Driving Style Analysis Using Data Mining Techniques

By Z. Constantinescu, Cristian Marinoiu, Monica Vladoiu. 2010

229 citations recorded by the source corpus.

This paper investigates the modeling of the personal driving style of various vehicle drivers based on several driving parameters. The purpose of such an endeavor is to classify the drivers according to their risk-proneness within the larger context of increasing traffic safety, which is a major concern worldwide. This information is valuable especially for those involved in fleet management and it can be used to improve and to make safer the driving style of various individuals who serve within that fleet. Equally important, such information could help any driver to see the danger within his or her driving style. Cluster and principal component analyses from exploratory statistics have been used to identify and explain drivers grouping according to their driving behavior. The driving parameters (behavioral indices) are collected from urban traffic by an in-house developed GPS-based device and sent to a

Subfields: Automotive Engineering, Control and Systems Engineering, Safety, Risk, Reliability and Quality

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Jacob Lambert
2. Alexander Carballo
3. Ekim Yurtsever
4. Kazuya Takeda
5. Bo Gao
6. Dongpu Cao

In Control and Systems Engineering

1. Kazuya Takeda
2. Alexander Carballo
3. Fei-yue Wang
4. Ekim Yurtsever
5. Jacob Lambert

In Safety, Risk, Reliability and Quality

1. Kazuya Takeda
2. Alexander Carballo
3. Ekim Yurtsever
4. Jacob Lambert

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “Principal component analysis was used and five distinct driving classes were detected in an unsupervised manner in [233] and A GMM based driver model was used to identify individual drivers”
• Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey (2018, methodology): “Although the feedback suitability and effectiveness can be argued, it has been observed that the simple fact of being monitored already encourages drivers to correct their style [17] [18].”
• Data mining techniques and applications - A decade review from 2000 to 2011 (2012, unknown): “Real time vehicle tracking Constantinescu et al. (2010)”