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This study evaluates truck drivers' attitudes toward an on-board monitoring system (OBMS), using an extended version of the Technology Acceptance Model (TAM) that accounts for drivers' trust in OBMS. Crashes that involve trucks incur a high cost to society and driver-related factors contribute to about one third of all large truck fatal crashes in the US. Therefore, safety initiatives that can increase drivers' awareness of their risky behaviors are highly desirable. In-vehicle feedback systems are designed to serve this purpose; however, their benefits will not be realized unless their information can positively influence safe driving. Acceptance constructs for the proposed model were measured using a survey administered after the monitoring system was introduced to the drivers but before the system was actually installed in their trucks. In line with the TAM, the results demonstrated that perceived usefulness is the most important determinant of intention to use the OBMS. Trust was also a major determinant of intention to use, suggesting that the acceptance model can be usefully augmented by this construct.

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.

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