National Surface Transportation Safety Center for Excellence Reports (NSTSCE, VTTI)

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https://hdl.handle.net/10919/5529
http://www.vtti.vt.edu/national/nstsce/

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Browsing National Surface Transportation Safety Center for Excellence Reports (NSTSCE, VTTI) by Subject "ambulance"

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    Creating a Dataset of Naturalistic Ambulance Driving: A Pilot Study of Two Ambulances
    Valente, Jacob T.; Terranova, Paolo; Perez, Miguel A. (National Surface Transportation Safety Center for Excellence, 2024-08-02)
    Motor vehicle collisions (MVCs) are an everyday occurrence in the United States. This pressing transportation and health care topic affects millions of citizens each year, and in many cases may result in fatality or lifelong injury complications. Despite best efforts, and notable success, to improve the frequency and severity of MVCs, these events are still a prevalent issue. In the wake of an MVC, crash occupants rely on emergency responders to quickly respond to the scene, control hazards, and administer necessary medical care. Efficiency within the emergency response event, to an MVC or some other medical care need, is contingent on a properly working transportation system, allowing emergency medical services (EMS) to travel to and from scenes both quickly and safely. Previous research has revealed that complex interactions with other road users not only hinders emergency response efficiency, but often results in hazardous and dangerous interactions on roadways. To capture these complex interactions from a firsthand perspective, this report details a naturalistic driving study that involved two ambulances and the subsequent dataset that was generated, which is the first of its kind. A custom data acquisition system was used to collect four external and three internal video perspectives on each vehicle, with continuous vehicle data that included vehicle speed, GPS location, and emergency system activation (i.e., emergency light or siren use). Following data collection, the dataset was summarized in the context of each participating agency, the consented drivers, trip type (emergent vs. non-emergent), trip duration, trip distance, and the time of day that the trip took place. The dataset was also processed through a map-matching algorithm that utilized the collected GPS data to provide additional context, including posted speed limit road classification. Finally, the dataset was subsampled to assess and interpret other road user behavior during emergent trips. The work outlined in this report serves as the foundation for additional research that could be leveraged from this dataset, as this dataset is intended to support the inquiry of future research questions within the scope of emergency vehicle operation and transportation. Additionally, some findings of this study and their implications apply beyond the scope of emergency MVC response and may be related more broadly to emergency response for all first responders and emergency events.