{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T11:55:23Z","timestamp":1790423723060,"version":"4.1.0"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,12,30]],"date-time":"2017-12-30T00:00:00Z","timestamp":1514592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people\u2019s daily activities including transportation types and duration by taking advantage of individual\u2019s smartphones. This paper introduces a novel segment-based transport mode detection architecture in order to improve the results of traditional classification algorithms in the literature. The proposed post-processing algorithm, namely the Healing algorithm, aims to correct the misclassification results of machine learning-based solutions. Our real-life test results show that the Healing algorithm could achieve up to 40% improvement of the classification results. As a result, the implemented mobile application could predict eight classes including stationary, walking, car, bus, tram, train, metro and ferry with a success rate of 95% thanks to the proposed multi-tier architecture and Healing algorithm.<\/jats:p>","DOI":"10.3390\/s18010087","type":"journal-article","created":{"date-parts":[[2018,1,3]],"date-time":"2018-01-03T12:00:06Z","timestamp":1514980806000},"page":"87","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection"],"prefix":"10.3390","volume":"18","author":[{"given":"M.","family":"Guvensan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Yildiz Technical University, 34220 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Burak","family":"Dusun","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Yildiz Technical University, 34220 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baris","family":"Can","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Yildiz Technical University, 34220 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.","family":"Turkmen","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Yildiz Technical University, 34220 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ranasinghe, S., Al Machot, F., and Mayr, H.C. (2016). A review on applications of activity recognition systems with regard to performance and evaluation. Int. J. Distrib. Sens. Netw., 12, 1550147716665520.","DOI":"10.1177\/1550147716665520"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Garg, S., Singh, P., Ramanathan, P., and Sen, R. (2014, January 2\u20135). VividhaVahana: smartphone based vehicle classification and its applications in developing region. Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, London, UK.","DOI":"10.4108\/icst.mobiquitous.2014.257982"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15974","DOI":"10.3390\/s150715974","article-title":"Smart city mobility application\u2014Gradient boosting trees for mobility prediction and analysis based on crowdsourced data","volume":"15","author":"Semanjski","year":"2015","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.pmcj.2017.06.002","article-title":"Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing","volume":"40","author":"Singh","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, J., Gon\u00e7alves, J.S., Rossetti, R.J., and Olaverri-Monreal, C. (2014, January 8\u201311). Smartphone sensor platform to study traffic conditions and assess driving performance. Proceedings of the 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.","DOI":"10.1109\/ITSC.2014.6958106"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.compenvurbsys.2014.07.011","article-title":"Urban sensing: Using smartphones for transportation mode classification","volume":"53","author":"Shin","year":"2015","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_7","unstructured":"Ohmori, N., Nakazato, M., Harata, N., Sasaki, K., and Nishii, K. (2006, January 22\u201326). Activity diary surveys using GPS mobile phones and PDA. Proceedings of the 85th Annual Meeting of the Transportation Research Board, Washington, DC, USA."},{"key":"ref_8","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 11th ACM Conference on Embedded Networked Sensor Systems, Roma, Italy.","DOI":"10.1145\/2517351.2517367"},{"key":"ref_9","unstructured":"Nikolic, M., and Bierlaire, M. (2017, January 18). Review of transportation mode detection approaches based on smartphone data. Proceedings of the 17th Swiss Transport Research Conference, Ascona, Switzerland. number EPFL-CONF-229181."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1080\/03081060.2015.1127540","article-title":"Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data","volume":"39","author":"Feng","year":"2016","journal-title":"Transp. Plan. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ohashi, H., Akiyama, T., Yamamoto, M., and Sato, A. (2013, January 5\u20138). Modality classification method based on the model of vibration generation while vehicles are running. Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science, Orlando, FL, USA.","DOI":"10.1145\/2533828.2533829"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12588","DOI":"10.3390\/s120912588","article-title":"Comprehensive context recognizer based on multimodal sensors in a smartphone","volume":"12","author":"Han","year":"2012","journal-title":"Sensors"},{"key":"ref_14","unstructured":"Widhalm, P., Nitsche, P., and Br\u00e4ndie, N. (2012, January 11\u201315). Transport mode detection with realistic smartphone sensor data. Proceedings of the 2012 21st International Conference on Pattern Recognition (ICPR), Tsukuba, Japan."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Stenneth, L., Wolfson, O., Yu, P.S., and Xu, B. (2011, January 1\u20134). Transportation mode detection using mobile phones and GIS information. Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information System, Chicago, IL, USA.","DOI":"10.1145\/2093973.2093982"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Stenneth, L., Thompson, K., Stone, W., and Alowibdi, J. (2012, January 16\u201319). Automated transportation transfer detection using GPS enabled smartphones. Proceedings of the 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), Anchorage, AK, USA.","DOI":"10.1109\/ITSC.2012.6338603"},{"key":"ref_17","first-page":"87","article-title":"Multi-stage approach to travel-mode segmentation and classification of gps traces","volume":"38","author":"Zhang","year":"2011","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Brunauer, R., Hufnagl, M., Rehrl, K., and Wagner, A. (2013, January 6\u20139). Motion pattern analysis enabling accurate travel mode detection from GPS data only. Proceedings of the Intelligent Transportation Systems-(ITSC), The Hague, The Netherlands.","DOI":"10.1109\/ITSC.2013.6728265"},{"key":"ref_19","unstructured":"Witayangkurn, A., Horanont, T., Ono, N., Sekimoto, Y., and Shibasaki, R. (2013, January 2\u20135). Trip reconstruction and transportation mode extraction on low data rate gps data from mobile phone. Proceedings of the International Conference on Computers in Urban Planning and Urban Management (CUPUM 2013), Utrecht, The Netherlands."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, S., Chen, C., and Ma, J. (2010, January 17\u201318). Accelerometer based transportation mode recognition on mobile phones. Proceedings of the 2010 Asia-Pacific Conference on Wearable Computing Systems (APWCS), Shenzhen, China.","DOI":"10.1109\/APWCS.2010.18"},{"key":"ref_21","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 on Wireless Days (WD), Dublin, Ireland.","DOI":"10.1109\/WD.2012.6402818"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2523","DOI":"10.1002\/wcm.2702","article-title":"Context-aware Android applications through transportation mode detection techniques","volume":"16","author":"Bedogni","year":"2016","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sankaran, K., Zhu, M., Guo, X.F., Ananda, A.L., Chan, M.C., and Peh, L.S. (2014, January 3\u20136). Using mobile phone barometer for low-power transportation context detection. Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, Memphis, TN, USA.","DOI":"10.1145\/2668332.2668343"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lan, G., Xu, W., Khalifa, S., Hassan, M., and Hu, W. (2016, January 14\u201318). Transportation mode detection using kinetic energy harvesting wearables. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Sydney, Australia.","DOI":"10.1109\/PERCOMW.2016.7457048"},{"key":"ref_25","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_26","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_27","first-page":"13","article-title":"Using mobile phones to determine transportation modes","volume":"6","author":"Reddy","year":"2010","journal-title":"ACM Trans. Sens. Netw. (TOSN)"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lorintiu, O., and Vassilev, A. (2016, January 1\u20134). Transportation mode recognition based on smartphone embedded sensors for carbon footprint estimation. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795875"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"de MS Quintella, C.A., Andrade, L.C., and Campos, C.A.V. (2016, January 1\u20134). Detecting the transportation mode for context-aware systems using smartphones. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795921"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1080\/15472450.2013.824762","article-title":"Real-time transportation mode detection using smartphones and artificial neural networks: Performance comparisons between smartphones and conventional global positioning system sensors","volume":"18","author":"Byon","year":"2014","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1016\/j.sbspro.2012.06.1080","article-title":"A strategy on how to utilize smartphones for automatically reconstructing trips in travel surveys","volume":"48","author":"Nitsche","year":"2012","journal-title":"Procedia-Soc. Behav. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s00779-010-0293-9","article-title":"Preprocessing techniques for context recognition from accelerometer data","volume":"14","author":"Figo","year":"2010","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Nick, T., Coersmeier, E., Geldmacher, J., and Goetze, J. (2010, January 18\u201323). Classifying means of transportation using mobile sensor data. Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain.","DOI":"10.1109\/IJCNN.2010.5596549"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"44","DOI":"10.3141\/2383-06","article-title":"Using smartphones and sensor technologies to automate collection of travel data","volume":"2383","author":"Abdulazim","year":"2013","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cardoso, N., Madureira, J., and Pereira, N. (2016, January 14\u201317). Smartphone-based transport mode detection for elderly care. Proceedings of the 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), Munich, Germany.","DOI":"10.1109\/HealthCom.2016.7749465"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.compenvurbsys.2016.03.001","article-title":"Making pervasive sensing possible: Effective travel mode sensing based on smartphones","volume":"58","author":"Zhou","year":"2016","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Das, R.D., and Winter, S. (2016). Detecting urban transport modes using a hybrid knowledge driven framework from GPS trajectory. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5110207"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bloch, A., Erdin, R., Meyer, S., Keller, T., and de Spindler, A. (2015, January 15\u201318). Battery-Efficient Transportation Mode Detection on Mobile Devices. Proceedings of the 2015 16th IEEE International Conference on Mobile Data Management (MDM), Pittsburgh, PA, USA.","DOI":"10.1109\/MDM.2015.16"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2921","DOI":"10.1109\/TITS.2016.2530999","article-title":"Online travel mode identification using smartphones with battery saving considerations","volume":"17","author":"Su","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Buber, E., and Guvensan, A.M. (2014, January 21\u201324). Discriminative time domain features for activity recognition on a mobile phone. Proceedings of the 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore.","DOI":"10.1109\/ISSNIP.2014.6827651"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: an update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_43","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\/1\/87\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:56:05Z","timestamp":1760208965000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/1\/87"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,30]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["s18010087"],"URL":"https:\/\/doi.org\/10.3390\/s18010087","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,30]]}}}