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Series of Scientific Lectures 2019-2020 – Lecture given by TTLog Director

Series of Scientific Lectures 2019-2020 – Lecture given by TTLog Director

Title: The impact of text-reading while driving, through cluster modeling analysis and decision trees.

Lecturer: Ass. Prof. Eftihia Nathanail

Date, Time: Wednesday 11/12/2019, 14:00

Location: Αίθουσα A2, Civil Engineering Department

Abstract: Driver distraction is one of the most significant causes of road crashes. The objectives of this

paper were to (i) investigate the correlation of text reading and text sending with demographic

characteristics, gender, age, race, highest educational level and driving experience was

investigated along with the influence of other driving habits on these dependent variables, and

(ii) develop driver behavior prediction models such as probability for lane encroachment while

text-reading and driving.

The research was based on a sample of 203 taxi drivers in Honolulu, who drove a simulator

under non-distraction and text-reading scenarios. Eight driving performance measures were

recorded. The same drivers completed a survey questionnaire which included

sociodemographic information and recorded drivers’ preferences and driving habits. Nonparametric

testing was used to define clusters, based on gender, age, experience, educational

level and race. The correlation was investigated of the driving behavior and the driving

performance indicators.

Ordinal regression analysis was implemented for text reading, considering demographic

parameters only, as independent variables, then enriched with driver habits, behavioral belief,

risk comprehension, descriptive norms and control beliefs. One of the statistically significant

results was that race appears to be a strong predictor of large effects during text reading.

Multinomial logit regression was also used and the lane encroachment incidents demonstrated

the best model fit with a classification accuracy of 87.6%. Text reading creates distraction,

which affects driving behavior. Age and race are the most important influencers in distraction



Download the abstract here