Application of naïve Bayesian approach in detecting reproducible fatal collision locations on freeway

This code implements the paper, Kim et al. (2021). Application of naïve Bayesian approach in detecting reproducible fatal collision locations on freeway. PLoS One 1–21. https://doi.org/10.1371/journal.pone.0251866

Overview

The objective of this model is to detect reproducible fatal collision locations based on a naive Bayesian approach. We adopted continuous risk profile (CRP) and spatial distribution of fatal collision locations as a prior and a likelihood, respectively. Based on the posterior that is the product of the prior and likelihood, reproducible fatal collision locations were detected and prioritized.

Getting Started

Dependencies

• R 4.0.3

Components

Dataset

• 'Data' contains traffic crash data collected from six routes of interstate highway in California from 2006 to 2008.
• Based on the data, safety performance function (SPF) and continuous risk profile (CRP) were estimated and saved as separate files. For more details on calculating SPF and CRP, see Kwon et al.(2013) and Chung et al. (2009)

FatalCRP.R

• Step-by-step implementation of the proposed method is provided in a single file, including data preprocessing, modeling, evaluation, and visualization
• Refer the FatalCRP.html for a detailed description of the code

Notice

• Please refer to the full paper with this code for understanding the logic behind each process

Authors

@Eui-Jin Kim

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• Benchmark model