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

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Identification and analysis of misclassified work-zone crashes using text mining techniques.

Md Abu Sayed¹, Xiao Qin¹, Rohit J Kate²

¹Department of Civil and Environmental Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, 53201, United States; Institute for Physical Infrastructure and Transportation (IPIT), University of Wisconsin-Milwaukee, Milwaukee, WI, 53201, United States.

Accident; Analysis and Prevention

|June 14, 2021

Summary

Accurate work zone crash data is vital for safety. This study uses text mining to identify underreported work zone (WZ) crashes in police narratives, improving traffic safety analysis.

Keywords:

Crash data Crash narrative Missclassified Noisy-OR Text mining Work zone

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Area of Science:

Transportation Safety
Data Science
Traffic Engineering

Background:

Work zone safety research depends on accurate crash data.
Police crash reports often misclassify work zone incidents due to various factors, leading to underrepresentation in statistics.
Crash narratives contain crucial details often omitted from structured data.

Purpose of the Study:

To develop a text mining classifier to identify missed work zone crashes from unstructured crash narratives.
To improve the accuracy of work zone crash data for better safety management and research.

Main Methods:

Utilized three-year crash data (2017-2019), with 2017-2018 for training and 2019 for testing.
Developed and applied a unigram + bigram noisy-OR classifier to analyze crash narratives.
Performed ad-hoc analysis on misclassified crashes to understand reasons for underreporting.

Main Results:

The developed classifier effectively and efficiently identified missed work zone crashes using crash narrative text.
The study highlighted specific circumstances and reasons contributing to the misclassification and omission of work zone crashes.
Text mining proved valuable in extracting critical safety information from unstructured data.

Conclusions:

Text mining techniques offer a viable solution for improving the completeness of work zone crash data.
Accurate crash data is essential for effective work zone safety management and targeted interventions.
Further analysis of misclassified crashes can inform improved data collection protocols and officer training.