Summary of Crash Severity Risk Modeling Strategies Under Data Imbalance, by Abdullah Al Mamun (1) et al.
Crash Severity Risk Modeling Strategies under Data Imbalance
by Abdullah Al Mamun, Abyad Enan, Debbie A. Indah, Judith Mwakalonge, Gurcan Comert, Mashrur Chowdhury
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates risk modeling strategies for predicting crash severity in work zones involving large vehicles, focusing on the challenge of imbalanced data between low-severity (LS) and high-severity (HS) crashes. The researchers utilize crash data from South Carolina work zones between 2014 and 2018, which shows a significant imbalance towards LS crashes. They explore various models’ performance under different feature selection and data balancing techniques to predict crash severity. The findings highlight the disparity in predicting HS and LS crashes due to class imbalance and feature overlaps. Combining features slightly improves HS crash prediction performance, while data balancing techniques like NearMiss-1, RandomUnderSampler, and K-SMOTE achieve better HS recall with certain models like Bayesian Mixed Logit (BML), NeuralNet, and RandomForest. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at ways to predict how severe car crashes are in areas where big vehicles like trucks and buses drive. They found that the data was uneven – there were many more minor crashes than major ones. The researchers tried different ways of choosing which factors to use when making predictions and how to balance out the differences between minor and major crashes. They found that some methods worked better for predicting major crashes, while others did a good job with both types of crashes. |
Keywords
» Artificial intelligence » Feature selection » Recall
