Abstract

Human factors are crucial in causing traffic crashes and researchers try to identify and understand these factors to explore patterns of risky driving behavior and its consequences. It is clear that human factors play a crucial role in the causation of traffic accidents, and researchers are trying to identify and understand these factors. That is why it is important to explore the patterns that identify drivers prone to risky driving behavior and the possible negative consequences of that behavior At this aim, an experiment was conducted using the VERA dynamic driving simulator at the Road Safety Laboratory of the University of Naples Federico II. Speed profiles and lateral positions were studied. Furthermore, individual information for each participant was gathered through self-report questionnaires: 1) Dula Dangerous Driving Index (DDDI), 2) Traffic Locus of Control (T-LOC), and 3) Marlowe Crowne Social Desirability (MCSDS). The data of vehicle kinematics, such as speed and lateral positions profiles, and personality traits of drivers, gathered through self-report questionnaires, were studied using a combination of three techniques of analysis: Cluster analysis, Cronbach’s alpha, and principal component analysis. The combined use of these analysis methods enables a comprehensive examination of the data, allowing for a detailed analysis of both the speed and lateral position profiles as well as the responses from self-questionnaires. The cluster analysis results, carried out on speed and lateral position profile, have identified four driving styles for each parameter. Regarding speed, the following driving styles have been identified: 1) moderate speed; 2) design speed; 3) exceeding design speed of less than 20 km/h; and 4) exceeding design speed of not less than 20 km/h. As regards lateral position, the following behaviors have been identified: 1) driving near the shoulder; 2) driving near the axis; 3) driving moderately into the oncoming lane; and 4) driving into the opposing lane. The results of Cronbach's alpha, used to assess the internal consistency of the questionnaires, indicated that DDDI and T-LOC were reliable for describing driver behaviors, while MCSDS was not significant. Principal Component Analysis, performed for each sub-scale, identified new latent variables that describe driving behaviors and highlighted significant relations between the driving attitudes of drivers assessed through self-questionary tests and the performance parameters obtained through the driving simulator. A tendency to increase speed was observed in drivers who display aggressive behaviors, such as impatience, tailgating, road rage, or making rude gestures, as well as in those prone to experiencing negative emotions while driving, such as impatience, nervousness, or irritation. Additionally, drivers who tend to avoid traffic jams by changing lanes often drive into the opposite lane, indicating a connection with the lateral position.

Document Type

Presentation

Publication Date

2024

Notes/Citation Information

Presented at the 2024 Road Safety & Simulation Conference in Lexington, KY, held October 28-31, 2024

Digital Object Identifier (DOI)

https://doi.org/10.13023/2024.RSS03

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