Investigating the Temporal Evolution of Driving Safety Efficiency Using Data Collected from Smartphone Sensors

This paper attempts to shed light on the temporal evolution of driving safety efficiency with the aim to acquire insights useful for both driver’s and road safety improvement. Data exploited herein are collected from a sophisticated platform that uses smartphone device sensors during a naturalistic driving experiment, at which the driving behavior from a sample of two hundred (200) drivers during 7-months is continuously recorded in real time. The main driving behavior analytics taken into consideration for the driving assessment include distance travelled, acceleration, braking, speed and smartphone usage; these data serve as inputs in the models developed. Various statistical, econometric, optimization and machine learning techniques are applied on data collected to perform the analysis. Initial data analysis results to the most critical components of microscopic driving behaviour, which are used as inputs in the k-means algorithm to perform the clustering analysis. The main driving characteristics of each cluster are identified and lead to the conclusion that there are three main driving groups of the a) moderate drivers, b) unstable drivers and c) cautious drivers.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AND10 Standing Committee on Vehicle User Characteristics.
  • Corporate Authors:

    Transportation Research Board

    ,    
  • Authors:
    • Tselentis, Dimitrios I
    • Vlahogianni, Eleni I
    • Yannis, George
  • Conference:
  • Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 7p

Subject/Index Terms

Filing Info

  • Accession Number: 01697918
  • Record Type: Publication
  • Report/Paper Numbers: 19-03473
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:41AM