RESEARCH ARTICLE
KASSANDRA Model: Detecting Dangerous Traffic Conditions By Modeling Drivers’ Internal Stress Energy
Nikolaos Nagkoulis1, 2
Article Information
Identifiers and Pagination:
Year: 2024Volume: 18
E-location ID: e26671212296402
Publisher ID: e26671212296402
DOI: 10.2174/0126671212296402240313055936
Article History:
Received Date: 31/12/2023Revision Received Date: 17/02/2024
Acceptance Date: 21/02/2024
Electronic publication date: 20/03/2024
Collection year: 2024
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Introduction
This paper introduces an innovative method to reduce car accidents by employing mechanical concepts and energy conservation to model drivers’ reactions in unexpected scenarios.
Methodology
The approach involves formulating equations to define drivers’ “internal stress energy,” indicative of their propensity for aggressive driving under time pressure. A spatiotemporal model was developed using traffic data from Highways England and accident data from Transport for London, analyzing around 200 car accidents with data from 80 cameras over two years.
Results and Discussion
Findings suggest a correlation between drivers’ internal stress energy and car accidents, highlighting the predictive value of the proposed equations in assessing road segment dangers. More specifically, using the proposed model with 15-minute timeframes increased car accident prediction four (4) times compared to the evenly spatiotemporal car accident distribution. With smaller timeframes, e.g., two (2) minutes, or with real-time data, its predictive power would be significantly higher!
Conclusion
The equations developed offer a promising tool for estimating and preventing car accidents by modeling the influence of drivers’ stress on driving behavior.