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Data Analysis on Powered Two Wheelers Riders’ Behaviour using Machine Learning


Mobyen Uddin Ahmed, Abderrahmane Boubezoul , Nils Göran Gustav Forsström , Nabaz Sherif , Daniel Stenekap , Stephane Espie , Anton Sundström , Rasmus Södergren

Publication Type:

Conference/Workshop Paper


First International Conference on Advances in Signal Processing and Artificial Intelligence


Analyzing powered two-wheeler rider behavior, i.e. classification of riding patterns based on 3-D accelerometer/gyroscope sensors mounted on motorcycles is challenging. This paper presents machine learning approach to classify four different riding events performed by powered two wheeler riders’ as a step towards increasing traffic safety. Three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been used to classify riding patterns. The classification is conducted based on features extracted in time and frequency domains from accelerometer/gyroscope sensors signals. A comparison result between different filter frequencies, window sizes, features sets, as well as machine learning algorithms is presented. According to the results, the Random Forest method performs most consistently through the different data sets and scores best.


author = {Mobyen Uddin Ahmed and Abderrahmane Boubezoul and Nils G{\"o}ran Gustav Forsstr{\"o}m and Nabaz Sherif and Daniel Stenekap and Stephane Espie and Anton Sundstr{\"o}m and Rasmus S{\"o}dergren},
title = {Data Analysis on Powered Two Wheelers Riders’ Behaviour using Machine Learning},
month = {March},
year = {2019},
booktitle = {First International Conference on Advances in Signal Processing and Artificial Intelligence},
url = {}