Title page for ETD etd-12012005-173602

Type of Document Dissertation
Author Kieliszewski, Cheryl A.
URN etd-12012005-173602
Title Twisted Metal: An Investigation into Observable Factors that Lead to Critical Traffic Events
Degree PhD
Department Industrial and Systems Engineering
Advisory Committee
Advisor Name Title
Dingus, Thomas A. Committee Chair
Hankey, Jonathan M. Committee Member
Kleiner, Brian M. Committee Member
Neale, Vicki L. Committee Member
Smith-Jackson, Tonya L. Committee Member
  • crash investigation
  • crash analysis
  • automotive
  • near-miss
  • driver behavior
Date of Defense 2005-10-24
Availability unrestricted
The purpose of this research was to explore traffic event severity relationships, evaluate the potentiality of a hazardous event, and develop a framework of observable event factors. Data was collected from three regions in Virginia, each assumed to exemplify a unique driving environment due to amount of traffic and infrastructure characteristics. In combination, a broad spectrum of site, traffic, and driver performance variables were accounted for. Observational techniques of surveillance, incident reporting, and inventorying were used to collect site, traffic, and driver data. This effort resulted in 368 observed traffic events that were evenly distributed among the three regions that represented metropolitan, mid-sized city, and town/rural driving environments. The 368 events were evaluated for severity and contributing variables where 1% of the events were non-injury crashes, 10% were serious, near-crashes, 24% were near-crashes, and the remaining 65% were serious errors with a hazard present. Exploratory analyses were performed to understand the general relationship between event severity levels. Binary logistic regression analyses (α = 0.05) were performed to further scope predictor variables to identify traffic event characteristics with respect to severity level, maneuver type, and conflict type. The results were that 69 of 162 observed predictor variables were valuable in characterizing traffic events based on severity. It was found that variables could be grouped to create event severity signatures for crashes, serious near-crashes, and near-crashes. Based on these signatures, it was found that there is a trend between severity levels that included a propensity for problems with straight path maneuvers, lateral and longitudinal vehicle control, and information density within the driving environment as contributing to driver error and hence crashes and near-crashes. There were also differences between the severity levels. These differences were evident in the degree of control the driver appeared to have of the vehicle, type of control regulating the driving environment, and type of road users present in the driving environment. Modifications to roadway evaluative techniques would increase awareness of additional variables that impact drivers to make more informed decisions for roadway enhancements.
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