Modeling Motorcyclists’ Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents

Abstract

Driving behavior is considered one of the most important factors in all road crashes, accounting for 40% of all fatal and serious accidents. Moreover, aggressive driving is the leading cause of traffic accidents that jeopardize human life and property. By evaluating data collected by various collection devices, it is possible to detect dangerous and aggressive driving, which is a huge step toward altering the situation. The utilization of driving data, which has arisen as a new tool for assessing the style of driving, has lately moved the concentration of aggressive recognition research. The goal of this study is to detect dangerous and aggressive driving profiles utilizing data gathered from motorcyclists and smartphone APPs that run on the Android operating system. A two-stage method is used: first, determine driver profile thresholds (rules), then differentiate between non-aggressive and aggressive driving and show the harmful conduct for producing the needed outcome. The data were collected from motorcycles using -Speedometer GPS-, an application based on the Android system, supplemented with spatiotemporal information. After the completion of data collection, preprocessing of the raw data was conducted to make them ready for use. The next steps were extracting the relevant features and developing the classification model, which consists of the transformation of patterns into features that are considered a compressed representation. Lastly, this study discovered a collection of key characteristics which might be used to categorize driving behavior as aggressive, normal, or dangerous. The results also revealed major safety issues related to driving behavior while riding a motorcycle, providing valuable insight into improving road safety and reducing accidents.

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“…On the other side, unsupervised learning uses unlabeled datasets and groups them into separate clusters by describing their structure depending on similar characteristics [54]. [26] smartphone accelerometer Statistical analysis Žylius [27] not specified accelerometer Random Forest algorithm Chhabra et al [29] smartphone accelerometer and gyroscope Statistical analysis Moukafih et al [30] smartphone accelerometer LTSM-FCN Azadani and Boukerche [31] in-vehicle sensors not specified DeepConvLSTM Schlegel et al [32] not specified accelerometer and gyroscope Hyper-dimensional computing Abdulwahid et al [33] smartphone accelerometer Statistical analysis Monselise and Yang [34] not specified accelerometer and gyroscope kNN algorithm Romero et al [35] MPU-6050 accelerometer and gyroscope ANN Brahim et al [59] smartphone accelerometer and gyroscope GBDT and LSTM Eren et al [65] smartphone accelerometer and gyroscope Statistical analysis…”