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Related Experiment Video

Updated: Feb 7, 2026

Laboratory Scale Slow Cook-Off Testing of Rocket Propellants: The Combustion Rate Analysis of a Slowly Heated Propellant CRASH-P Test

Laboratory Scale Slow Cook-Off Testing of Rocket Propellants: The Combustion Rate Analysis of a Slowly Heated Propellant CRASH-P Test

Published on: February 6, 2021

A novel method for imminent crash prediction and prevention.

Zhi Chen¹, Xiao Qin²

¹Department of Civil and Environmental Engineering, University of Wisconsin-Milwaukee, 2025 E Newport Ave, NWQ 4515, Milwaukee, WI 53201, United States.

Accident; Analysis and Prevention

|July 16, 2018

Summary

This study introduces a novel crash prediction and prevention system to identify high-risk driving conditions and suggest traffic controls. The method effectively detects crash-prone situations and evaluates safety measures, enhancing road safety.

Keywords:

Cell transmission model (CTM)Crash prediction Crash prevention Variable speed limit (VSL)

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Last Updated: Feb 7, 2026

Laboratory Scale Slow Cook-Off Testing of Rocket Propellants: The Combustion Rate Analysis of a Slowly Heated Propellant CRASH-P Test

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Area of Science:

Transportation Engineering
Traffic Safety
Intelligent Transportation Systems

Background:

Road crashes pose significant safety and economic challenges.
Existing methods for crash prevention often lack real-time risk detection and strategy optimization.
The need for proactive traffic management systems is critical.

Purpose of the Study:

To develop and validate a comprehensive method for predicting imminent crash risk.
To create a system that recommends optimal traffic control strategies for crash prevention.
To evaluate the effectiveness of the proposed method in real-world scenarios.

Main Methods:

A two-module approach: crash prediction and crash prevention.
Crash prediction module identifies crash-prone conditions based on exceeding a probability threshold.
Crash prevention module simulates traffic control alternatives and recommends the optimal strategy.

Main Results:

The proposed method successfully detected crash-prone conditions.
The system effectively evaluated the safety and mobility impacts of various countermeasures.
A case study utilizing variable speed limits (VSL) demonstrated the method's efficacy.

Conclusions:

The developed crash prediction and prevention method is effective for real-time risk detection.
The system can reliably assess the impact of safety countermeasures.
This approach offers a promising solution for enhancing traffic safety and management.