{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:13:39Z","timestamp":1771665219846,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T00:00:00Z","timestamp":1597795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006469","name":"Fundo para o Desenvolvimento das Ci\u00eancias e da Tecnologia","doi-asserted-by":"publisher","award":["0025\/2019\/AKP"],"award-info":[{"award-number":["0025\/2019\/AKP"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To monitor road safety, billions of records can be generated by Controller Area Network bus each day on public transportation. Automation to determine whether certain driving behaviour of drivers on public transportation can be considered safe on the road using artificial intelligence or machine learning techniques for big data analytics has become a possibility recently. Due to the high false classification rates of the current methods, our goal is to build a practical and accurate method for road safety predictions that automatically determine if the driving behaviour is safe on public transportation. In this paper, our main contributions include (1) a novel feature extraction method because of the lack of informative features in raw CAN bus data, (2) a novel boosting method for driving behaviour classification (safe or unsafe) to combine advantages of deep learning and shallow learning methods with much improved performance, and (3) an evaluation of our method using a real-world data to provide accurate labels from domain experts in the public transportation industry for the first time. The experiments show that the proposed boosting method with our proposed features outperforms seven other popular methods on the real-world dataset by 5.9% and 5.5%.<\/jats:p>","DOI":"10.3390\/s20174671","type":"journal-article","created":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T09:22:31Z","timestamp":1597828951000},"page":"4671","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Complementary Deep and Shallow Learning with Boosting for Public Transportation Safety"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5756-0127","authenticated-orcid":false,"given":"Shengda","family":"Luo","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Macau University of Science and Technology, Taipa 999078, Macao"}]},{"given":"Alex Po","family":"Leung","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Macau University of Science and Technology, Taipa 999078, Macao"}]},{"given":"Xingzhao","family":"Qiu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Macau University of Science and Technology, Taipa 999078, Macao"},{"name":"Melbourne School of Engineering, the University of Melbourne, Melbourne 3010, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6966-9015","authenticated-orcid":false,"given":"Jan Y. K.","family":"Chan","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Macau University of Science and Technology, Taipa 999078, Macao"}]},{"given":"Haozhi","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Macau University of Science and Technology, Taipa 999078, Macao"},{"name":"The School of Computer Science, Fudan University, Shanghai 201203, China"},{"name":"Zhuhai Fudan Innovation Institute, Zhuhai 519000, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tison, J., Chaudhary, N., Cosgrove, L., and Group, P.R. (2011). National Phone Survey on Distracted Driving Attitudes and Behaviors, Technical Report.","DOI":"10.1037\/e562822012-001"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.2105\/AJPH.2009.187179","article-title":"Trends in fatalities from distracted driving in the United States, 1999 to 2008","volume":"100","author":"Wilson","year":"2010","journal-title":"Am. J. Public Health"},{"key":"ref_3","unstructured":"WHO (2018). Global Status Report on Road Safety 2018, World Health Organization."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1109\/TITS.2015.2462084","article-title":"Driver behavior analysis for safe driving: A survey","volume":"16","author":"Kaplan","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hallac, D., Sharang, A., Stahlmann, R., Lamprecht, A., Huber, M., Roehder, M., Sosic, R., and Leskovec, J. (2016, January 1\u20134). Driver identification using automobile sensor data from a single turn. Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795670"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.14778\/3137765.3137822","article-title":"Exploring big volume sensor data with Vroom","volume":"10","author":"Moll","year":"2017","journal-title":"Proc. VLDB Endow."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/JPROC.2016.2634938","article-title":"The car as an ambient sensing platform [point of view]","volume":"105","author":"Massaro","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Van Ly, M., Martin, S., and Trivedi, M.M. (2013, January 23\u201326). Driver classification and driving style recognition using inertial sensors. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, Australia.","DOI":"10.1109\/IVS.2013.6629603"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.dss.2013.06.001","article-title":"Evaluation and aggregation of pay-as-you-drive insurance rate factors: A classification analysis approach","volume":"56","author":"Paefgen","year":"2013","journal-title":"Decis. Support Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Vaitkus, V., Lengvenis, P., and \u017dylius, G. (2014, January 2\u20135). Driving style classification using long-term accelerometer information. Proceedings of the 19th International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland.","DOI":"10.1109\/MMAR.2014.6957429"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.trd.2013.10.003","article-title":"Driving behavior at a roundabout: A hierarchical Bayesian regression analysis","volume":"26","author":"Mudgal","year":"2014","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wu, Z., Li, F., Xie, C., Ren, T., Chen, J., and Liu, L. (2019). A deep learning framework for driving behavior identification on in-vehicle CAN-BUS sensor data. Sensors, 19.","DOI":"10.3390\/s19061356"},{"key":"ref_13","unstructured":"Ontan\u00f3n, S., Lee, Y.C., Snodgrass, S., Winston, F.K., and Gonzalez, A.J. (2017, January 27\u201329). Learning to predict driver behavior from observation. Proceedings of the AAAI Spring Symposium Series, Palo Alto, CA, USA."},{"key":"ref_14","first-page":"100198","article-title":"In-vehicle network intrusion detection using deep convolutional neural network","volume":"21","author":"Song","year":"2020","journal-title":"Veh. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhou, A., Li, Z., and Shen, Y. (2019). Anomaly Detection of CAN Bus Messages Using a Deep Neural Network for Autonomous Vehicles. Appl. Sci., 9.","DOI":"10.3390\/app9153174"},{"key":"ref_16","first-page":"7","article-title":"Detecting attacks on the can protocol with machine learning","volume":"588","author":"Chockalingam","year":"2016","journal-title":"Annu. EECS"},{"key":"ref_17","unstructured":"Narayanan, S.N., Mittal, S., and Joshi, A. (2015). Using data analytics to detect anomalous states in vehicles. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kiencke, U., Dais, S., and Litschel, M. (1986). Automotive serial controller area network. SAE Transactions, SAE International.","DOI":"10.4271\/860391"},{"key":"ref_19","unstructured":"(2009, September 09). Building Adapter for Vehicle On-board Diagnostic. Available online: obddiag.net."},{"key":"ref_20","unstructured":"Lebrun, A., and Demay, J.C. (2016, September 09). Canspy: A Platform for Auditing CAN Devices. Available online: https:\/\/jyx.jyu.fi\/bitstream\/handle\/123456789\/67095."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Paefgen, J., Michahelles, F., and Staake, T. (2011, January 17\u201321). GPS trajectory feature extraction for driver risk profiling. Proceedings of the 2011 International Workshop on Trajectory Data Mining and Analysis, Beijing, China.","DOI":"10.1145\/2030080.2030091"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1080\/10630730802401942","article-title":"Using GPS data to understand driving behavior","volume":"15","author":"Grengs","year":"2008","journal-title":"J. Urban Technol."},{"key":"ref_23","first-page":"20","article-title":"Modeling and recognizing driver behavior based on driving data: A survey","volume":"2014","author":"Wang","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1515\/popets-2015-0029","article-title":"Automobile driver fingerprinting","volume":"2016","author":"Enev","year":"2016","journal-title":"Proc. Privacy Enhanc. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez, M., Echanobe, J., and del Campo, I. (2016, January 1\u20134). Driver identification and impostor detection based on driving behavior signals. Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795582"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fung, N.C., Wallace, B., Chan, A.D., Goubran, R., Porter, M.M., Marshall, S., and Knoefel, F. (2017, January 7\u201310). Driver identification using vehicle acceleration and deceleration events from naturalistic driving of older drivers. Proceedings of the IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rochester, MN, USA.","DOI":"10.1109\/MeMeA.2017.7985845"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-018-0118-7","article-title":"Who is behind the wheel? Driver identification and fingerprinting","volume":"5","author":"Ezzini","year":"2018","journal-title":"J. Big Data"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102504","DOI":"10.1016\/j.compeleceng.2017.12.050","article-title":"Human behavior characterization for driving style recognition in vehicle system","volume":"83","author":"Martinelli","year":"2018","journal-title":"Comp. Electr. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5377","DOI":"10.1093\/mnras\/staa166","article-title":"An investigation on the factors affecting machine learning classifications in gamma-ray astronomy","volume":"492","author":"Luo","year":"2020","journal-title":"Mon. Not. R. Astronom. Soc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105037","DOI":"10.1016\/j.phrs.2020.105037","article-title":"Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing","volume":"160","author":"Luo","year":"2020","journal-title":"Pharmacol. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/JPROC.2006.888388","article-title":"Driver behavior and situation aware brake assistance for intelligent vehicles","volume":"95","author":"McCall","year":"2007","journal-title":"Proc. IEEE"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3053","DOI":"10.1109\/TMM.2019.2916455","article-title":"Region-based Context Enhanced Network for Robust Multiple Face Alignment","volume":"21","author":"Lin","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1109\/TCYB.2019.2917049","article-title":"Task-Oriented Feature-Fused Network With Multivariate Dataset for Joint Face Analysis","volume":"50","author":"Lin","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sainath, T.N., Vinyals, O., Senior, A., and Sak, H. (2015, January 19\u201325). Convolutional, long short-term memory, fully connected deep neural networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, QLD, Australia.","DOI":"10.1109\/ICASSP.2015.7178838"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., and Beaufays, F. (2014, January 14\u201318). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association, Singapore.","DOI":"10.21437\/Interspeech.2014-80"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TITS.2011.2119483","article-title":"Online driver distraction detection using long short-term memory","volume":"12","author":"Wollmer","year":"2011","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1002\/widm.1143","article-title":"Generating ensembles of heterogeneous classifiers using stacked generalization","volume":"5","author":"Sesmero","year":"2015","journal-title":"Wiley Interdiscip. Rev. Data Mining Knowl. Discov."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","article-title":"Ensemble based systems in decision making","volume":"6","author":"Polikar","year":"2006","journal-title":"IEEE Circuits Syst. Mag."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000). Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems, Springer.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hansun, S. (2013, January 27\u201328). A new approach of moving average method in time series analysis. Proceedings of the Conference on New Media Studies (CoNMedia), Tangerang, Indonesia.","DOI":"10.1109\/CoNMedia.2013.6708545"},{"key":"ref_41","first-page":"81","article-title":"FRAMA\u2013Fractal Adaptive Moving Average","volume":"23","author":"Ehlers","year":"2005","journal-title":"Tech. Anal. Stock. Commod."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1057\/palgrave.jors.2600823","article-title":"Some properties of a simple moving average when applied to forecasting a time series","volume":"50","author":"Johnston","year":"1999","journal-title":"J. Oper. Res. Soc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"John, G.H., Kohavi, R., and Pfleger, K. (1994). Irrelevant features and the subset selection problem. Machine Learning Proceedings 1994, Elsevier.","DOI":"10.1016\/B978-1-55860-335-6.50023-4"},{"key":"ref_47","unstructured":"Almuallim, H., and Dietterich, T.G. (1991). Learning with Many Irrelevant Features, AAAI."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Cristianini, N., and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press.","DOI":"10.1017\/CBO9780511801389"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10107-010-0420-4","article-title":"Pegasos: Primal estimated sub-gradient solver for svm","volume":"127","author":"Singer","year":"2011","journal-title":"Math. Program."},{"key":"ref_50","first-page":"207","article-title":"Distance metric learning for large margin nearest neighbor classification","volume":"10","author":"Weinberger","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1007\/s100440200009","article-title":"A data complexity analysis of comparative advantages of decision forest constructors","volume":"5","author":"Ho","year":"2002","journal-title":"Pattern Anal. Appl."},{"key":"ref_52","unstructured":"Rennie, J., Shih, L., Teevan, J., and Karger, D. (2003, January 21\u201324). Tackling the poor assumptions of Naive Bayes classifiers. Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), Washington, DC, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1093\/biostatistics\/kxj035","article-title":"Regularized linear discriminant analysis and its application in microarrays","volume":"8","author":"Guo","year":"2006","journal-title":"Biostatistics"},{"key":"ref_54","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, Kauai, HI, USA."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/MITS.2014.2315660","article-title":"The warrigal dataset: Multi-vehicle trajectories and v2v communications","volume":"6","author":"Ward","year":"2014","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_56","unstructured":"Luo, S., Leung, A.P., and Qiu, X. (2019, January 10\u201316). A Machine Learning Framework for Road Safety of Smart Public Transportation. Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI) Workshops 2019, Macao, China."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4671\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:02:59Z","timestamp":1760176979000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,19]]},"references-count":56,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174671"],"URL":"https:\/\/doi.org\/10.3390\/s20174671","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,19]]}}}