":"Xianli Zhao and Guixin Wang. 2022. Deep Q networks-based optimization of emergency resource scheduling for urban public health events. Neural Computing and Applications (2022), 1\u201310.","journal-title":"Neural Computing and Applications"},{"issue":"3","key":"e_1_3_1_258_2","first-page":"1","article-title":"Supply-demand-aware deep reinforcement learning for dynamic fleet management","author":"Zheng Bolong","year":"2022","unstructured":"Bolong Zheng, Lingfeng Ming, Qi Hu, Zhipeng L\u00fc, Guanfeng Liu, and Xiaofang Zhou. 2022. Supply-demand-aware deep reinforcement learning for dynamic fleet management. ACM Transactions on Intelligent Systems and Technology13, 3 (2022), 1\u201319.","journal-title":"ACM Transactions on Intelligent Systems and Technology13"},{"key":"e_1_3_1_259_2","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1145\/3357384.3357900","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Zheng Guanjie","year":"2019","unstructured":"Guanjie Zheng, Yuanhao Xiong, Xinshi Zang, Jie Feng, Hua Wei, Huichu Zhang, Yong Li, Kai Xu, and Zhenhui Li. 2019. Learning phase competition for traffic signal control. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1963\u20131972."},{"issue":"9","key":"e_1_3_1_260_2","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1038\/s43588-023-00503-5","article-title":"Spatial planning of urban communities via deep reinforcement learning","volume":"3","author":"Zheng Yu","year":"2023","unstructured":"Yu Zheng, Yuming Lin, Liang Zhao, Tinghai Wu, Depeng Jin, and Yong Li. 2023. Spatial planning of urban communities via deep reinforcement learning. Nature Computational Science 3, 9 (2023), 748\u2013762.","journal-title":"Nature Computational Science"},{"key":"e_1_3_1_261_2","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1145\/3580305.3599901","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Zheng Yu","year":"2023","unstructured":"Yu Zheng, Hongyuan Su, Jingtao Ding, Depeng Jin, and Yong Li. 2023. Road planning for slums via deep reinforcement learning. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 5695\u20135706."},{"key":"e_1_3_1_262_2","unstructured":"Zhu Zhongming Lu Linong Yao Xiaona Liu Wei et\u00a0al. 2020. World Cities Report 2020: The value of sustainable urbanization. (2020)."},{"key":"e_1_3_1_263_2","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.trb.2014.11.009","article-title":"A trial-and-error congestion pricing scheme for networks with elastic demand and link capacity constraints","volume":"72","author":"Zhou Bojian","year":"2015","unstructured":"Bojian Zhou, Michiel Bliemer, Hai Yang, and Jie He. 2015. A trial-and-error congestion pricing scheme for networks with elastic demand and link capacity constraints. Transportation Research Part B: Methodological 72 (2015), 77\u201392.","journal-title":"Transportation Research Part B: Methodological"},{"key":"e_1_3_1_264_2","first-page":"42769","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhou Jianan","year":"2023","unstructured":"Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, and Jie Zhang. 2023. Towards omni-generalizable neural methods for vehicle routing problems. In Proceedings of the International Conference on Machine Learning. PMLR, 42769\u201342789."},{"key":"e_1_3_1_265_2","first-page":"2645","volume-title":"Proceedings of the CIKM","author":"Zhou Ming","year":"2019","unstructured":"Ming Zhou, Jiarui Jin, Weinan Zhang, Zhiwei Qin, Yan Jiao, Chenxi Wang, Guobin Wu, Yong Yu, and Jieping Ye. 2019. Multi-agent reinforcement learning for order-dispatching via order-vehicle distribution matching. In Proceedings of the CIKM. 2645\u20132653."},{"issue":"3","key":"e_1_3_1_266_2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.jnlssr.2021.08.004","article-title":"Multi-sensing paradigm based urban air quality monitoring and hazardous gas source analyzing: a review","volume":"2","author":"Zhu Zhengqiu","year":"2021","unstructured":"Zhengqiu Zhu, Bin Chen, Yong Zhao, and Yatai Ji. 2021. Multi-sensing paradigm based urban air quality monitoring and hazardous gas source analyzing: a review. Journal of Safety Science and Resilience 2, 3 (2021), 131\u2013145.","journal-title":"Journal of Safety Science and Resilience"},{"key":"e_1_3_1_267_2","doi-asserted-by":"crossref","first-page":"107960","DOI":"10.1016\/j.cie.2022.107960","article-title":"Reinforcement learning based framework for COVID-19 resource allocation","volume":"167","author":"Zong Kai","year":"2022","unstructured":"Kai Zong and Cuicui Luo. 2022. Reinforcement learning based framework for COVID-19 resource allocation. Computers and Industrial Engineering 167 (2022), 107960.","journal-title":"Computers and Industrial Engineering"},{"key":"e_1_3_1_268_2","unstructured":"Zefang Zong Tao Feng Tong Xia Depeng Jin and Yong Li. 2021. Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey. arXiv:2108.04462. Retrieved from https:\/\/arxiv.org\/abs\/2108.04462"},{"key":"e_1_3_1_269_2","doi-asserted-by":"crossref","first-page":"4648","DOI":"10.1145\/3534678.3539037","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Zong Zefang","year":"2022","unstructured":"Zefang Zong, Hansen Wang, Jingwei Wang, Meng Zheng, and Yong Li. 2022. Rbg: Hierarchically solving large-scale routing problems in logistic systems via reinforcement learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4648\u20134658."},{"key":"e_1_3_1_270_2","first-page":"9980","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zong Zefang","year":"2022","unstructured":"Zefang Zong, Meng Zheng, Yong Li, and Depeng Jin. 2022. Mapdp: Cooperative multi-agent reinforcement learning to solve pickup and delivery problems. In Proceedings of the AAAI Conference on Artificial Intelligence. 9980\u20139988."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3695986","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3695986","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:30Z","timestamp":1750291470000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3695986"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,24]]},"references-count":269,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4,30]]}},"alternative-id":["10.1145\/3695986"],"URL":"https:\/\/doi.org\/10.1145\/3695986","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,24]]},"assertion":[{"value":"2023-06-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-22","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}