A machine learning model for vehicle crash type prediction

Travel safety research works include the studies of risk factor investigation, crash detection, and crash frequency prediction. However, the existing studies are focused on the macro level, paying little attention to the specific crash type. In this study, eXtreme Gradient Boosting (XGBoost) method is applied to predict the occurrence of different types of crashes. A two-layer model is proposed. The first layer is used to distinguish potential crashes from crash-free observations and the second layer is used for crash type recognition. The results show that the proposed model can detect the potential accident and identify the crash type successfully, with accuracy levels of over 99% and 62%, respectively. Besides the crash type prediction model, this study provides a detailed analysis of the impacts of different risk factors on different types of crashes. From the traffic management perspective, the results of this study can prepare traffic managers for the potential threatens well in advance. From travelers’ perspective, the results of this study can be used to warn travelers of potential dangers before the trip so that a better trip planning can be made as well as alert them of the potential dangers during the trip. All of these actions are important for the travel safety management and can help protect people’s life and property.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Li, Xiyue

Contributors dc:contributor

• Meidani, Hadi

• Crash type, Crash prediction, Machine learning

Statement dc:rights

• Copyright 2020 Xiyue Li

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108066

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108066