Date of Award

Fall 12-15-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Analytics

First Advisor

Kayla Jordan

Abstract

Traffic crashes account for most of casualties and injuries worldwide, and there has been growing concerns and studies regarding the contributing factors of traffic crashes. There are many factors causing or related to an occurrence of traffic crash, e.g., land use, traffic flow conditions, driver behavior and weather condition. This paper studied the spatial and temporal distribution of crashes on highway and developed real-time prediction models for crash occurrence. Traffic flow data, weather data, and crash data from multiple data sources were collected and processed to develop the model. Multiple machine learning models, such as SVM model and Decision Tree model, were used as the candidate models. It was found that weather, crash time, and traffic flow shortly prior to the crash occurrence are critical impacting factors for real-time crash prediction. The candidate models have low to moderate sensitivity to predict the crash occurrences due to limited sample size. To use the models in a traffic operations environment, a prediction tool with interactive map could be developed to proactively monitor crash hot spots and prepare staffing and resources for the potential crash occurrences.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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