Understanding the relationship between mild cognitive impairment (MCI) and driving behavior is essential for enhancing road safety, particularly among older adults. This study introduces a novel approach by incorporating specific trip destinations-such as home, work, medical appointments, social activities, and errands-using geohashing to analyze the driving habits of older drivers in Nebraska. We employed a two-fold methodology that combines data visualization with advanced machine learning models, including C5.0, Random Forest, and Support Vector Machines, to assess the effectiveness of these location-based variables in predicting cognitive impairment. Notably, the C5.0 model showed a robust and stable performance, achieving a median recall of 0.68, which indicates that our methodology accurately identifies cognitive impairment in drivers 68\% of the time. This emphasizes our model's capacity to reduce false negatives, a crucial factor given the profound implications of failing to identify impaired drivers. Our findings underscore the innovative use of life-space variables in understanding and predicting cognitive decline, offering avenues for early intervention and tailored support for affected individuals.
@article{arxiv.2504.09027,
title = {Predicting Mild Cognitive Impairment Using Naturalistic Driving and Trip Destination Modeling},
author = {Souradeep Chattopadhyay and Guillermo Basulto-Elias and Jun Ha Chang and Matthew Rizzo and Shauna Hallmark and Anuj Sharma and Soumik Sarkar},
journal= {arXiv preprint arXiv:2504.09027},
year = {2025}
}