Objective: Speeding is a prevalent and complex risky behavior that can be affected by many factors. Understanding how drivers speed is important for developing countermeasures, especially as new automation features emerge. The current study seeks to identify and describe types of real-world speeding behaviors with and without the use of partial-automation.Methods: This study used a combination of supervised and unsupervised data analysis techniques to assess relevant factors in real-world speeding epochs, extracted from the MIT Advanced Vehicle Technology Naturalistic Driving Study, and classified them into distinct speeding behaviors. Speeding epochs were defined as traveling at least 5 mph over the speed limit for a minimum duration of 3 s. Vehicle speed-exceedance profiles were characterized over time using Dynamic Time Warping and included in multivariate models that evaluated the associations between different features of the speeding epochs, such as speeding duration and magnitude. Finally, the identified features were used to cluster speeding behaviors using the Gower dissimilarity measure.Results: The analysis yielded four types of behaviors in both partially-automated and manual driving: (i) Incidental speeding (low duration, low magnitude), (ii) Moderate speeding (low duration, moderate magnitude), (iii) Elevated speeding (moderate duration, high magnitude), and (iv) Extended speeding (long duration, high magnitude). When comparing the behaviors with and without partial-automation use, both Incidental and Moderate speeding were found to have significantly longer durations with partial-automation than manual driving. Elevated speeding was found to be more prevalent and associated with higher magnitudes during manual than with partially-automated driving. Finally, although Extended speeding was more prevalent during automation use, it was associated with a lower mean and maximum speed magnitude compared to Extended speeding during manual driving.Conclusions: This work highlights the variability in speeding behavior between and within partially-automated and manual driving. The design of systems that mitigate risky speeding behaviors should consider targeting divergent behaviors observed between manual and automated driving as a mechanism to mitigate the prevalence of the different behaviors associated with each state.
Keywords: Automation; cluster analysis; driver behavior; naturalistic data; safety; speeding.