U.S. Department of Transportation
Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC 20590
Human Factors Literature Reviews on Intersections, Speed Management, Pedestrians and Bicyclists, and Visibility, FHWA-HRT-06-34, July 2006 [PDF 2.12 MB]
NHTSA 100-Car Naturalistic Driving Studies
This research effort was initiated to provide an unprecedented level of detail concerning driver performance, behavior, environment, driving context and other factors that were associated with critical incidents, near crashes and crashes for 100 drivers across a period of one year.
Task Analysis of Intersection Driving Scenarios: Information Processing Bottlenecks, FHWA-HRT-06-033, August 2006. [HTML, PDF 4.01 MB]
Report purpose is to identify the information processing bottlenecks that drivers face in specific intersection driving scenarios. These bottlenecks represent situations in which drivers may become overloaded by driving demands, which could result in drivers conducting important driving tasks in improper fashion or skipping certain tasks altogether. Seven distinct driving scenarios were investigated in the task analysis, and each scenario was successively separated into segments, tasks, and subtasks/information processing elements. The scenarios included in the analysis were: (1) left turn on green light, (2) left turn on yellow light, (3) straight on yellow light, (4) straight on green light, (5) right turn on green light, (6) right turn on red light, and (7) stop on red light.
Causal Factors for Intersection Crashes in Northern Virginia
Intersection crashes cost the nation more than $40 billion annually, account for more than one-fifth of all highway crash fatalities nationally, and totaled almost 75,000 in the Virginia Department of Transportation’s (VDOT) Northern Virginia District for the period 2001 through 2006. Although VDOT maintains several databases containing more than 170 data elements with detailed crash, driver, and roadway attributes, it was not clear to users of these databases how these data elements could be used to identify causal factors for these intersection crashes for two reasons: (1) the quality of some of the data elements was imperfect, and (2) and random variation is inherent in crashes. This study developed an approach to address these two issues.
Source Organization: Virginia Transportation Research Council