A High-Precision Driving Behavior Classification Framework using Convolutional Neural Networks based on Vehicle Telematics Data

Sami  Shaffiee Haghshenas; Sina Shaffiee Haghshenas; Giuseppe  Guido; Vittorio  Astarita; Saeid Jafarzadeh Ghoushchi

doi:10.31181/sems41202680

Authors

Sami Shaffiee Haghshenas Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy Author https://orcid.org/0000-0002-9301-8677
Sina Shaffiee Haghshenas Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy Author https://orcid.org/0000-0003-2859-3920
Giuseppe Guido Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy Author https://orcid.org/0000-0001-7570-4123
Vittorio Astarita Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy Author https://orcid.org/0000-0002-3673-9814
Saeid Jafarzadeh Ghoushchi Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran Author https://orcid.org/0000-0003-3665-9010

DOI:

https://doi.org/10.31181/sems41202680

Keywords:

Driving Behavior, Road Safety, Fuel Consumption, Classification, Convolutional Neural Networks

Abstract

The increase in road travel has a significant impact on road safety, fuel consumption, and environmental pollution. Continuous efforts have been made to increase road safety and reduce fuel consumption and pollution. One of the factors that is of great importance in this regard is driver behavior. In this study, an advanced artificial intelligence model using convolutional neural networks is used to accurately classify driving behaviors under speed threshold criteria. In this modeling, a dataset with 370 data points is used with five inputs. The input data for modeling and predicting driver behaviors include fuel flow rate/hour, engine speed, speed, acceleration, and grade. Four classes (labels) are considered: normal (class 1), moderate (class 2), aggressive (class 3), and dangerous (class 4). The obtained results indicate that the convolutional neural network model achieved very high classification performance during the modeling process. In addition to classification accuracy, the proposed framework contributes by establishing a structured behavior classification scheme based on telematics data and speed-threshold criteria, which can support intelligent safety monitoring, eco-driving assessment, and transportation management applications. Finally, the most important factors that affect driver behavior classification are the speed of cars and engine speed.

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References

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