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Updated: Sep 10, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

Safety evaluation for heavy vehicle drivers using extreme value model based on the multi-source sensing data.

Chenxiao Zhang¹, Yongfeng Ma², Tarek Sayed³

¹School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China; Department of Civil Engineering, The University of British Columbia, Canada.

Accident; Analysis and Prevention

|August 26, 2025

Summary

This study introduces a new safety framework for heavy vehicles using extreme value modeling. It identifies key driving indicators and thresholds to predict and manage crash risks effectively.

Keywords:

Extreme value model Heavy vehicles Multi-source sensing data Safety evaluation Surrogate measures of safety

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Area of Science:

Transportation Engineering
Data Science
Risk Management

Background:

Intelligent networked platforms collect multi-source sensing data from heavy vehicles for safety.
Existing crash risk management and safety evaluation methods do not fully utilize this driving data.

Purpose of the Study:

To propose a novel safety evaluation framework for heavy vehicles.
To develop methods for determining crash risk thresholds and analyzing indicator dependencies.

Main Methods:

Employed extreme value modeling, including univariate and bivariate logistic-based models.
Applied block maxima models to a dataset of 3,452 heavy vehicle trips.
Identified thresholds for Speed time-varying stochastic volatility (Speed-Vf) and Jerk.

Main Results:

Speed-Vf and Jerk are effective indicators for heavy vehicle driving risk assessment.
Unloaded conditions and high distraction/fatigue warnings increase crash risk.
Optimal thresholds for Speed-Vf and Jerk were identified for crash estimation.

Conclusions:

The proposed framework effectively evaluates heavy vehicle safety.
Bivariate models provide more robust crash risk estimations than univariate models.
The study highlights the importance of analyzing joint probabilities for accurate risk assessment.