{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:02:00Z","timestamp":1775066520654,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Fujian Province National Natural Science under Grant","award":["2021J01319"],"award-info":[{"award-number":["2021J01319"]}]},{"name":"National Natural Science Foundation of China under Grant","award":["no. 61802133"],"award-info":[{"award-number":["no. 61802133"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>As a critical guidance facility for vehicle convergence and diversion in urban traffic networks, the control effect of traffic signals directly affects traffic efficiency and road congestion level. As a mature deep reinforcement learning algorithm, the double deep Q network has shown a significant optimization effect in intelligent traffic signal control research. In this paper, for the feature extraction defects of deep double Q network and the problem of underestimating the evaluation value of actions, we propose an Attention Mechanism Updated Weights Double Deep Q Network (AMUW\u2013DDQN) based on the attention mechanism for the optimal control of traffic signals. The AMUW\u2013DDQN method enhances the perceptual ability of the network by introducing the attention mechanism of Squeeze And Excitation Networks (SENet) to make the neural network pay attention to important state components automatically, and based on the idea that accurate representation of potentially optimal action values is better than the balanced representation of all the action values, it is considered that underestimated actions have a certain probability of being the optimal action and the loss function is weighted to optimize the action values. Simulation experiments were also conducted using the traffic flow data of the intersection of Fengze Street\u2013Tian\u2019an South Road, Fengze District, Quanzhou City, Fujian Province, China. The experimental results show that the method proposed in this paper has the most significant final convergence effect for the same number of iterations, and has better performance in the evaluation indexes such as vehicle queue length and vehicle delay time.<\/jats:p>","DOI":"10.1007\/s40747-025-01841-9","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T12:39:20Z","timestamp":1742387960000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Traffic signal optimization control method based on attention mechanism updated weights double deep Q network"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0509-6131","authenticated-orcid":false,"given":"Huizhen","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Zhenwei","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Youqing","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Haotian","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Xinyan","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"issue":"3","key":"1841_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3068287","volume":"50","author":"KLA Yau","year":"2017","unstructured":"Yau KLA, Qadir J, Khoo HL, Ling MH, Komisarczuk P (2017) A survey on reinforcement learning models and algorithms for traffic signal control. ACM Comput Surv (CSUR) 50(3):1. https:\/\/doi.org\/10.1145\/3068287","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1841_CR2","unstructured":"Xiao Z, Tong H, Qu R, Xing H, Luo S, Zhu Z, Song F, Feng L (2023) CapMatch: Semi-supervised contrastive transformer capsule with feature-based knowledge distillation for human activity recognition, IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1841_CR3","doi-asserted-by":"crossref","unstructured":"Xiao Z, Xu X, Xing H, Zhao B, Wang X, Song F, Qu R, Feng L (2024) DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification, IEEE Transactions on Cognitive and Developmental Systems","DOI":"10.2139\/ssrn.4327154"},{"key":"1841_CR4","doi-asserted-by":"publisher","unstructured":"Mileti\u0107 M, Ivanjko E, Mand\u017euka S, Ne\u010doska DK (2021) Combining neural gas and reinforcement learning for adaptive traffic signal control. In: 2021 International Symposium ELMAR, IEEE, pp. 179\u2013182. https:\/\/doi.org\/10.1109\/ELMAR52657.2021.9550948","DOI":"10.1109\/ELMAR52657.2021.9550948"},{"issue":"15","key":"1841_CR5","doi-asserted-by":"publisher","first-page":"18333","DOI":"10.1007\/s10489-023-04469-9","volume":"53","author":"Y Chen","year":"2023","unstructured":"Chen Y, Zhang H, Liu M, Ye M, Xie H, Pan Y (2023) Traffic signal optimization control method based on adaptive weighted averaged double deep Q network. Appl Intell 53(15):18333. https:\/\/doi.org\/10.1007\/s10489-023-04469-9","journal-title":"Appl Intell"},{"key":"1841_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2020.105713","volume":"146","author":"M Essa","year":"2020","unstructured":"Essa M, Sayed T (2020) Self-learning adaptive traffic signal control for real-time safety optimization. Accident Anal Prevent 146:105713","journal-title":"Accident Anal Prevent"},{"issue":"3","key":"1841_CR7","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/JAS.2016.7508798","volume":"3","author":"L Li","year":"2016","unstructured":"Li L, Lv Y, Wang FY (2016) Traffic signal timing via deep reinforcement learning. IEEE\/CAA J Automatica Sinica 3(3):247. https:\/\/doi.org\/10.1109\/JAS.2016.7508798","journal-title":"IEEE\/CAA J Automatica Sinica"},{"key":"1841_CR8","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.procs.2023.03.036","volume":"220","author":"C Wu","year":"2023","unstructured":"Wu C, Kim I, Ma Z (2023) Deep Reinforcement Learning Based Traffic Signal Control: A Comparative Analysis. Proc Comput Sci 220:275","journal-title":"Proc Comput Sci"},{"key":"1841_CR9","first-page":"638","volume":"58","author":"A Cabrejas-Egea","year":"2021","unstructured":"Cabrejas-Egea A, Zhang R, Walton N (2021) Reinforcement learning for traffic signal control: comparison with commercial systems. Trans Res Proc 58:638","journal-title":"Trans Res Proc"},{"key":"1841_CR10","doi-asserted-by":"publisher","unstructured":"Swapno SMR, Chhabra G, Kaushik K, Nobel SN, Islam MB, Shahiduzzaman M (2023) An Adaptive Traffic Signal Management System Incorporating Reinforcement Learning, in 2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA\/ICIS), IEEE, pp. 1\u20136. https:\/\/doi.org\/10.1109\/AICERA\/ICIS59538.2023.10420185","DOI":"10.1109\/AICERA\/ICIS59538.2023.10420185"},{"key":"1841_CR11","doi-asserted-by":"publisher","unstructured":"VM SM, Krishnendhu S, Mohandas P (2023) Real-Time Traffic Signal Prediction and Control using Deep Q-Network, in 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3), IEEE, pp. 1\u20136. https:\/\/doi.org\/10.1109\/IC2E357697.2023.10262818","DOI":"10.1109\/IC2E357697.2023.10262818"},{"key":"1841_CR12","doi-asserted-by":"publisher","unstructured":"Xu Z, Zhang L, Qi F (2023) Adaptive traffic light control based on reinforcement learning under different stages of autonomy, in 2023 35th Chinese Control and Decision Conference (CCDC), IEEE, pp. 715\u2013720. https:\/\/doi.org\/10.1109\/CCDC58219.2023.10327174","DOI":"10.1109\/CCDC58219.2023.10327174"},{"key":"1841_CR13","doi-asserted-by":"publisher","unstructured":"Fan L, Yang Y, Ji H, Xiong S (2023) Research on Cooperative Control of Traffic Signals based on Deep Reinforcement Learning. In: 2023 IEEE 12th data driven control and learning systems conference (DDCLS), IEEE, pp. 1608\u20131612. https:\/\/doi.org\/10.1109\/DDCLS58216.2023.10167232","DOI":"10.1109\/DDCLS58216.2023.10167232"},{"issue":"1","key":"1841_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/15472450.2018.1527694","volume":"24","author":"M Xu","year":"2020","unstructured":"Xu M, Wu J, Huang L, Zhou R, Wang T, Hu D (2020) Network-wide traffic signal control based on the discovery of critical nodes and deep reinforcement learning. J Intell Trans Syst 24(1):1. https:\/\/doi.org\/10.1080\/15472450.2018.1527694","journal-title":"J Intell Trans Syst"},{"key":"1841_CR15","doi-asserted-by":"publisher","unstructured":"Shanmugasundaram P, Sinha A (2021) Intelligent traffic control using double deep q networks for time-varying traffic flows, in 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, pp. 64\u201369. https:\/\/doi.org\/10.1109\/SPIN52536.2021.9565961","DOI":"10.1109\/SPIN52536.2021.9565961"},{"key":"1841_CR16","doi-asserted-by":"publisher","unstructured":"Agafonov A, Myasnikov V (2021) Traffic signal control: A double q-learning approach, in 2021 16th Conference on Computer Science and Intelligence Systems (FedCSIS), IEEE, pp. 365\u2013369. https:\/\/doi.org\/10.15439\/2021F109","DOI":"10.15439\/2021F109"},{"issue":"5","key":"1841_CR17","doi-asserted-by":"publisher","first-page":"13851","DOI":"10.1007\/s11042-023-16112-3","volume":"83","author":"R Kumar","year":"2024","unstructured":"Kumar R, Sharma NVK, Chaurasiya VK (2024) Adaptive traffic light control using deep reinforcement learning technique. Multimedia Tools Appl 83(5):13851. https:\/\/doi.org\/10.1007\/s11042-023-16112-3","journal-title":"Multimedia Tools Appl"},{"issue":"1","key":"1841_CR18","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1109\/MITS.2022.3144797","volume":"15","author":"F Mao","year":"2022","unstructured":"Mao F, Li Z, Li L (2022) A comparison of deep reinforcement learning models for isolated traffic signal control. IEEE Intell Trans Syst Mag 15(1):160. https:\/\/doi.org\/10.1109\/MITS.2022.3144797","journal-title":"IEEE Intell Trans Syst Mag"},{"key":"1841_CR19","doi-asserted-by":"publisher","unstructured":"Zhang X, Xu X (2023) FP-WDDQN: An improved deep reinforcement learning algorithm for adaptive traffic signal control, in 2023 IEEE International conference on data mining workshops (ICDMW), IEEE, pp. 44\u201351. https:\/\/doi.org\/10.1109\/ICDMW60847.2023.00015","DOI":"10.1109\/ICDMW60847.2023.00015"},{"key":"1841_CR20","doi-asserted-by":"publisher","unstructured":"Bouktif S, Cheniki A, Ouni A, El-Sayed H (2021) Traffic signal control based on deep reinforcement learning with simplified state and reward definitions. In: 2021 4th International conference on artificial intelligence and big data (ICAIBD), IEEE, pp. 253\u2013260. https:\/\/doi.org\/10.1109\/ICAIBD51990.2021.9459029","DOI":"10.1109\/ICAIBD51990.2021.9459029"},{"issue":"02","key":"1841_CR21","first-page":"430","volume":"40","author":"A Ren","year":"2023","unstructured":"Ren A, Zhou D, Feng J et al (2023) Attention mechanism based deep reinforcement learning for traffic signal control. Appl Res Comput 40(02):430","journal-title":"Appl Res Comput"},{"key":"1841_CR22","doi-asserted-by":"publisher","unstructured":"Raeis M, Leon-Garcia A (2021) A deep reinforcement learning approach for fair traffic signal control, in 2021 IEEE international intelligent transportation systems conference (ITSC), IEEE, pp. 2512\u20132518. https:\/\/doi.org\/10.1109\/ITSC48978.2021.9564847","DOI":"10.1109\/ITSC48978.2021.9564847"},{"key":"1841_CR23","doi-asserted-by":"publisher","unstructured":"P\u00e1los P, Husz\u00e1k \u00c1 (2020) Comparison of q-learning based traffic light control methods and objective functions, in 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), IEEE, pp. 1\u20136. https:\/\/doi.org\/10.23919\/SoftCOM50211.2020.9238290","DOI":"10.23919\/SoftCOM50211.2020.9238290"},{"key":"1841_CR24","unstructured":"Wang Z, Schaul T, Hessel M, Hasselt H, Lanctot M, Freitas N (2016) Dueling network architectures for deep reinforcement learning, in International conference on machine learning, PMLR, pp. 1995\u20132003"},{"key":"1841_CR25","doi-asserted-by":"publisher","unstructured":"Bhumeka S, Nahar A, Alam T, Sultan SM (2023) 3-Lane Based Traffic Signal Control Using Sequential-Duel Deep Q-Network (SD-DQN), in 2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), IEEE, pp. 112\u2013115. https:\/\/doi.org\/10.1109\/ECBIOS57802.2023.10218510","DOI":"10.1109\/ECBIOS57802.2023.10218510"},{"key":"1841_CR26","doi-asserted-by":"publisher","unstructured":"Sahu SP, Dewangan DK, Agrawal A, Priyanka TS (2021) Traffic light cycle control using deep reinforcement technique, in 2021 international conference on artificial intelligence and smart systems (ICAIS), IEEE, pp. 697\u2013702. https:\/\/doi.org\/10.1109\/ICAIS50930.2021.9395880","DOI":"10.1109\/ICAIS50930.2021.9395880"},{"issue":"2","key":"1841_CR27","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1109\/TVT.2018.2890726","volume":"68","author":"X Liang","year":"2019","unstructured":"Liang X, Du X, Wang G, Han Z (2019) A deep reinforcement learning network for traffic light cycle control. IEEE Trans Vehicular Technol 68(2):1243. https:\/\/doi.org\/10.1109\/TVT.2018.2890726","journal-title":"IEEE Trans Vehicular Technol"},{"key":"1841_CR28","doi-asserted-by":"publisher","unstructured":"Gu S, Zhang T, Zhang Y (2023) Inverse Reinforcement Learning Integrated Reinforcement Learning for Single Intersection Traffic Signal Control, in 2023 5th International conference on industrial artificial intelligence (IAI), IEEE, pp. 1\u20136. https:\/\/doi.org\/10.1109\/IAI59504.2023.10327510","DOI":"10.1109\/IAI59504.2023.10327510"},{"key":"1841_CR29","doi-asserted-by":"publisher","unstructured":"Ni W, Wang P, Li Z, Li C (2023) Traffic Signal Control Optimization Based on Deep Reinforcement Learning with Attention Mechanisms, in International conference on neural information processing, Springer, pp. 147\u2013158. https:\/\/doi.org\/10.1007\/978-981-99-8067-3_11","DOI":"10.1007\/978-981-99-8067-3_11"},{"key":"1841_CR30","doi-asserted-by":"publisher","unstructured":"Yang D, Zai W, Yan L, Wang J (2023) Low Carbon City Traffic Signal Control Based on Deep Reinforcement Learning, in 2023 Panda Forum on Power and Energy (PandaFPE), IEEE, pp. 1797\u20131801. https:\/\/doi.org\/10.1109\/PandaFPE57779.2023.10140589","DOI":"10.1109\/PandaFPE57779.2023.10140589"},{"key":"1841_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110440","volume":"267","author":"S Bouktif","year":"2023","unstructured":"Bouktif S, Cheniki A, Ouni A, El-Sayed H (2023) Deep reinforcement learning for traffic signal control with consistent state and reward design approach. Knowl-Based Syst 267:110440","journal-title":"Knowl-Based Syst"},{"key":"1841_CR32","doi-asserted-by":"publisher","unstructured":"Garg D, Chli M, Vogiatzis G (2018) Deep reinforcement learning for autonomous traffic light control, in 2018 3rd IEEE international conference on intelligent transportation engineering (ICITE), IEEE, pp. 214\u2013218. https:\/\/doi.org\/10.1109\/ICITE.2018.8492537,","DOI":"10.1109\/ICITE.2018.8492537"},{"issue":"2","key":"1841_CR33","first-page":"169","volume":"47","author":"H Sun","year":"2019","unstructured":"Sun H, Chen C, Liu Q, Zhao J (2019) Traffic signal control method based on deep reinforcement learning. Comput Sci 47(2):169","journal-title":"Comput Sci"},{"issue":"1","key":"1841_CR34","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1109\/TITS.2019.2958859","volume":"22","author":"R Zhang","year":"2020","unstructured":"Zhang R, Ishikawa A, Wang W, Striner B, Tonguz OK (2020) Using reinforcement learning with partial vehicle detection for intelligent traffic signal control. IEEE Trans Intell Trans Syst 22(1):404. https:\/\/doi.org\/10.1109\/TITS.2019.2958859","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"1841_CR35","doi-asserted-by":"publisher","unstructured":"Guo M, Wang P, Chan CY, Askary S (2019) A reinforcement learning approach for intelligent traffic signal control at urban intersections, in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE, pp. 4242\u20134247. https:\/\/doi.org\/10.1109\/ITSC.2019.8917268","DOI":"10.1109\/ITSC.2019.8917268"},{"issue":"1","key":"1841_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/15472450.2018.1527694","volume":"24","author":"M Xu","year":"2020","unstructured":"Xu M, Wu J, Huang L, Zhou R, Wang T, Hu D (2020) Network-wide traffic signal control based on the discovery of critical nodes and deep reinforcement learning. J Intell Trans Syst 24(1):1. https:\/\/doi.org\/10.1080\/15472450.2018.1527694","journal-title":"J Intell Trans Syst"},{"key":"1841_CR37","doi-asserted-by":"publisher","unstructured":"Egea AC, Howell S, Knutins M, Connaughton C (2020) Assessment of reward functions for reinforcement learning traffic signal control under real-world limitations, in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 965\u2013972. https:\/\/doi.org\/10.1109\/SMC42975.2020.9283498","DOI":"10.1109\/SMC42975.2020.9283498"},{"key":"1841_CR38","doi-asserted-by":"publisher","unstructured":"Zhong D, Boukerche A (2019) Traffic signal control using deep reinforcement learning with multiple resources of rewards, in Proceedings of the 16th ACM international symposium on performance evaluation of wireless Ad Hoc, sensor, & ubiquitous networks, pp. 23\u201328. https:\/\/doi.org\/10.1145\/3345860.3361522","DOI":"10.1145\/3345860.3361522"},{"issue":"2","key":"1841_CR39","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1109\/TVT.2019.2962514","volume":"69","author":"J Lee","year":"2019","unstructured":"Lee J, Chung J, Sohn K (2019) Reinforcement learning for joint control of traffic signals in a transportation network. IEEE Trans Vehicular Technol 69(2):1375. https:\/\/doi.org\/10.1109\/TVT.2019.2962514","journal-title":"IEEE Trans Vehicular Technol"},{"key":"1841_CR40","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"6","key":"1841_CR41","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","volume":"34","author":"K Arulkumaran","year":"2017","unstructured":"Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA (2017) Deep reinforcement learning: a brief survey. IEEE Signal Process Mag 34(6):26. https:\/\/doi.org\/10.1109\/MSP.2017.2743240","journal-title":"IEEE Signal Process Mag"},{"key":"1841_CR42","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1007\/s11390-020-9967-6","volume":"35","author":"Y Zheng","year":"2020","unstructured":"Zheng Y, Hao JY, Zhang ZZ, Meng ZP, Hao XT (2020) Efficient multiagent policy optimization based on weighted estimators in stochastic cooperative environments. J Comput Sci Technol 35:268. https:\/\/doi.org\/10.1007\/s11390-020-9967-6","journal-title":"J Comput Sci Technol"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01841-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01841-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01841-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T10:36:44Z","timestamp":1745923004000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01841-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,19]]},"references-count":42,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["1841"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01841-9","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,19]]},"assertion":[{"value":"26 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication:"}}],"article-number":"217"}}