{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T01:53:56Z","timestamp":1770515636281,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DE210101623"],"award-info":[{"award-number":["DE210101623"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001778","name":"Deakin University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001778","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Traffic accidents are a major global health and economic concern. As such, research into understanding driving behaviors becomes essential to minimize the associated risks. Among various factors that can influence driving behaviors, listening to music while driving is a complex task that needs investigation. Music can enhance arousal and manage stress, which could potentially improve one\u2019s driving performance. Listening to music, however, also competes for cognitive resources, increasing one\u2019s mental workload and potentially degrading the driving ability. This study investigates the impact of listening to music with different tempos on drivers and their performance using an Artificial Intelligence (AI) approach. A total of 26 participants are subjected to three driving scenarios in a unique simulated experiment, while utilizing a motion platform. The conditions are driving while listening to slow-tempo music, and fast-tempo music, and with no music. A dataset is created by collecting data through Tobii eye-tracking glasses, Equivital sensor belts, and a software tool. This dataset is preprocessed and used to train a convolutional neural network-long short-term memory (CNN-LSTM) model. This model\u2019s performance is optimized through hyperparameter tuning and Chi-squared feature selection, in order to maximize accuracy and minimize computation time. The model performance is compared with those from a densely layered deep learning model and several classical machine learning models. The devised CNN-LSTM model outperforms other machine learning models, achieving an average accuracy rate of 99.29% with minimal variance across multiple evaluations, demonstrating its effectiveness and consistency in classifying drivers\u2019 behaviors under varying auditory conditions.<\/jats:p>","DOI":"10.1007\/s00521-025-11077-w","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T10:51:11Z","timestamp":1740394271000},"page":"9401-9412","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluating the impact of music tempo on drivers and their performance using an artificial intelligence model: a multi-source data approach"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2043-5437","authenticated-orcid":false,"given":"Arian","family":"Shajari","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3620-8693","authenticated-orcid":false,"given":"Houshyar","family":"Asadi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2675-9547","authenticated-orcid":false,"given":"Shehab","family":"Alsanwy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-5270","authenticated-orcid":false,"given":"Saeid","family":"Nahavandi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4191-9083","authenticated-orcid":false,"given":"Chee Peng","family":"Lim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"issue":"1","key":"11077_CR1","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1109\/TITS.2012.2225104","volume":"14","author":"B Vanholme","year":"2012","unstructured":"Vanholme B, Gruyer D, Lusetti B, Glaser S, Mammar S (2012) Highly automated driving on highways based on legal safety. IEEE Trans Intell Transp Syst 14(1):333\u2013347","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"2","key":"11077_CR2","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/S0386-1112(14)60067-4","volume":"25","author":"H Ito","year":"2001","unstructured":"Ito H, UNO H, Atsumi B, Akamatsu M (2001) Visual distraction while driving. Iatss Res 25(2):21","journal-title":"Iatss Res"},{"key":"11077_CR3","unstructured":"World Health Organization. \"Road traffic injuries.\" https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/road-traffic-injuries (accessed"},{"key":"11077_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121066","volume":"234","author":"L Mou","year":"2023","unstructured":"Mou L et al (2023) Multimodal driver distraction detection using dual-channel network of CNN and transformer. Exp Syst Appl 234:121066","journal-title":"Exp Syst Appl"},{"key":"11077_CR5","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.trf.2023.08.003","volume":"98","author":"J Navarro","year":"2023","unstructured":"Navarro J, Gaujoux V, Ouimet MC, Ferreri L, Reynaud E (2023) How does background music affect drivers\u2019 behaviours, emotions and mood behind the wheel? Transport Res F: Traffic Psychol Behav 98:47\u201360","journal-title":"Transport Res F: Traffic Psychol Behav"},{"key":"11077_CR6","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/2118553","author":"A Shajari","year":"2023","unstructured":"Shajari A et al (2023) Detection of driving distractions and their impacts. J Adv Trans. https:\/\/doi.org\/10.1155\/2023\/2118553","journal-title":"J Adv Trans"},{"issue":"4","key":"11077_CR7","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1093\/jmt\/38.4.254","volume":"38","author":"WE Knight","year":"2001","unstructured":"Knight WE, Rickard NS (2001) Relaxing music prevents stress-induced increases in subjective anxiety, systolic blood pressure, and heart rate in healthy males and females. J Music Ther 38(4):254\u2013272","journal-title":"J Music Ther"},{"issue":"4","key":"11077_CR8","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1136\/hrt.2005.064600","volume":"92","author":"L Bernardi","year":"2006","unstructured":"Bernardi L, Porta C, Sleight P (2006) Cardiovascular, cerebrovascular, and respiratory changes induced by different types of music in musicians and non-musicians: the importance of silence. Heart 92(4):445\u2013452","journal-title":"Heart"},{"issue":"3","key":"11077_CR9","doi-asserted-by":"publisher","first-page":"143","DOI":"10.3233\/OER-2007-7301","volume":"7","author":"BH Dalton","year":"2007","unstructured":"Dalton BH, Behm DG (2007) Effects of noise and music on human and task performance: a systematic review. Occup Ergon 7(3):143\u2013152","journal-title":"Occup Ergon"},{"issue":"2","key":"11077_CR10","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1518\/001872006777724417","volume":"48","author":"DD Salvucci","year":"2006","unstructured":"Salvucci DD (2006) Modeling driver behavior in a cognitive architecture. Hum Factors 48(2):362\u2013380","journal-title":"Hum Factors"},{"issue":"4","key":"11077_CR11","doi-asserted-by":"publisher","first-page":"267","DOI":"10.34172\/hpp.2023.32","volume":"13","author":"M Ghojazadeh","year":"2023","unstructured":"Ghojazadeh M, Farhoudi M, Rezaei M, Rahnemayan S, Narimani M, Sadeghi-Bazargani H (2023) Effect of music on driving performance and physiological and psychological indicators: a systematic review and meta-analysis study. Health Promotion Perspectives 13(4):267","journal-title":"Health Promotion Perspectives"},{"issue":"4","key":"11077_CR12","doi-asserted-by":"publisher","first-page":"204166951986198","DOI":"10.1177\/2041669519861982","volume":"10","author":"R Li","year":"2019","unstructured":"Li R, Chen YV, Zhang L (2019) Effect of music tempo on long-distance driving: which tempo is the most effective at reducing fatigue? i-Perception 10(4):2041669519861982","journal-title":"i-Perception"},{"key":"11077_CR13","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.aap.2012.01.022","volume":"48","author":"AB \u00dcnal","year":"2012","unstructured":"\u00dcnal AB, Steg L, Epstude K (2012) The influence of music on mental effort and driving performance. Accid Anal Prev 48:271\u2013278","journal-title":"Accid Anal Prev"},{"issue":"7","key":"11077_CR14","first-page":"3256","volume":"7","author":"N Pradheep","year":"2020","unstructured":"Pradheep N, Venkatachalam M, Saroja M, Sivasooriya V (2020) Effect of music and noise on human driving and accident: a systematic review. Int Res J Eng Technol 7(7):3256\u20133261","journal-title":"Int Res J Eng Technol"},{"key":"11077_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2021.106168","volume":"157","author":"L Miao","year":"2021","unstructured":"Miao L, Gu Y, He L, Wang H, Schwebel DC, Shen Y (2021) The influence of music tempo on mental load and hazard perception of novice drivers. Accid Anal Prev 157:106168","journal-title":"Accid Anal Prev"},{"issue":"4","key":"11077_CR16","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/S1369-8478(01)00025-0","volume":"4","author":"W Brodsky","year":"2001","unstructured":"Brodsky W (2001) The effects of music tempo on simulated driving performance and vehicular control. Trans Res F: Traffic Psychol Behav 4(4):219\u2013241","journal-title":"Trans Res F: Traffic Psychol Behav"},{"issue":"7","key":"11077_CR17","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1080\/00140139.2021.2003872","volume":"65","author":"CI Karageorghis","year":"2022","unstructured":"Karageorghis CI et al (2022) Interactive effects of task load and music tempo on psychological, psychophysiological, and behavioural outcomes during simulated driving. Ergonomics 65(7):915\u2013932","journal-title":"Ergonomics"},{"key":"11077_CR18","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1016\/j.trf.2018.10.007","volume":"60","author":"B Millet","year":"2019","unstructured":"Millet B, Ahn S, Chattah J (2019) The impact of music on vehicular performance: a meta-analysis. Trans Res F: Traffic Psychol Behav 60:743\u2013760","journal-title":"Trans Res F: Traffic Psychol Behav"},{"issue":"1","key":"11077_CR19","first-page":"1793","volume":"3","author":"T Jayalakshmi","year":"2011","unstructured":"Jayalakshmi T, Santhakumaran A (2011) Statistical normalization and back propagation for classification. Int J Comput Theory Eng 3(1):1793\u20138201","journal-title":"Int J Comput Theory Eng"},{"key":"11077_CR20","unstructured":"I. Goodfellow, Y. Bengio, and A. Courville, (2016) Deep learning. MIT press"},{"key":"11077_CR21","unstructured":"H. Gholamalinezhad and H. Khosravi, (2020) Pooling methods in deep neural networks, a review. arXiv preprint arXiv:2009.07485"},{"key":"11077_CR22","doi-asserted-by":"publisher","first-page":"99837","DOI":"10.1109\/ACCESS.2022.3206425","volume":"10","author":"A Halbouni","year":"2022","unstructured":"Halbouni A, Gunawan TS, Habaebi MH, Halbouni M, Kartiwi M, Ahmad R (2022) CNN-LSTM: hybrid deep neural network for network intrusion detection system. IEEE Access 10:99837\u201399849","journal-title":"IEEE Access"},{"key":"11077_CR23","unstructured":"J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, 2014 Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555"},{"issue":"10","key":"11077_CR24","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1140\/epjst\/e2019-900046-x","volume":"228","author":"K Smagulova","year":"2019","unstructured":"Smagulova K, James AP (2019) A survey on LSTM memristive neural network architectures and applications. European Phys J Special Topics 228(10):2313\u20132324","journal-title":"European Phys J Special Topics"},{"key":"11077_CR25","unstructured":"R. Pascanu, C. Gulcehre, K. Cho, and Y. Bengio, (2013) How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026"},{"issue":"8","key":"11077_CR26","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"11077_CR27","doi-asserted-by":"crossref","unstructured":"B. Chen, Q. Huang, Y. Chen, L. Cheng, and R. Chen, (2018) Deep neural networks for multi-class sentiment classification. in 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), 2018: IEEE, pp. 854\u2013859.","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2018.00142"},{"issue":"4","key":"11077_CR28","first-page":"1213","volume":"20","author":"K Srinivasan","year":"2019","unstructured":"Srinivasan K, Cherukuri AK, Vincent DR, Garg A, Chen B-Y (2019) An efficient implementation of artificial neural networks with K-fold cross-validation for process optimization. Journal of Internet Technology 20(4):1213\u20131225","journal-title":"Journal of Internet Technology"},{"issue":"185","key":"11077_CR29","first-page":"1","volume":"18","author":"L Li","year":"2018","unstructured":"Li L, Jamieson K, DeSalvo G, Rostamizadeh A, Talwalkar A (2018) Hyperband: a novel bandit-based approach to hyperparameter optimization. J Mach Learn Res 18(185):1\u201352","journal-title":"J Mach Learn Res"},{"key":"11077_CR30","volume-title":"A guide to chi-squared testing","author":"PE Greenwood","year":"1996","unstructured":"Greenwood PE, Nikulin MS (1996) A guide to chi-squared testing. John Wiley & Sons"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11077-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11077-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11077-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T07:07:33Z","timestamp":1748329653000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11077-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,24]]},"references-count":30,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["11077"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11077-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,24]]},"assertion":[{"value":"29 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 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":"This work was supported by Australian Research Council (Grant ID: DE210101623).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}