A crash narrative is the detailed description of a crash by law enforcement officers. Although it is a common practice to manually review crash reports to collect valuable information from crash diagram and narrative, the process is time-consuming and labor intensive; and the interpretation of a crash depends on a reviewer’s experience, knowledge and judgment. This project will develop a method for extracting, classifying and analyzing the key contextual information and sequence of events relating to a crash using the text mining techniques and tools. Such information can be vital for seeking the causal factors of a crash such as the actions, behaviors and circumstances leading to a pedestrian crash that are not usually presented in the structured data fields. In addition, maximizing the value of a crash narrative will encourage and incentivize law enforcement agencies to use a narrative to capture things that are not available in the data fields.

Project Documents

Project Details

Project ID


Start Date
October 1, 2019

End Date
September 30, 2021

Focus Areas
Data Analytics, Modeling and Simulation

Wi Dept Of Transportation
National Highway Traffic Safety Administration (NHTSA)

Research Centers
Institute for Physical Infrastructure and Transportation (IPIT)

Principal Investigator

Qin, Xiao
Director, Institute for Physical Infrastructure and Transportation (IPIT)
Professor, Civil and Environmental Engineering University of Wisconsin-Milwaukee

Co-Principal Investigator

Kate, Rohit J
Associate Professor Department of Computer Science University of Wisconsin-Milwaukee