Applying Machine Learning for Improving Performance Classification on Driving Behavior

https://doi.org/10.22146/ijitee.56919

Ahmad Iwan Fadli(1*), Selo Sulistyo(2), Sigit Wibowo(3)

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author

Abstract


Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation.  It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.

Keywords


Cellular phone sensor data, Machine Learning Algorithms, Driving Behavior.

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References

(2018) World Health Organization - WHO website. [Online], https://www.who.int/publications/i/item/global-status-report-on-road-safety-2018, access date: Sept. 16, 2019.

C. Ma, X. Dai, J. Zhu, N. Liu, H. Sun, and M. Liu, “DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration,” Mobile Information Systems, vol. 2017. pp. 1–15, Mar. 2017.

L. Zhu, F. R. Yu, Y. Wang, B. Ning, and T. Tang, “Big Data Analytics in Intelligent Transportation Systems: A Survey,” IEEE Trans. Intell. Transp. Syst., vol. 20, pp. 383–398, Jan. 2019.

D. N. Lu, D. N. Nguyen, T. H. Nguyen, and H. N. Nguyen, “Vehicle mode and driving activity detection based on analyzing sensor data of smartphones,” Sensors (Switzerland), vol. 18, pp. 1–25, Mar. 2018.

X. Wu, X. Zhu, G. Q. Wu, and W. Ding, “Data mining with big data,” IEEE Trans. Knowl. Data Eng., vol. 26, pp. 97–107, Jan. 2014.

P. Patil, N. Yaligar, and S. Meena, “Comparision of Performance of Classifiers - SVM, RF and ANN in Potato Blight Disease Detection Using Leaf Images,” IEEE Int. Conf. Comput. Intell. Comput. Res. ICCIC, 2017, pp. 1–5

J. F. Júnior, E. Carvalho1, B. V. Ferreira1, C. D. Souza, Y. Suhara, A. Pentland, G. Pessina., “Driver behavior profiling: An investigation with different smartphone sensors and machine learning,” PLoS ONE., Vol. 12, pp. 1-16, Apr. 2017.

J. Yu, Z. Chen, Y. Zhu, Y. Jennifer Chen, L. Kong, and M. Li, “Fine-Grained Abnormal Driving Behaviors Detection and Identification with Smartphones,” IEEE Trans. Mob. Comput., Vol. 16, pp.1–14, 2017

K. T. Nguyen, F. Portet, and C. Garbay, “Dealing with Imbalanced data sets for Human Activity Recognition using Mobile Phone sensors,” Intelligent Environments., Vol. 3, pp 129-138, Jun. 2018.

A. Jahangiri and H. A. Rakha, “Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data,” IEEE Trans. Intell. Transp. Syst., Vol. 16, pp. 2406–2417, Oct. 2015.



DOI: https://doi.org/10.22146/ijitee.56919

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ISSN  : 2550-0554 (online)

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