Transportation mode detection by using smartphone sensors and machine learning

Yıl 2016, Cilt: 22 Sayı: 5, 376 - 383, 20.10.2016

Ensar Arif Sağbaş , Serkan Ballı

Öz

The aim of this study is to detect transportation modes of the users by using smartphone sensors. Therefore, GPS (Global Positioning System), accelerometer and gyroscope sensor data have been collected while walking, running, cycling and travelling by bus or by car from the smartphone of the user. Sensor data were tagged with 12 second interval and 2500 pattern were obtained. 14 features were acquired from the dataset. Machine learning methods were tested on the dataset. Best result was obtained from GPS, accelerometer and gyroscope sensor combination and Random Forest method with 99.4% accuracy rate.

Akıllı telefon algılayıcıları ve makine öğrenmesi kullanılarak ulaşım türü tespiti

Öz

Bu çalışmada akıllı telefon algılayıcıları kullanılarak kullanıcıların ulaşım türü tespitinin yapılması amaçlanmaktadır. Bunun için kullanıcıdan yürürken, koşarken, bisiklet sürerken, araba veya otobüs ile seyahat ederken GPS (Global Positioning System), ivmeölçer ve jiroskop algılayıcılarından elde edilen veriler toplanmıştır. Veriler 12’şer saniyelik aralıklarla etiketlenmiş ve toplamda 2500 örüntü elde edilmiştir. Bu verilerden 14 öznitelik elde edilmiştir. Oluşturulan veri seti ile makine öğrenmesi yöntemleri kullanılarak testler gerçekleştirilmiştir. En iyi sonuç GPS, ivmeölçer ve jiroskop algılayıcılarının kombinasyonundan, %99.4 doğruluk oranı ile Random Forest yönteminden elde edilmiştir.

Anahtar Kelimeler

Ulaşım türü, Sınıflandırma, Akıllı telefon, Algılayıcı verisi

Kaynakça

• Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT. “A survey of mobile phone sensing”. Communications Magazine, 48(9), 140-150, 2010.
• Reddy S, Burke J, Estrin D, Hansen M, Srivastava M. “Determining transportation mode on mobile phones”. Wearable Computers, 12th IEEE International Symposium, Pittsburgh, USA, 28 September-1 October 2008.
• Zheng Y, Liu L, Wang L, Xie X. “Learning transportation mode from raw GPS data for geographic applications on the web”. 17th World Wide Web Conference, Beijing, China, 21-25 April 2008.
• Győrbíró N, Fábián Á, Hományi G. “An activity recognition system for mobile phones”. Mobile Networks and Applications, 14(1), 82-91, 2009.
• Wang S, Chen C, Ma J. “Accelerometer based transportation mode recognition on mobile phone”. 2010 Asia-Pacific Conference, Shenzhen, China, 17-18 April 2010.
• Stenneth L, Wolfson O, Yu FS, Xu B. “Transportation mode detection using mobile phones and GIS information”. 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, IL, USA, 1-4 November 2011.
• Lara OD, Pérez AJ, Labrador MA, Posada JD. “Centinela: A human activity recognition system based on acceleration and vital sign data”. Pervasive and Mobile Computing, 8(5), 717-729, 2012.
• Widhalm P, Nitsche P, Brandie N. “Transport mode detection with realistic Smartphone sensor data”. 21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 11-15 November 2012.
• Kose M, Incel OD, Ersoy C. “Online human activity recognition on smart phones”. 2nd International Workshop on Mobile Sensing. Beijing, China, 16 April 2012.
• Bolbol A, Cheng T, Tsapakis I, Haworth J. “Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification”. Computers, Environment and Urban Systems, 36(6), 526-537, 2012.
• Hemminki S, Nurmi P, Tarkoma S. “Accelerometer-based transportation mode detection on smartphones”. Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, Rome, Italy, 11-14 November 2013.
• Feng T, Timmermans HJP. “Transportation mode recognition using GPS and accelerometer data”. Transportation Research Part C: Emerging Technologies, 37, 118-130, 2013.
• Ellis K, Godbole S, Marshall S, Lanckriet G, Staudenmayer J, Kerr J. “Identifying active travel behaviors in challenging environments using GPS, accelerometers, and machine learning algorithms”. Frontiers in Public Health, 2(36), 1-8, 2014.
• Shin D, Aliaga D, Tunçer B, Arisona SM, Kim S, Zünd D, Schmitt G. “Urban sensing: Using smartphones for transportation Environment and Urban Systems, 53, 76-86, 2015.
• classification”. Computers,
• Sökün H, Kalkan H, Cetişli B. “Classification of physical activities using accelerometer signals”. Signal Processing and Communications Applications Conference (SIU), Muğla, Turkey, 18-20 April 2012.
• Xia H, Qiao Y, Jian J, Chang Y. “Using smart phone sensors to detect transportation modes”. Sensors, 14(11), 20843-20865, 2014.
• El-Rabbany A. Introduction to GPS: The Global Positioning System. 2nd ed. Norwood, USA, Artech House, 2002.
• Su X, Tong H, Ji P. “Activity recognition with smartphone sensors”. Tsinghua Science and Technology, 19(3), 235-249, 2014.
• Sağbaş E.A, Ballı S. “Akıllı Telefon Sensörlerinin Kullanımı ve Ham Sensör Verilerine Erişim”. Akademik Bilişim Konferansı, Eskişehir, Türkiye, 4-6 Şubat 2015.
• Chandra B, Gupta M, Gupt MP. “Robust approach for estimating probabilities in Naive-Bayes classifier”. Pattern Recognition and Machine Intelligence. Kolkata, India, 18-22 December 2007.
• Korb KB, Nicholson AE. Bayesian Artificial Intelligence. 2nd ed. Boca Raton, FL, USA, CRC Press, 2011.
• Feng T, Timmermans HJP. “Comparative evaluation of algorithms for GPS data imputation”. 13th WCTR, Rio de Janerio, Brazil, 15-18 July 2010.
• Akman M, Genç Y, Ankaralı H. “Random forest yöntemi ve sağlık alanında bir uygulama”. Türkiye Klinikleri, 3(1), 36-48, 2011.
• Samsung. “Galaxy Note 2”. http://www.samsung.
• com/tr/consumer/mobile-devices/smartphones/galaxy
• note/GT-N7100RWDTUR (08.07.2015).
• Garner SR. “Weka: The waikato environment for knowledge analysis”. 2nd New Zealand Computer Science Research Students Conference, Hamilton, New Zealand, 18-21 April 1995. [26] Weka. “Use WEKA in your Java code”. https://weka.wikispaces.com/Use+WEKA+in+your+Java +code (08.07.2015).