{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:27:46Z","timestamp":1768811266013,"version":"3.49.0"},"reference-count":97,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T00:00:00Z","timestamp":1695945600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T00:00:00Z","timestamp":1695945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pers Ubiquit Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00779-023-01740-1","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T22:02:03Z","timestamp":1696024923000},"page":"1729-1746","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An intelligent divide-and-conquer approach for driving style management"],"prefix":"10.1007","volume":"27","author":[{"given":"Khalid Ali","family":"Al Abri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8238-6813","authenticated-orcid":false,"given":"Nafaa","family":"Jabeur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hana","family":"Gharrad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ansar Ul-Haque","family":"Yasar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,29]]},"reference":[{"key":"1740_CR1","doi-asserted-by":"publisher","unstructured":"Abuali N, Abou-Zeid H (2016) Driver behavior modeling: Developments and future directions. Int J Veh Technol 2016. https:\/\/doi.org\/10.1155\/2016\/6952791","DOI":"10.1155\/2016\/6952791"},{"key":"1740_CR2","doi-asserted-by":"publisher","unstructured":"Aguilar J, Aguilar K, Chavez Garcia G, Cordero J, Puerto E (2017) Different intelligent approaches for modeling the style of car driving, 284\u2013291. https:\/\/doi.org\/10.5220\/0006411902840291","DOI":"10.5220\/0006411902840291"},{"key":"1740_CR3","doi-asserted-by":"publisher","first-page":"105008","DOI":"10.1109\/ACCESS.2020.2999829","volume":"8","author":"MH Alkinani","year":"2020","unstructured":"Alkinani MH, Khan WZ, Arshad Q (2020) Detecting human driver inattentive and aggressive driving behavior using deep learning: recent advances, requirements and open challenges. IEEE Access 8:105008\u2013105030. https:\/\/doi.org\/10.1109\/ACCESS.2020.2999829","journal-title":"IEEE Access"},{"issue":"2","key":"1740_CR4","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1109\/TITS.2011.2179537","volume":"13","author":"GS Aoude","year":"2012","unstructured":"Aoude GS, Desaraju VR, Stephens LH, How JP (2012) Driver behavior classification at intersections and validation on large naturalistic data set. IEEE Trans Intell Transp Syst 13(2):724\u2013736. https:\/\/doi.org\/10.1109\/TITS.2011.2179537","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1740_CR5","doi-asserted-by":"publisher","unstructured":"Azevedo-Sa H, Yang XJ, Robert LP, Tilbury DM (2021) Handling trust between drivers and automated vehicles for improved collaboration. Companion of the 2021 ACM\/IEEE International Conference on Human-Robot Interaction, 589\u2013591. https:\/\/doi.org\/10.1145\/3434074.3446358","DOI":"10.1145\/3434074.3446358"},{"key":"1740_CR6","doi-asserted-by":"publisher","DOI":"10.7302\/1286","author":"H AzevedoS\u00e1","year":"2021","unstructured":"AzevedoS\u00e1 H, Yang XJ, Robert L, Tilbury D (2021) A unified bi-directional model for natural and artificial trust in human-robot collaboration. IEEE Robot Autom Lett. https:\/\/doi.org\/10.7302\/1286","journal-title":"IEEE Robot Autom Lett"},{"issue":"2","key":"1740_CR7","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/s12665-011-1263-x","volume":"66","author":"GD Bathrellos","year":"2012","unstructured":"Bathrellos GD, Gaki-Papanastassiou K, Skilodimou HD, Papanastassiou D, Chousianitis KG (2012) Potential suitability for urban planning and industry development using natural hazard maps and geological\u2013geomorphological parameters. Environ Earth Sci 66(2):537\u2013548. https:\/\/doi.org\/10.1007\/s12665-011-1263-x","journal-title":"Environ Earth Sci"},{"key":"1740_CR8","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.scitotenv.2016.10.025","volume":"575","author":"GD Bathrellos","year":"2017","unstructured":"Bathrellos GD, Skilodimou HD, Chousianitis K, Youssef AM, Pradhan B (2017) Suitability estimation for urban development using multi-hazard assessment map. Sci Total Environ 575:119\u2013134. https:\/\/doi.org\/10.1016\/j.scitotenv.2016.10.025","journal-title":"Sci Total Environ"},{"issue":"2","key":"1740_CR9","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1109\/TITS.2019.2896672","volume":"21","author":"MM Bejani","year":"2020","unstructured":"Bejani MM, Ghatee M (2020) Convolutional neural network with adaptive regularization to classify driving styles on smartphones. IEEE Trans Intell Transp Syst 21(2):543\u2013552. https:\/\/doi.org\/10.1109\/TITS.2019.2896672","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1740_CR10","doi-asserted-by":"publisher","unstructured":"Belakhdar I, Kaaniche W, Djemal R, Ouni B (2018) Single-channel-based automatic drowsiness detection architecture with a reduced number of EEG features. Microprocessors Microsyst 58. https:\/\/doi.org\/10.1016\/j.micpro.2018.02.004","DOI":"10.1016\/j.micpro.2018.02.004"},{"key":"1740_CR11","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1109\/ICIT.2017.7915497","volume":"2017","author":"P Brombacher","year":"2017","unstructured":"Brombacher P, Masino J, Frey M, Gauterin F (2017) Driving event detection and driving style classification using artificial neural networks. IEEE Int Conf Ind Technol (ICIT) 2017:997\u20131002. https:\/\/doi.org\/10.1109\/ICIT.2017.7915497","journal-title":"IEEE Int Conf Ind Technol (ICIT)"},{"key":"1740_CR12","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.aap.2015.03.036","volume":"80","author":"C Chen","year":"2015","unstructured":"Chen C, Zhang G, Tarefder R, Ma J, Wei H, Guan H (2015) A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes. Accid Anal Prev 80:76\u201388. https:\/\/doi.org\/10.1016\/j.aap.2015.03.036","journal-title":"Accid Anal Prev"},{"key":"1740_CR13","doi-asserted-by":"publisher","first-page":"6687378","DOI":"10.1155\/2021\/6687378","volume":"2021","author":"D Chen","year":"2021","unstructured":"Chen D, Chen Z, Zhang Y, Qu X, Zhang M, Wu C (2021) Driving style recognition under connected circumstance using a supervised hierarchical Bayesian model. J Adv Transp 2021:6687378. https:\/\/doi.org\/10.1155\/2021\/6687378","journal-title":"J Adv Transp"},{"key":"1740_CR14","doi-asserted-by":"publisher","unstructured":"Chen J, Wu Y, Huang H, Wu B, Hou G (2018) Driving-data-driven platform of driving behavior spectrum for vehicle networks. 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), 518\u2013525. https:\/\/doi.org\/10.1109\/HPCC\/SmartCity\/DSS.2018.00099","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2018.00099"},{"key":"1740_CR15","doi-asserted-by":"publisher","unstructured":"Chhabra R, Verma S, Krishna CR (2017) A survey on driver behavior detection techniques for intelligent transportation systems. 2017 7th International Conference on Cloud Computing, Data Science Engineering - Confluence, 36\u201341. https:\/\/doi.org\/10.1109\/CONFLUENCE.2017.7943120","DOI":"10.1109\/CONFLUENCE.2017.7943120"},{"key":"1740_CR16","unstructured":"Choi S, Kim J, Kwak D, Angkititrakul P, Hansen JH (2007) Analysis and classification of driver behavior using in-vehicle can-bus information. Biennial Workshop on DSP for In-Vehicle and Mobile Systems 17\u201319"},{"issue":"8","key":"1740_CR17","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1049\/iet-its.2016.0294","volume":"11","author":"D Chu","year":"2017","unstructured":"Chu D, Deng Z, He Y, Wu C, Sun C, Lu Z (2017) Curve speed model for driver assistance based on driving style classification. IET Intel Transport Syst 11(8):501\u2013510. https:\/\/doi.org\/10.1049\/iet-its.2016.0294","journal-title":"IET Intel Transport Syst"},{"key":"1740_CR18","doi-asserted-by":"publisher","unstructured":"del Campo I, Asua E, Mart\u00ednez V, Mata-Carballeira \u00d3, Echanobe J (2018) Driving style recognition based on ride comfort using a hybrid machine learning algorithm. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 3251\u20133258. https:\/\/doi.org\/10.1109\/ITSC.2018.8569722","DOI":"10.1109\/ITSC.2018.8569722"},{"key":"1740_CR19","doi-asserted-by":"publisher","unstructured":"Deng C, Wu C, Lyu N, Huang Z (2017) Driving style recognition method using braking characteristics based on hidden Markov model. PLoS ONE 12. https:\/\/doi.org\/10.1371\/journal.pone.0182419","DOI":"10.1371\/journal.pone.0182419"},{"key":"1740_CR20","doi-asserted-by":"publisher","unstructured":"Deng Z, Chu D, Wu C, He Y, Cui J (2018) Curve safe speed model considering driving style based on driver behaviour questionnaire. Transp Res Part F: Traffic Psychol Behav 65. https:\/\/doi.org\/10.1016\/j.trf.2018.02.007","DOI":"10.1016\/j.trf.2018.02.007"},{"issue":"3","key":"1740_CR21","doi-asserted-by":"publisher","first-page":"1838","DOI":"10.1109\/TSMC.2020.3037229","volume":"52","author":"Z Deng","year":"2022","unstructured":"Deng Z, Chu D, Wu C, Liu S, Sun C, Liu T, Cao D (2022) A probabilistic model for driving-style-recognition-enabled driver steering behaviors. IEEE Trans Syst Man Cybern: Syst 52(3):1838\u20131851. https:\/\/doi.org\/10.1109\/TSMC.2020.3037229","journal-title":"IEEE Trans Syst Man Cybern: Syst"},{"key":"1740_CR22","doi-asserted-by":"publisher","unstructured":"Ding X, Chong X, Bao Z, Xue Y, Zhang S (2017) Fuzzy comprehensive assessment method based on the entropy weight method and its application in the water environmental safety evaluation of the Heshangshan drinking water source area, three gorges reservoir area, China. Water 9(5). https:\/\/doi.org\/10.3390\/w9050329","DOI":"10.3390\/w9050329"},{"key":"1740_CR23","doi-asserted-by":"publisher","unstructured":"Ding Z, Zhu M, Wu Z, Fu Y, Liu X (2018) Combining AHP-Entropy approach with GIS for construction waste landfill selection\u2014a case study of Shenzhen. Int J Environ Res Public Health 15(10). https:\/\/doi.org\/10.3390\/ijerph15102254","DOI":"10.3390\/ijerph15102254"},{"key":"1740_CR24","doi-asserted-by":"publisher","unstructured":"Dong W, Li J, Yao R, Li C, Yuan T, Wang L (2016) Characterizing driving styles with deep learning. https:\/\/doi.org\/10.48550\/arXiv.1607.03611","DOI":"10.48550\/arXiv.1607.03611"},{"issue":"2","key":"1740_CR25","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1109\/TITS.2010.2092770","volume":"12","author":"Y Dong","year":"2011","unstructured":"Dong Y, Hu Z, Uchimura K, Murayama N (2011) Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans Intell Transp Syst 12(2):596\u2013614. https:\/\/doi.org\/10.1109\/TITS.2010.2092770","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1740_CR26","doi-asserted-by":"publisher","unstructured":"D\u00f6rr D, Grabengiesser D, Gauterin F (2014) Online driving style recognition using fuzzy logic. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, pp. 1021\u20131026. https:\/\/doi.org\/10.1109\/ITSC.2014.6957822","DOI":"10.1109\/ITSC.2014.6957822"},{"key":"1740_CR27","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.trf.2018.06.044","volume":"58","author":"HR Eftekhari","year":"2018","unstructured":"Eftekhari HR, Ghatee M (2018) Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition. Transport Res F: Traffic Psychol Behav 58:782\u2013796. https:\/\/doi.org\/10.1016\/j.trf.2018.06.044","journal-title":"Transport Res F: Traffic Psychol Behav"},{"key":"1740_CR28","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.rser.2015.12.163","volume":"60","author":"M Engelken","year":"2016","unstructured":"Engelken M, R\u00f6mer B, Drescher M, Welpe IM, Picot A (2016) Comparing drivers, barriers, and opportunities of business models for renewable energies: A review. Renew Sustain Energy Rev 60:795\u2013809. https:\/\/doi.org\/10.1016\/j.rser.2015.12.163","journal-title":"Renew Sustain Energy Rev"},{"key":"1740_CR29","doi-asserted-by":"publisher","unstructured":"Feraud IS, Naranjo JE (2019) Are you a good driver? A data-driven approach to estimate driving style. Proceedings of the 11th International Conference on Computer Modeling and Simulation, 3\u20137. https:\/\/doi.org\/10.1145\/3307363.3307375","DOI":"10.1145\/3307363.3307375"},{"key":"1740_CR30","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1016\/j.procs.2017.05.389","volume":"109","author":"M Feraud","year":"2017","unstructured":"Feraud M, Galland S (2017) First comparison of SARL to other agent-programming languages and frameworks. Procedia Comput Sci 109:1080\u20131085. https:\/\/doi.org\/10.1016\/j.procs.2017.05.389","journal-title":"Procedia Comput Sci"},{"issue":"2","key":"1740_CR31","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1109\/TITS.2018.2836308","volume":"20","author":"U Fugiglando","year":"2019","unstructured":"Fugiglando U, Massaro E, Santi P, Milardo S, Abida K, Stahlmann R, Netter F, Ratti C (2019) Driving behavior analysis through CAN bus data in an uncontrolled environment. IEEE Trans Intell Transp Syst 20(2):737\u2013748. https:\/\/doi.org\/10.1109\/TITS.2018.2836308","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1740_CR32","doi-asserted-by":"publisher","unstructured":"Garrosa M, Olmeda E, del Toro S, D\u00edaz V (2021) Holistic vehicle instrumentation for assessing driver driving styles. Sensors 21(4). https:\/\/doi.org\/10.3390\/s21041427","DOI":"10.3390\/s21041427"},{"issue":"8","key":"1740_CR33","doi-asserted-by":"publisher","first-page":"1899","DOI":"10.1007\/s12369-020-00703-3","volume":"13","author":"Y Guo","year":"2021","unstructured":"Guo Y, Yang XJ (2021) Modeling and predicting trust dynamics in human-robot teaming: a Bayesian inference approach. Int J Soc Robot 13(8):1899\u20131909. https:\/\/doi.org\/10.1007\/s12369-020-00703-3","journal-title":"Int J Soc Robot"},{"issue":"1","key":"1740_CR34","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1109\/TR.2017.2778754","volume":"67","author":"Z Guo","year":"2018","unstructured":"Guo Z, Pan Y, Zhao G, Cao S, Zhang J (2018) Detection of driver vigilance level using EEG signals and driving contexts. IEEE Trans Reliab 67(1):370\u2013380. https:\/\/doi.org\/10.1109\/TR.2017.2778754","journal-title":"IEEE Trans Reliab"},{"key":"1740_CR35","doi-asserted-by":"publisher","unstructured":"Han W, Wang W, Li X, Xi J (2019) Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation. IET Intell Transp Syst 13. https:\/\/doi.org\/10.1049\/iet-its.2017.0379","DOI":"10.1049\/iet-its.2017.0379"},{"issue":"7","key":"1740_CR36","first-page":"72","volume":"15","author":"L Huang","year":"2018","unstructured":"Huang L, Dong ZH, Zhang RM (2018) Analysis of driving behavior based on random forest. Wireless Int Technol 15(7):72\u201376","journal-title":"Wireless Int Technol"},{"issue":"12","key":"1740_CR37","doi-asserted-by":"publisher","first-page":"196","DOI":"10.19678\/j.issn.1000-3428.0050708","volume":"44","author":"F Hui","year":"2018","unstructured":"Hui F, Peng N, Jing SC, Zhou Q, Jia S (2018) Driving behavior clustering and anomaly detection method based on agglomeration level. Comput Eng 44(12):196\u2013201. https:\/\/doi.org\/10.19678\/j.issn.1000-3428.0050708","journal-title":"Comput Eng"},{"key":"1740_CR38","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1109\/SICE.2007.4421155","volume":"2007","author":"M Ishibashi","year":"2007","unstructured":"Ishibashi M, Okuwa M, Doi S, Akamatsu M (2007) Indices for characterizing driving style and their relevance to car following behavior. SICE Ann Conf 2007:1132\u20131137. https:\/\/doi.org\/10.1109\/SICE.2007.4421155","journal-title":"SICE Ann Conf"},{"key":"1740_CR39","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.trf.2020.01.003","volume":"69","author":"T Itkonen","year":"2020","unstructured":"Itkonen T, Lehtonen E, Selpi S (2020) Characterisation of motorway driving style using naturalistic driving data. Transport Res F: Traffic Psychol Behav 69:72\u201379. https:\/\/doi.org\/10.1016\/j.trf.2020.01.003","journal-title":"Transport Res F: Traffic Psychol Behav"},{"key":"1740_CR40","doi-asserted-by":"publisher","unstructured":"Jabbar R, Shinoy M, Kharbeche M, Al-Khalifa K, Krichen M, Barkaoui K (2020) Driver drowsiness detection model using convolutional neural networks techniques for android application. 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), 237\u2013242. https:\/\/doi.org\/10.1109\/ICIoT48696.2020.9089484","DOI":"10.1109\/ICIoT48696.2020.9089484"},{"issue":"1","key":"1740_CR41","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1207\/S15327566IJCE0401_04","volume":"4","author":"J-Y Jian","year":"2000","unstructured":"Jian J-Y, Bisantz AM, Drury CG (2000) Foundations for an empirically determined scale of trust in automated systems. Int J Cogn Ergon 4(1):53\u201371. https:\/\/doi.org\/10.1207\/S15327566IJCE0401_04","journal-title":"Int J Cogn Ergon"},{"key":"1740_CR42","doi-asserted-by":"publisher","first-page":"2641","DOI":"10.1109\/ICRA.2015.7139555","volume":"2015","author":"M Kuderer","year":"2015","unstructured":"Kuderer M, Gulati S, Burgard W (2015) Learning driving styles for autonomous vehicles from demonstration. IEEE Int Conf Robot Autom (ICRA) 2015:2641\u20132646. https:\/\/doi.org\/10.1109\/ICRA.2015.7139555","journal-title":"IEEE Int Conf Robot Autom (ICRA)"},{"issue":"4","key":"1740_CR43","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/s18040957","volume":"18","author":"KW Lee","year":"2018","unstructured":"Lee KW, Yoon HS, Song JM, Park KR (2018) Convolutional neural network-based classification of driver\u2019s emotion during aggressive and smooth driving using multi-modal camera sensors. Sensors (Switzerland) 18(4):14\u201316. https:\/\/doi.org\/10.3390\/s18040957","journal-title":"Sensors (Switzerland)"},{"key":"1740_CR44","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.trc.2016.11.011","volume":"74","author":"G Li","year":"2017","unstructured":"Li G, Li SE, Cheng B, Green P (2017) Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities. Transp Res Part C: Emerg Technol 74:113\u2013125. https:\/\/doi.org\/10.1016\/j.trc.2016.11.011","journal-title":"Transp Res Part C: Emerg Technol"},{"key":"1740_CR45","first-page":"8","volume":"12","author":"JW Li","year":"2018","unstructured":"Li JW, Zhao ZG, Shen PH, Guo QY (2018) Research on k-means clustering and recognition method of driving style. Autom Technol 12:8\u201312","journal-title":"Autom Technol"},{"key":"1740_CR46","doi-asserted-by":"publisher","unstructured":"Li M, Song X, Cao H, Wang J, Huang Y, Hu C, Wang H (2019) Shared control with a novel dynamic authority allocation strategy based on game theory and driving safety field. Mech Syst Signal Process 124. https:\/\/doi.org\/10.1016\/j.ymssp.2019.01.040","DOI":"10.1016\/j.ymssp.2019.01.040"},{"key":"1740_CR47","doi-asserted-by":"publisher","unstructured":"Lin C-T, Liang S-F, Chao W-H, Ko L-W, Chao C-F, Chen Y-C, Huang T-Y (2006) Driving style classification by analyzing EEG responses to unexpected obstacle dodging tasks. 2006 IEEE International Conference on Systems, Man and Cybernetics, 6, 4916\u20134919. https:\/\/doi.org\/10.1109\/ICSMC.2006.385084","DOI":"10.1109\/ICSMC.2006.385084"},{"key":"1740_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2014\/569109","volume":"2014","author":"N Lin","year":"2014","unstructured":"Lin N, Zong C, Tomizuka M, Song P, Zhang Z, Li G (2014) An overview on study of identification of driver behavior characteristics for automotive control. Math Probl Eng 2014:1\u201315. https:\/\/doi.org\/10.1155\/2014\/569109","journal-title":"Math Probl Eng"},{"issue":"4","key":"1740_CR49","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1109\/TITS.2015.2496157","volume":"17","author":"T Liu","year":"2016","unstructured":"Liu T, Yang Y, Huang G-B, Yeo YK, Lin Z (2016) Driver distraction detection using semi-supervised machine learning. IEEE Trans Intell Transp Syst 17(4):1108\u20131120. https:\/\/doi.org\/10.1109\/TITS.2015.2496157","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1740_CR50","doi-asserted-by":"publisher","unstructured":"Liu W, Deng K, Zhang X, Cheng Y, Zheng Z, Jiang F, Peng J (2020) A semi-supervised tri-catboost method for driving style recognition. Symmetry 12(3). https:\/\/doi.org\/10.3390\/sym12030336","DOI":"10.3390\/sym12030336"},{"key":"1740_CR51","unstructured":"Lu M (2011) Comparison of driver classification based on subjective evaluation and objective experiment. Transportation Research Board, 90th Annual Meeting, Washington DC"},{"issue":"4","key":"1740_CR52","doi-asserted-by":"publisher","first-page":"2965","DOI":"10.1109\/TIE.2018.2850031","volume":"66","author":"C Lv","year":"2019","unstructured":"Lv C, Hu X, Sangiovanni-Vincentelli A, Li Y, Martinez CM, Cao D (2019) Driving-style-based codesign optimization of an automated electric vehicle: a cyber-physical system approach. IEEE Trans Industr Electron 66(4):2965\u20132975. https:\/\/doi.org\/10.1109\/TIE.2018.2850031","journal-title":"IEEE Trans Industr Electron"},{"key":"1740_CR53","doi-asserted-by":"publisher","first-page":"459","DOI":"10.3389\/fpsyg.2018.00459","volume":"9","author":"J Ma","year":"2018","unstructured":"Ma J, Gu J, Jia H, Yao Z, Chang R (2018) The Relationship between drivers\u2019 cognitive fatigue and speed variability during monotonous daytime driving. Front Psychol 9:459. https:\/\/doi.org\/10.3389\/fpsyg.2018.00459","journal-title":"Front Psychol"},{"key":"1740_CR54","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1109\/AIM.2015.7222548","volume":"2015","author":"U Manawadu","year":"2015","unstructured":"Manawadu U, Ishikawa M, Kamezaki M, Sugano S (2015) Analysis of individual driving experience in autonomous and human-driven vehicles using a driving simulator. IEEE Int Conf Adv Intell Mechatron (AIM) 2015:299\u2013304. https:\/\/doi.org\/10.1109\/AIM.2015.7222548","journal-title":"IEEE Int Conf Adv Intell Mechatron (AIM)"},{"key":"1740_CR55","doi-asserted-by":"publisher","unstructured":"Marina Martinez C, Heuke M, Gao B, Cao D (2017) Driving style recognition for intelligent vehicle control and advance driver assistance: a survey. IEEE Trans Intell Transp Syst, PP. https:\/\/doi.org\/10.1109\/TITS.2017.2706978","DOI":"10.1109\/TITS.2017.2706978"},{"key":"1740_CR56","doi-asserted-by":"publisher","first-page":"102504","DOI":"10.1016\/j.compeleceng.2017.12.050","volume":"83","author":"F Martinelli","year":"2020","unstructured":"Martinelli F, Mercaldo F, Orlando A, Nardone V, Santone A, Sangaiah AK (2020) Human behavior characterization for driving style recognition in vehicle system. Comput Electr Eng 83:102504. https:\/\/doi.org\/10.1016\/j.compeleceng.2017.12.050","journal-title":"Comput Electr Eng"},{"key":"1740_CR57","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.trf.2014.06.008","volume":"26","author":"LM Martinussen","year":"2014","unstructured":"Martinussen LM, M\u00f8ller M, Prato CG (2014) Assessing the relationship between the driver behavior questionnaire and the driver skill inventory: revealing sub-groups of drivers. Transport Res F: Traffic Psychol Behav 26:82\u201391. https:\/\/doi.org\/10.1016\/j.trf.2014.06.008","journal-title":"Transport Res F: Traffic Psychol Behav"},{"key":"1740_CR58","doi-asserted-by":"publisher","unstructured":"Meseguer J, Calafate C, Cano J-C (2018) On the correlation between heart rate and driving style in real driving scenarios. Mob Netw Appl 23. https:\/\/doi.org\/10.1007\/s11036-017-0833-x","DOI":"10.1007\/s11036-017-0833-x"},{"key":"1740_CR59","doi-asserted-by":"publisher","first-page":"6006","DOI":"10.1051\/matecconf\/201815006006","volume":"150","author":"R Mohamed","year":"2018","unstructured":"Mohamed R, MohdYusof M, Wahid N (2018) A comparative study of feature selection techniques for bat algorithm in various applications. MATEC Web of Conferences 150:6006. https:\/\/doi.org\/10.1051\/matecconf\/201815006006","journal-title":"MATEC Web of Conferences"},{"key":"1740_CR60","doi-asserted-by":"publisher","first-page":"139896","DOI":"10.1109\/ACCESS.2021.3102222","volume":"9","author":"A Mohammed","year":"2021","unstructured":"Mohammed A, Yazid MRM, Zaidan BB, Zaidan AA, Garfan S, Zaidan RA, Ameen HA, Kareem ZH, Malik RQ (2021) A landscape of research on bus driver behavior: taxonomy, open challenges, motivations, recommendations, limitations, and pathways solution in future. IEEE Access 9:139896\u2013139927. https:\/\/doi.org\/10.1109\/ACCESS.2021.3102222","journal-title":"IEEE Access"},{"key":"1740_CR61","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.trf.2013.05.003","volume":"20","author":"M M\u00f8ller","year":"2013","unstructured":"M\u00f8ller M, Haustein S (2013) Keep on cruising: Changes in lifestyle and driving style among male drivers between the age of 18 and 23. Transport Res F: Traffic Psychol Behav 20:59\u201369. https:\/\/doi.org\/10.1016\/j.trf.2013.05.003","journal-title":"Transport Res F: Traffic Psychol Behav"},{"key":"1740_CR62","doi-asserted-by":"publisher","unstructured":"Nakamura RYM, Pereira LAM, Costa KA, Rodrigues D, Papa JP, Yang X-S (2012) BBA: A Binary Bat Algorithm for feature selection. 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, 291\u2013297. https:\/\/doi.org\/10.1109\/SIBGRAPI.2012.47","DOI":"10.1109\/SIBGRAPI.2012.47"},{"key":"1740_CR63","doi-asserted-by":"publisher","first-page":"108282","DOI":"10.1016\/j.comnet.2021.108282","volume":"198","author":"H Gharrad","year":"2021","unstructured":"Gharrad H, Jabeur N, Yasar AU-H, Galland S, Mbarki M (2021) A five-step drone collaborative planning approach for the management of distributed spatial events and vehicle notification using multi-agent systems and firefly algorithms. Comput Netw 198:108282. https:\/\/doi.org\/10.1016\/j.comnet.2021.108282. (ISSN 1389-1286)","journal-title":"Comput Netw"},{"key":"1740_CR64","doi-asserted-by":"publisher","unstructured":"Ouali T, Shah N, Kim B, Fuente D, Gao B (2016) Driving style identification algorithm with real-world data based on statistical approach. https:\/\/doi.org\/10.4271\/2016-01-1422","DOI":"10.4271\/2016-01-1422"},{"key":"1740_CR65","doi-asserted-by":"publisher","unstructured":"Outay F, Jabeur N, Haddad H, Bouyahia Z, Gharrad H (2021) Toward an intelligent driving behavior adjustment based on legal personalized policies within the context of connected vehicles. Front Built Environ 7. https:\/\/doi.org\/10.3389\/fbuil.2021.686732","DOI":"10.3389\/fbuil.2021.686732"},{"key":"1740_CR66","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.aap.2018.11.016","volume":"123","author":"B Palat","year":"2019","unstructured":"Palat B, Saint Pierre G, Delhomme P (2019) Evaluating individual risk proneness with vehicle dynamics and self-report data - toward the efficient detection of At-risk drivers. Accid Anal Prev 123:140\u2013149. https:\/\/doi.org\/10.1016\/j.aap.2018.11.016","journal-title":"Accid Anal Prev"},{"issue":"2","key":"1740_CR67","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TITS.2019.2910157","volume":"21","author":"G Panagopoulos","year":"2020","unstructured":"Panagopoulos G, Pavlidis I (2020) Forecasting markers of habitual driving behaviors associated with crash risk. IEEE Trans Intell Transp Syst 21(2):841\u2013851. https:\/\/doi.org\/10.1109\/TITS.2019.2910157","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1740_CR68","doi-asserted-by":"publisher","unstructured":"Pugnetti C, Elmer S (2020) Self-assessment of driving style and the willingness to share personal information. J Risk Financ Manag 13(3). https:\/\/doi.org\/10.3390\/jrfm13030053","DOI":"10.3390\/jrfm13030053"},{"key":"1740_CR69","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.trd.2018.05.002","volume":"66","author":"G Qi","year":"2019","unstructured":"Qi G, Wu J, Zhou Y, Du Y, Jia Y, Hounsell N, Stanton NA (2019) Recognizing driving styles based on topic models. Transp Res Part D: Transp Environ 66:13\u201322. https:\/\/doi.org\/10.1016\/j.trd.2018.05.002","journal-title":"Transp Res Part D: Transp Environ"},{"key":"1740_CR70","doi-asserted-by":"publisher","unstructured":"Qi W, Shen B, Dong L, Wang Z, Zeng K (2018) Evaluation method of taxi drivers\u2019; stress level based on DBQ and MDSI. In CICTP 2018, pp 2012\u20132019. https:\/\/doi.org\/10.1061\/9780784481523.200","DOI":"10.1061\/9780784481523.200"},{"issue":"9","key":"1740_CR71","doi-asserted-by":"publisher","first-page":"e0238728","DOI":"10.1371\/journal.pone.0238728","volume":"15","author":"F Rezapur-Shahkolai","year":"2020","unstructured":"Rezapur-Shahkolai F, Taheri M, Etesamifard T, Roshanaei G, Shirahmadi S (2020) Dimensions of aberrant driving behaviors and their association with road traffic injuries among drivers. PloS One 15(9):e0238728. https:\/\/doi.org\/10.1371\/journal.pone.0238728","journal-title":"PloS One"},{"key":"1740_CR72","first-page":"108","volume":"39","author":"XS Wang","year":"2018","unstructured":"Wang XS, Bian Z (2018) Recognition and prediction of driving behavior based on bayesian model. J Commun 39:108\u2013117","journal-title":"J Commun"},{"issue":"5","key":"1740_CR73","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1080\/15472450.2016.1198699","volume":"21","author":"J Schorr","year":"2017","unstructured":"Schorr J, Hamdar SH, Silverstein C (2017) Measuring the safety impact of road infrastructure systems on driver behavior: vehicle instrumentation and real world driving experiment. J Intell Transp Syst 21(5):364\u2013374. https:\/\/doi.org\/10.1080\/15472450.2016.1198699","journal-title":"J Intell Transp Syst"},{"key":"1740_CR74","doi-asserted-by":"publisher","DOI":"10.4271\/2010-01-0835","author":"F Syed","year":"2010","unstructured":"Syed F, Nallapa S, Dobryden A, Grand C, McGee R, Filev D (2010) Design and analysis of an adaptive real-time advisory system for improving real world fuel economy in a hybrid electric vehicle. SAE Tech Pap. https:\/\/doi.org\/10.4271\/2010-01-0835","journal-title":"SAE Tech Pap"},{"key":"1740_CR75","doi-asserted-by":"publisher","unstructured":"Tanveer H, Mubasher MM, Jaffry SW (2020) Integrating human panic factor in intelligent driver model. 3rd International Conference on Advancements in Computational Sciences, ICACS 2020, 0\u20135. https:\/\/doi.org\/10.1109\/ICACS47775.2020.9055947","DOI":"10.1109\/ICACS47775.2020.9055947"},{"issue":"3","key":"1740_CR76","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/S0001-4575(03)00010-1","volume":"36","author":"O Taubman-Ben-Ari","year":"2004","unstructured":"Taubman-Ben-Ari O, Mikulincer M, Gillath O (2004) The multidimensional driving style inventory\u2014scale construct and validation. Accid Anal Prev 36(3):323\u2013332. https:\/\/doi.org\/10.1016\/S0001-4575(03)00010-1","journal-title":"Accid Anal Prev"},{"key":"1740_CR77","doi-asserted-by":"publisher","first-page":"88","DOI":"10.5539\/mas.v8n1p88","volume":"8","author":"R Vaiana","year":"2014","unstructured":"Vaiana R, Iuele T, Astarita V, Caruso MV, Tassitani A, Zaffino C, Giofr\u00e9 V (2014) Driving behavior and traffic safety: an acceleration-based safety evaluation procedure for smartphones. Mod Appl Sci 8:88\u201396. https:\/\/doi.org\/10.5539\/mas.v8n1p88","journal-title":"Mod Appl Sci"},{"key":"1740_CR78","doi-asserted-by":"publisher","unstructured":"van Huysduynen H, Terken J, Eggen B (2018) The relation between self-reported driving style and driving behaviour. A simulator study. Transp Res Part F Traffic Psychol Behav 56. https:\/\/doi.org\/10.1016\/j.trf.2018.04.017","DOI":"10.1016\/j.trf.2018.04.017"},{"key":"1740_CR79","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1109\/IVS.2013.6629603","volume":"2013","author":"M Van Ly","year":"2013","unstructured":"Van Ly M, Martin S, Trivedi MM (2013) Driver classification and driving style recognition using inertial sensors. IEEE Intell Veh Symposium (IV) 2013:1040\u20131045. https:\/\/doi.org\/10.1109\/IVS.2013.6629603","journal-title":"IEEE Intell Veh Symposium (IV)"},{"key":"1740_CR80","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.aap.2015.07.007","volume":"84","author":"J Wang","year":"2015","unstructured":"Wang J, Zheng Y, Li X, Yu C, Kodaka K, Li K (2015) Driving risk assessment using near-crash database through data mining of tree-based model. Accid Anal Prev 84:54\u201364. https:\/\/doi.org\/10.1016\/j.aap.2015.07.007","journal-title":"Accid Anal Prev"},{"key":"1740_CR81","doi-asserted-by":"publisher","unstructured":"Wang P, Fu Y, Zhang J, Wang P, Zheng Y, Aggarwal C (2018) You are how you drive: peer and temporal-aware representation learning for driving behavior analysis. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2457\u20132466. https:\/\/doi.org\/10.1145\/3219819.3219985","DOI":"10.1145\/3219819.3219985"},{"key":"1740_CR82","doi-asserted-by":"publisher","unstructured":"Wang Q, Zhang R, Wang Y, Lv S (2020) Machine learning-based driving style identification of truck drivers in open-pit mines. Electronics 9(1). https:\/\/doi.org\/10.3390\/electronics9010019","DOI":"10.3390\/electronics9010019"},{"key":"1740_CR83","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/VPPC.2011.6043061","volume":"2011","author":"R Wang","year":"2011","unstructured":"Wang R, Lukic SM (2011) Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles. IEEE Veh Power Prop Conf 2011:1\u20137. https:\/\/doi.org\/10.1109\/VPPC.2011.6043061","journal-title":"IEEE Veh Power Prop Conf"},{"issue":"5","key":"1740_CR84","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1109\/THMS.2017.2736948","volume":"47","author":"W Wang","year":"2017","unstructured":"Wang W, Xi J, Chong A, Li L (2017) Driving style classification using a semisupervised support vector machine. IEEE Trans Human Mach Syst 47(5):650\u2013660. https:\/\/doi.org\/10.1109\/THMS.2017.2736948","journal-title":"IEEE Trans Human Mach Syst"},{"issue":"8","key":"1740_CR85","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1109\/TITS.2018.2870525","volume":"20","author":"W Wang","year":"2019","unstructured":"Wang W, Xi J, Zhao D (2019) Driving style analysis using primitive driving patterns with Bayesian nonparametric approaches. IEEE Trans Intell Transp Syst 20(8):2986\u20132998. https:\/\/doi.org\/10.1109\/TITS.2018.2870525","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1740_CR86","doi-asserted-by":"publisher","unstructured":"Wang X, Wang H (2020) Cluster analysis for driving behavior of dangerous goods transportation based on data mining. IEEE Access, PP 1. https:\/\/doi.org\/10.1109\/ACCESS.2020.2964648","DOI":"10.1109\/ACCESS.2020.2964648"},{"issue":"4","key":"1740_CR87","first-page":"42","volume":"36","author":"H Wen","year":"2018","unstructured":"Wen H, Yang XM, Wu CZ (2018) Analysis of driving behavior characteristics of commercial vehicles under big data environment. Traf Inf Security 36(4):42\u201350","journal-title":"Traf Inf Security"},{"key":"1740_CR88","doi-asserted-by":"publisher","unstructured":"Wu M, Zhang S, Dong Y (2016) A novel model-based driving behavior recognition system using motion sensors. Sensors 16(10). https:\/\/doi.org\/10.3390\/s16101746","DOI":"10.3390\/s16101746"},{"issue":"3","key":"1740_CR89","doi-asserted-by":"publisher","first-page":"263","DOI":"10.15888\/j.cnki.csa.006256","volume":"27","author":"ZH Wu","year":"2018","unstructured":"Wu ZH, Wu ZC, Zhang J, Chen S, Chen J (2018) Research on driving behavior evaluation based on fuzzy c-means and neural network. Computer Sys Appl 27(3):263\u2013267. https:\/\/doi.org\/10.15888\/j.cnki.csa.006256","journal-title":"Computer Sys Appl"},{"key":"1740_CR90","doi-asserted-by":"publisher","unstructured":"Wu ZX, He YT, Yu LJ, Fu L, Chen P (2018) Research on driving style recognition algorithm based on big data. Autom Technol 10:10\u201315. https:\/\/doi.org\/10.19620\/j.cnki.1000-3703.20181053","DOI":"10.19620\/j.cnki.1000-3703.20181053"},{"key":"1740_CR91","doi-asserted-by":"publisher","unstructured":"W\u00fcrtz S, G\u00f6hner U (2020) Driving style analysis using recurrent neural networks with LSTM cells 11:1. https:\/\/doi.org\/10.12720\/jait.11.1.1-9","DOI":"10.12720\/jait.11.1.1-9"},{"key":"1740_CR92","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.aap.2017.11.010","volume":"116","author":"L Yang","year":"2018","unstructured":"Yang L, Ma R, Zhang HM, Guan W, Jiang S (2018) Driving behavior recognition using EEG data from a simulated car-following experiment. Accid Anal Prev 116:30\u201340. https:\/\/doi.org\/10.1016\/j.aap.2017.11.010","journal-title":"Accid Anal Prev"},{"key":"1740_CR93","doi-asserted-by":"publisher","unstructured":"Yang X-S (2010) A new metaheuristic bat-inspired algorithm 284. https:\/\/doi.org\/10.1007\/978-3-642-12538-6_6","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"1740_CR94","doi-asserted-by":"publisher","unstructured":"Yang X-S (2012) Bat algorithm for multi-objective optimisation. Int J Bio-Inspired Comput 3. https:\/\/doi.org\/10.1504\/IJBIC.2011.042259","DOI":"10.1504\/IJBIC.2011.042259"},{"key":"1740_CR95","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/VPPC46532.2019.8952462","volume":"2019","author":"X Zhang","year":"2019","unstructured":"Zhang X, Huang Y, Guo K, Li W (2019) Driving style classification for vehicle-following with unlabeled naturalistic driving data. IEEE Veh Power Prop Conf (VPPC) 2019:1\u20135. https:\/\/doi.org\/10.1109\/VPPC46532.2019.8952462","journal-title":"IEEE Veh Power Prop Conf (VPPC)"},{"issue":"4","key":"1740_CR96","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1260\/2046-0430.2.4.269","volume":"2","author":"X Zhu","year":"2013","unstructured":"Zhu X, Hu X, Chiu YC (2013) Design of driving behavior pattern measurements using smartphone global positioning system data. Int J Transp Sci Technol 2(4):269\u2013288. https:\/\/doi.org\/10.1260\/2046-0430.2.4.269","journal-title":"Int J Transp Sci Technol"},{"key":"1740_CR97","doi-asserted-by":"publisher","first-page":"3564835","DOI":"10.1155\/2020\/3564835","volume":"2020","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Tian D, Yan F (2020) Effectiveness of entropy weight method in decision-making. Math Probl Eng 2020:3564835. https:\/\/doi.org\/10.1155\/2020\/3564835","journal-title":"Math Probl Eng"}],"container-title":["Personal and Ubiquitous Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00779-023-01740-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00779-023-01740-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00779-023-01740-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T05:10:31Z","timestamp":1697778631000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00779-023-01740-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,29]]},"references-count":97,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["1740"],"URL":"https:\/\/doi.org\/10.1007\/s00779-023-01740-1","relation":{},"ISSN":["1617-4909","1617-4917"],"issn-type":[{"value":"1617-4909","type":"print"},{"value":"1617-4917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,29]]},"assertion":[{"value":"4 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2023","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"}}]}}