{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T06:30:09Z","timestamp":1775716209407,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,3,29]],"date-time":"2018-03-29T00:00:00Z","timestamp":1522281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Vietnam National University, Hanoi (VNU) under the project no. QG 17.39"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers\u2019 vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Na\u00efve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art.<\/jats:p>","DOI":"10.3390\/s18041036","type":"journal-article","created":{"date-parts":[[2018,3,29]],"date-time":"2018-03-29T12:51:56Z","timestamp":1522327916000},"page":"1036","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones"],"prefix":"10.3390","volume":"18","author":[{"given":"Dang-Nhac","family":"Lu","sequence":"first","affiliation":[{"name":"University of Engineering and Technology, Vietnam National University in Hanoi (VNU-UET), Hanoi 123105, Vietnam"},{"name":"Academy of Journalism and Communication, Hanoi 123105, Vietnam"}]},{"given":"Duc-Nhan","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Posts and Telecommunications Institute of Technology in Hanoi (PTIT), Hanoi 151100, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8599-2998","authenticated-orcid":false,"given":"Thi-Hau","family":"Nguyen","sequence":"additional","affiliation":[{"name":"University of Engineering and Technology, Vietnam National University in Hanoi (VNU-UET), Hanoi 123105, Vietnam"}]},{"given":"Ha-Nam","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Information Technology Institute, Vietnam National University in Hanoi (VNU-ITI), Hanoi 123105, Vietnam"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,29]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2017, January 09). Global Status Report on Road Safety. Available online: http:\/\/www.who.int\/violence_injury_prevention\/road_safety_status\/2015\/en\/."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bedogni, L., Di Felice, M., and Bononi, L. (2012, January 21\u201323). By train or by car? Detecting the user\u2019s motion type through smartphone sensors data. Proceedings of the 2012 IFIP Wireless Days, Dublin, Ireland.","DOI":"10.1109\/WD.2012.6402818"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hemminki, S., Nurmi, P., and Tarkoma, S. (2013, January 11\u201315). Accelerometer-based transportation mode detection on smartphones. Proceedings of the SenSys \u201913, 11th ACM Conference on Embedded Networked Sensor Systems, Roma, Italy.","DOI":"10.1145\/2517351.2517367"},{"key":"ref_4","unstructured":"Widhalm, P., Nitsche, P., and Br\u00e4ndie, N. (2012, January 11\u201315). Transport mode detection with realistic smartphone sensor data. Proceedings of the 21st International Conference on Pattern Recognition (ICPR), Tsukuba, Japan."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shafique, M.A., and Hato, E. (2016). Travel Mode Detection with Varying Smartphone Data Collection Frequencies. Sensors, 16.","DOI":"10.3390\/s16050716"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fang, S.H., Liao, H.H., Fei, Y.X., Chen, K.H., Huang, J.W., Lu, Y.D., and Tsao, Y. (2016). Transportation modes classification using sensors on smartphones. Sensors, 16.","DOI":"10.3390\/s16081324"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Wang, Y., Fu, K., and Wu, F. (2017). Identifying different transportation modes from trajectory data using tree-based ensemble classifiers. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6020057"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Guvensan, M.A., Dusun, B., Can, B., and Turkmen, H.I. (2018). A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection. Sensors, 18.","DOI":"10.3390\/s18010087"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1080\/01441647.2016.1246489","article-title":"Transportation mode detection\u2014An in-depth review of applicability and reliability","volume":"37","author":"Prelipcean","year":"2017","journal-title":"Transp. Rev."},{"key":"ref_10","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 2013 IEEE Intelligent Vehicles Symposium, Gold Coast, Australia.","DOI":"10.1109\/IVS.2013.6629603"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Aljaafreh, A., Alshabatat, N., and Al-Din, M.N. (2012, January 24\u201327). Driving style recognition using fuzzy logic. Proceedings of the 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Istanbul, Turkey.","DOI":"10.1109\/ICVES.2012.6294318"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4264","DOI":"10.1109\/TVT.2013.2263400","article-title":"Context-aware driver behavior detection system in intelligent transportation systems","volume":"62","author":"Zedan","year":"2013","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bergasa, L.M., Almer\u00eda, D., Almaz\u00e1n, J., Yebes, J.J., and Arroyo, R. (2014, January 8\u201311). Drivesafe: An app for alerting inattentive drivers and scoring driving behaviors. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium Proceedings, Dearborn, MI, USA.","DOI":"10.1109\/IVS.2014.6856461"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dai, J., Teng, J., Bai, X., and Shen, Z. (2010, January 22\u201325). Mobile phone based drunk driving detection. Proceedings of the 2010 4th International Conference on Pervasive Computing Technologies for Healthcare, Munich, Germany.","DOI":"10.4108\/ICST.PERVASIVEHEALTH2010.8901"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/MITS.2014.2328673","article-title":"Driver behavior profiling using smartphones: A low-cost platform for driver monitoring","volume":"7","author":"Castignani","year":"2015","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Johnson, D.A., and Trivedi, M.M. (2011, January 5\u20137). Driving style recognition using a smartphone as a sensor platform. Proceedings of the 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, WA, USA.","DOI":"10.1109\/ITSC.2011.6083078"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1109\/TITS.2012.2187640","article-title":"Safe driving using mobile phones","volume":"13","author":"Fazeen","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Eren, H., Makinist, S., Akin, E., and Yilmaz, A. (2012, January 3\u20137). Estimating driving behavior by a smartphone. Proceedings of the 2012 IEEE Intelligent Vehicles Symposium (IV), Alcala de Henares, Spain.","DOI":"10.1109\/IVS.2012.6232298"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Z., Yu, J., Zhu, Y., Chen, Y., and Li, M. (2015, January 22\u201325). D3: Abnormal driving behaviors detection and identification using smartphone sensors. Proceedings of the 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Seattle, WA, USA.","DOI":"10.1109\/SAHCN.2015.7338354"},{"key":"ref_20","first-page":"357","article-title":"Mobile online activity recognition system based on smartphone sensors","volume":"538","author":"Lu","year":"2016","journal-title":"Adv. Inf. Commun. Technol."},{"key":"ref_21","first-page":"30","article-title":"A Novel Mobile Online Vehicle Status Awareness Method Using Smartphone Sensors","volume":"Volume 424","author":"Kim","year":"2017","journal-title":"International Conference on Information Science and Applications"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lu, D.N., Tran, T.B., Nguyen, D.N., Nguyen, T.H., and Nguyen, H.N. (2017). Abnormal Behavior Detection Based on Smartphone Sensors. Context-Aware Systems and Applications, and Nature of Computation and Communication, Springer.","DOI":"10.1007\/978-3-319-77818-1_19"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3141\/1784-01","article-title":"Analysis of crash precursors on instrumented freeways","volume":"1784","author":"Lee","year":"2002","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zaldivar, J., Calafate, C.T., Cano, J.C., and Manzoni, P. (2011, January 4\u20137). Providing accident detection in vehicular networks through OBD-II devices and Android-based smartphones. Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks, Bonn, Germany.","DOI":"10.1109\/LCN.2011.6115556"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Astarita, V., Guido, G., Mongelli, D.W.E., and Giofr\u00e8, V.P. (2014, January 8\u201310). Ecosmart and TutorDrive: Tools for fuel consumption reduction. Proceedings of the 2014 IEEE International Conference on Service Operations and Logistics, and Informatics, Qingdao, China.","DOI":"10.1109\/SOLI.2014.6960716"},{"key":"ref_26","first-page":"64","article-title":"Distributed Road Surface Condition Monitoring Using Mobile Phones","volume":"Volume 6905","author":"Perttunen","year":"2011","journal-title":"Proceedings of the Ubiquitous Intelligence and Computing\u20148th International Conference, UIC 2011"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bhoraskar, R., Vankadhara, N., Raman, B., and Kulkarni, P. (2012, January 3\u20137). Wolverine: Traffic and road condition estimation using smartphone sensors. Proceedings of the 2012 4th International Conference on Communication Systems and Networks, Bangalore, India.","DOI":"10.1109\/COMSNETS.2012.6151382"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1061\/(ASCE)TE.1943-5436.0000126","article-title":"Using GPS data to gain insight into public transport travel time variability","volume":"136","author":"Mazloumi","year":"2010","journal-title":"J. Transp. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"C\u00e1rdenas-Ben\u00edtez, N., Aquino-Santos, R., Maga\u00f1a-Espinoza, P., Aguilar-Velazco, J., Edwards-Block, A., and Medina Cass, A. (2016). Traffic Congestion Detection System through Connected Vehicles and Big Data. Sensors, 16.","DOI":"10.3390\/s16050599"},{"key":"ref_30","first-page":"754","article-title":"Non-recurrent traffic congestion detection on heterogeneous urban road networks","volume":"11","author":"Cheng","year":"2015","journal-title":"Transp. A Transp. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Araujo, R., Igreja, A., de Castro, R., and Araujo, R. (2012, January 3\u20137). Driving coach: A smartphone application to evaluate driving efficient patterns. Proceedings of the 2012 IEEE Intelligent Vehicles Symposium (IV), Alcala de Henares, Spain.","DOI":"10.1109\/IVS.2012.6232304"},{"key":"ref_32","first-page":"612","article-title":"Motosafe: Active safe system for digital forensics of motorcycle rider with android","volume":"2","author":"Condro","year":"2012","journal-title":"Int. J. Inf. Electron. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.3390\/s140406474","article-title":"Window Size Impact in Human Activity Recognition","volume":"14","author":"Banos","year":"2014","journal-title":"Sensors"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.medengphy.2015.04.005","article-title":"Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer","volume":"37","author":"Fida","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ma, C., Dai, X., Zhu, J., Liu, N., Sun, H., and Liu, M. (2017). DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration. Mob. Inf. Syst., 2017.","DOI":"10.1155\/2017\/9075653"},{"key":"ref_36","unstructured":"Li, F., Zhang, H., Che, H., and Qiu, X. (2016, January 1\u20134). Dangerous driving behavior detection using smartphone sensors. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1109\/TMC.2016.2618873","article-title":"Fine-grained abnormal driving behaviors detection and identification with smartphones","volume":"16","author":"Yu","year":"2016","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"J\u00fanior, J.F., Carvalho, E., Ferreira, B.V., de Souza, C., Suhara, Y., Pentland, A., and Pessin, G. (2017). Driver behavior profiling: An investigation with different smartphone sensors and machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0174959"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1049\/iet-its.2014.0248","article-title":"Survey of smartphone-based sensing in vehicles for intelligent transportation system applications","volume":"9","author":"Engelbrecht","year":"2015","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_40","unstructured":"Antoniou, A. (2006). Digital Signal Processing: Signal, Systems, and Filters, The McGraw-Hill."},{"key":"ref_41","unstructured":"Premerlani, W., and Bizard, P. (2009). Direction Cosine Matrix Imu: Theory, DCM. Technical Report."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/0013-4694(70)90143-4","article-title":"EEG analysis based on time domain properties","volume":"29","author":"Hjorth","year":"1970","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_43","first-page":"2012","article-title":"Tilt sensing using a three-axis accelerometer","volume":"1","author":"Pedley","year":"2013","journal-title":"Free Scale Semicond. Appl. Note"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pang, B., Lee, L., and Vaithyanathan, S. (2002, January 6\u20137). Thumbs up? Sentiment classification using machine-learning techniques. Proceedings of the Empirical Methods of Natural Language Processing (EMNLP\u201902), Philadelphia, PA, USA.","DOI":"10.3115\/1118693.1118704"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.14778\/2733004.2733015","article-title":"Big data small footprint: The design of a low-power classifier for detecting transportation modes","volume":"7","author":"Yu","year":"2014","journal-title":"Proc. VLDB Endow."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Vu, T.H., Dung, L., and Wang, J.C. (2016, January 15\u201319). Transportation Mode Detection on Mobile Devices Using Recurrent Nets. Proceedings of the 2016 ACM on Multimedia Conference, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2967249"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1036\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:59:02Z","timestamp":1760194742000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1036"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,29]]},"references-count":47,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["s18041036"],"URL":"https:\/\/doi.org\/10.3390\/s18041036","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,29]]}}}