Citation

Ghazizadeh M, Lee JD. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2012; 56(1): 2256-2260.

Copyright

(Copyright © 2012, Human Factors and Ergonomics Society, Publisher SAGE Publishing)

DOI

10.1177/1071181312561475

PMID

unavailable

Abstract

Driving simulators, crash databases, and more recently, naturalistic studies all help understand how changes to vehicle design affect driving safety. The rapid computerization of cars makes it increasingly important to capitalize on these sources and exploit others. The present study explores a rarely analyzed data source on traffic fatalities: National Highway Traffic Safety Administration's vehicle owner's complaint database. The textual data within the event description field of each complaint is extracted and analyzed using a text mining approach that involves the use of latent semantic analysis (LSA) for reducing the dimensionality of the problem. Hierarchical clustering is then employed to identify clusters of complaints that share content. Clusters are described in terms of the most frequent terms and the time trends of the complaints within them. The analysis highlights how text mining analysis can help unlock the wealth of information contained in consumer complaint databases.

Language: en