{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T11:11:26Z","timestamp":1774955486525,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:00:00Z","timestamp":1774656000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-36795-6","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T13:55:44Z","timestamp":1774706144000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving road safety in smart cities using machine learning techniques"],"prefix":"10.1038","volume":"16","author":[{"given":"Muhammad Shujaat","family":"Abid","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mushtaq","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Said","family":"Nabi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tayyaba","family":"Sehar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dildar","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeong Hyeon","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,28]]},"reference":[{"key":"36795_CR1","unstructured":"Behboudi, N., Moosavi, S., & Ramnath, R. Recent advances in traffic accident analysis and prediction: a comprehensive review of machine learning techniques. arXiv: 2406.13968. (2024)."},{"issue":"1","key":"36795_CR2","doi-asserted-by":"publisher","first-page":"100","DOI":"10.22214\/ijraset.2020.1018","volume":"8","author":"S Dabhade","year":"2020","unstructured":"Dabhade, S., Mahale, S., Chitalkar, A., Gawhad, P. & Pagare, V. Road accident analysis and prediction using machine learning. Int. J. Res. Appl. Sci. Eng. Technol. 8(1), 100\u2013103 (2020).","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"key":"36795_CR3","doi-asserted-by":"publisher","first-page":"5135","DOI":"10.1016\/j.matpr.2021.05.415","volume":"47","author":"A Chand","year":"2021","unstructured":"Chand, A., Jayesh, S. & Bhasi, A. B. Road traffic accidents: An overview of data sources, analysis techniques and contributing factors. Materials Today Proc. 47, 5135\u20135141 (2021).","journal-title":"Materials Today Proc."},{"issue":"1","key":"36795_CR4","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1136\/emj.2005.023754","volume":"23","author":"WY Yee","year":"2006","unstructured":"Yee, W. Y., Cameron, P. A. & Bailey, M. J. Road traffic injuries in the elderly. Emerg. Med. J. 23(1), 42\u201346 (2006).","journal-title":"Emerg. Med. J."},{"key":"36795_CR5","doi-asserted-by":"publisher","first-page":"106322","DOI":"10.1016\/j.aap.2021.106322","volume":"160","author":"Z Ma","year":"2021","unstructured":"Ma, Z., Mei, G. & Cuomo, S. An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors. Accid. Anal. Prev. 160, 106322 (2021).","journal-title":"Accid. Anal. Prev."},{"issue":"1","key":"36795_CR6","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1186\/s12911-024-02725-7","volume":"24","author":"Z Ramezani","year":"2024","unstructured":"Ramezani, Z., Charati, J. Y., Alizadeh-Navaei, R. & Eslamijouybari, M. Accelerated hazard prediction based on age time-scale for women diagnosed with breast cancer using a deep learning method. BMC Med. Inform. Decis. Mak. 24(1), 314 (2024).","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"3","key":"36795_CR7","first-page":"343","volume":"11","author":"M Roostaee","year":"2023","unstructured":"Roostaee, M. & Meidanshahi, R. Hidden pattern discovery on clinical data: An approach based on data mining techniques. J. AI Data Min. 11(3), 343\u2013355 (2023).","journal-title":"J. AI Data Min."},{"key":"36795_CR8","first-page":"861","volume":"8","author":"T Fawcett","year":"2005","unstructured":"Fawcett, T. Roc analysis in pattern recognition. Pattern Recogn. Lett. 8, 861\u2013874 (2005).","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"36795_CR9","first-page":"39","volume":"32","author":"D Shanshal","year":"2020","unstructured":"Shanshal, D., Babaoglu, C. & Ba\u015far, A. Prediction of fatal and major injury of drivers, cyclists, and pedestrians in collisions. Promet Traffic Transp. 32(1), 39\u201353 (2020).","journal-title":"Promet Traffic Transp."},{"issue":"1","key":"36795_CR10","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3390\/systems13010031","volume":"13","author":"J Tang","year":"2025","unstructured":"Tang, J., Huang, Y., Liu, D., Xiong, L. & Bu, R. Research on traffic accident severity level prediction model based on improved machine learning. Systems 13(1), 31 (2025).","journal-title":"Systems"},{"key":"36795_CR11","volume-title":"Introduction to Data Mining and Knowledge Discovery","author":"HA Edelstein","year":"1998","unstructured":"Edelstein, H. A. Introduction to Data Mining and Knowledge Discovery (Two Crows, 1998)."},{"key":"36795_CR12","doi-asserted-by":"crossref","unstructured":"Ahammad, S. H., Sukesh, M., Narender, M., Ettyem, S. A., Al-Majdi, K., & Saikumar, K. A novel approach to avoid road traffic accidents and develop safety rules for traffic using crash prediction model technique. In: Micro-Electronics and Telecommunication Engineering: Proceedings of 6th ICMETE 2022. 367\u2013377. (Springer, 2023).","DOI":"10.1007\/978-981-19-9512-5_34"},{"issue":"5","key":"36795_CR13","doi-asserted-by":"publisher","first-page":"55","DOI":"10.18400\/tjce.1322965","volume":"34","author":"Z\u00c7 Kuyumcu","year":"2023","unstructured":"Kuyumcu, Z. \u00c7., Aslan, H. & Yurtay, N. Identifying interrelated factors of fatal and injury traffic accidents using association rules. Turkish J. Civil Eng. 34(5), 55\u201380 (2023).","journal-title":"Turkish J. Civil Eng."},{"key":"36795_CR14","volume-title":"AIP Conference Proceedings","author":"NH Datu","year":"2023","unstructured":"Datu, N. H. Road traffic accidents analysis using association rule mining and descriptive analytics. In AIP Conference Proceedings (ed. Datu, N. H.) (AIP Publishing, 2023)."},{"key":"36795_CR15","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/OJITS.2022.3160404","volume":"3","author":"M Emu","year":"2022","unstructured":"Emu, M., Kamal, F. B., Choudhury, S. & Rahman, Q. A. Fatality prediction for motor vehicle collisions: Mining big data using deep learning and ensemble methods. IEEE Open J. Intell. Transp. Syst. 3, 199\u2013209 (2022).","journal-title":"IEEE Open J. Intell. Transp. Syst."},{"key":"36795_CR16","volume-title":"Journal of Physics: Conference Series","author":"J Yadav","year":"2021","unstructured":"Yadav, J., Batra, K. & Goel, A. K. A framework for analyzing road accidents using machine learning paradigms. In Journal of Physics: Conference Series (eds Yadav, J. et al.) (IOP Publishing, 2021)."},{"issue":"1","key":"36795_CR17","first-page":"1257627","volume":"2020","author":"J Gan","year":"2020","unstructured":"Gan, J., Li, L., Zhang, D., Yi, Z. & Xiang, Q. An alternative method for traffic accident severity prediction: using deep forests algorithm. J. Adv. Transp. 2020(1), 1257627 (2020).","journal-title":"J. Adv. Transp."},{"key":"36795_CR18","volume-title":"2019 IEEE International Conference on Big Data (Big Data)","author":"A H\u00e9bert","year":"2019","unstructured":"H\u00e9bert, A., Gu\u00e9don, T., Glatard, T. & Jaumard, B. High-resolution road vehicle collision prediction for the city of montreal. In 2019 IEEE International Conference on Big Data (Big Data) (eds H\u00e9bert, A. et al.) (IEEE, 2019)."},{"key":"36795_CR19","volume-title":"2019 7th International Conference on Smart Computing & Communications (ICSCC)","author":"MF Labib","year":"2019","unstructured":"Labib, M. F., Rifat, A. S., Hossain, M. M., Das, A. K. & Nawrine, F. Road accident analysis and prediction of accident severity by using machine learning in Bangladesh. In 2019 7th International Conference on Smart Computing & Communications (ICSCC) (eds Labib, M. F. et al.) (IEEE, 2019)."},{"key":"36795_CR20","volume-title":"2019 3rd International Conference on Computing and Communications Technologies (ICCCT)","author":"G Parathasarathy","year":"2019","unstructured":"Parathasarathy, G., Soumya, T. R., Das, Y. J., Saravanakumar, J. & Merjora, A. A. Using hybrid data mining algorithm for analysing road accidents data set. In 2019 3rd International Conference on Computing and Communications Technologies (ICCCT) (eds Parathasarathy, G. et al.) (IEEE, 2019)."},{"key":"36795_CR21","volume-title":"2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN)","author":"B Kumeda","year":"2019","unstructured":"Kumeda, B. et al. Classification of road traffic accident data using machine learning algorithms. In 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN) (eds Kumeda, B. et al.) (IEEE, 2019)."},{"key":"36795_CR22","volume-title":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","author":"RE Al Mamlook","year":"2019","unstructured":"Al Mamlook, R. E., Ali, A., Hasan, R. A. & Kazim, H. A. M. Machine learning to predict the freeway traffic accidents-based driving simulation. In 2019 IEEE National Aerospace and Electronics Conference (NAECON) (eds Al Mamlook, R. E. et al.) (IEEE, 2019)."},{"key":"36795_CR23","volume-title":"International Conference on Recent Developments in Science, Engineering and Technology","author":"Z Alam","year":"2017","unstructured":"Alam, Z. Improving road safety in India using data mining techniques. In International Conference on Recent Developments in Science, Engineering and Technology (ed. Alam, Z.) (Springer, 2017)."},{"issue":"5","key":"36795_CR24","first-page":"661","volume":"33","author":"L Babaoglu","year":"2021","unstructured":"Babaoglu, L. & Babaoglu, C. Prediction of fatalities in vehicle collisions in Canada. Promet Traffic Transp. 33(5), 661\u2013669 (2021).","journal-title":"Promet Traffic Transp."},{"key":"36795_CR25","unstructured":"Toronto Police Services. KSI. Public Safety Data Portal. https:\/\/data.torontopolice.on.ca\/pages\/total-ksi. (2023)."},{"key":"36795_CR26","doi-asserted-by":"publisher","unstructured":"Road Traffic Accident Dataset. RTA. The Punjab Emergency Service, Rescue 1122 Rawalpindi. https:\/\/doi.org\/10.7910\/DVN\/4VGTDR. (2023).","DOI":"10.7910\/DVN\/4VGTDR"},{"key":"36795_CR27","unstructured":"Powers, D. M. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv: 2010.16061. (2020)."},{"key":"36795_CR28","doi-asserted-by":"publisher","unstructured":"Abid, M. S., Hussain, M., Nabi, S., Sehar, T., Hussain, D., & Gu, Y. H. Road Traffic Accident Analysis (Version 1.0). https:\/\/doi.org\/10.5281\/zenodo.18184155. (2026).","DOI":"10.5281\/zenodo.18184155"},{"issue":"9","key":"36795_CR29","doi-asserted-by":"publisher","first-page":"1982","DOI":"10.3390\/math11091982","volume":"11","author":"MS Ben-Shachar","year":"2023","unstructured":"Ben-Shachar, M. S., Patil, I., Th\u00e9riault, R., Wiernik, B. M. & L\u00fcdecke, D. Phi, Fei, Fo, Fum: effect sizes for categorical data that use the chi-squared statistic. Mathematics 11(9), 1982 (2023).","journal-title":"Mathematics"},{"issue":"5","key":"36795_CR30","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/j.amjcard.2004.05.027","volume":"94","author":"AJ Deluca","year":"2004","unstructured":"Deluca, A. J., Cusack, E., Aronow, W. S. & Monsen, C. E. Sensitivity, specificity, positive predictive value, and negative predictive value of the dipyridamole sestamibi stress test in predicting graft occlusion or\u2265 50% new native coronary artery disease in men versus women and in patients aged\u2265 65 years versus< 65 years who had prior coronary artery bypass grafting. Am. J. Cardiol. 94(5), 625\u2013626 (2004).","journal-title":"Am. J. Cardiol."},{"key":"36795_CR31","first-page":"1","volume":"21","author":"R O\u011fuz","year":"2024","unstructured":"O\u011fuz, R., Babacan, H. H., A\u015f\u0131c\u0131o\u011flu, F. & \u00dcvet, H. Classification of 3D shoe prints using the PointNet architecture: proof of concept investigation of binary classification of Nike and Adidas outsoles. For. Sci. Med. Pathol. 21, 1\u201310 (2024).","journal-title":"For. Sci. Med. Pathol."},{"key":"36795_CR32","doi-asserted-by":"crossref","unstructured":"Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"36795_CR33","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470","volume-title":"Classification and Regression Trees","author":"L Breiman","year":"2017","unstructured":"Breiman, L., Friedman, J., Olshen, R. A. & Stone, C. J. Classification and Regression Trees (Routledge, 2017)."},{"key":"36795_CR34","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273\u2013297 (1995).","journal-title":"Mach. Learn."},{"issue":"8","key":"36795_CR35","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. Long short-term memory. Neural Comput. 9 (8), 1735\u20131780 (1997).","journal-title":"Neural Comput."},{"key":"36795_CR36","unstructured":"GeeksforGeeks. Impact of dataset size on deep learning model. GeeksforGeeks. (2024)."},{"issue":"1","key":"36795_CR37","doi-asserted-by":"publisher","first-page":"22329","DOI":"10.1038\/s41598-024-73643-x","volume":"14","author":"N Bhatt","year":"2024","unstructured":"Bhatt, N. et al. A data-centric approach to improve performance of deep learning models. Sci. Rep. 14(1), 22329 (2024).","journal-title":"Sci. Rep."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-36795-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-36795-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-36795-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T10:25:08Z","timestamp":1774952708000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-36795-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,28]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["36795"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-36795-6","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,28]]},"assertion":[{"value":"11 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2026","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study utilized data from the UNBC McMaster Shoulder pain Expression Archive Database, which is publicly available for research purposes. Ethical approval for the collection and use of this data was granted by the University of Northern British Columbia. Informed consent was obtained from participants, who were made aware of the study\u2019s objectives and their rights. Researchers can access the dataset by signing an agreement form and will receive instructions within five business days.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}],"article-number":"10793"}}