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Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2016 / Cilt: 22 - Sayı: 5

Transportation mode detection by using smartphone sensors and machine learning

Akıllı telefon algılayıcıları ve makine öğrenmesi kullanılarak ulaşım türü tespiti

Ensar Arif Sağbaş
Serkan Ballı
Pages
376–383
Publication Date
2016-10-20
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Best result was obtained from GPS, accelerometer and gyroscope sensor combination and Random Forest method with 99.4% accuracy rate.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is to detect transportation modes of the users by using smartphone sensors.
📊
Key Findings & Data:Sensor data were tagged with 12 second interval and 2500 pattern were obtained. 14 features were acquired from the dataset.
💡
Conclusion & Impact:Best result was obtained from GPS, accelerometer and gyroscope sensor combination and Random Forest method with 99.4% accuracy rate.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Ulaşım türü · Sınıflandırma · Akıllı telefon

Abstract

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.

Özet

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.

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Cite This Article

Sağbaş, E. A., Ballı, S. (2016). Transportation mode detection by using smartphone sensors and machine learning. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 22 - Sayı: 5, 376–383. https://ajindex.com/articles/42604

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