Novice drivers (16-year-olds with < or = 6 months' driving experience) have the highest crash involvement rates per 100 million vehicle miles (161 million vehicle km). In the past, this was attributed to greater risk taking or poorly developed psychomotor skills. More recently, however, their high crash involvement rate has been hypothesized to be attributable largely to their relative inability to acquire and assess information in inherently risky situations. The current study seeks to evaluate this hypothesis by recording eye movements while 72 participants (24 novice drivers, 24 younger drivers, and 24 older drivers) drove through 16 risky scenarios in an advanced driving simulator. There were significant age-related differences in driver scanning behavior, consistent with the hypothesis that novice drivers' scanning patterns reflect their failure to acquire information about potential risks and their consequent failure to deal with these risks. Actual or potential applications of this research include modification of these scenarios for display on a PC as a basis for a training module that would enable novice drivers to recognize risky scenarios before they encounter them on the road, in the hope of reducing their high fatality rate.

A field study evaluated the stopping characteristics of vehicles 2.5 to 5.5 s upstream of signalized intersections at the start of a yellow interval, a region typically considered drivers' indecision zone or dilemma zone. Characteristics included brake-response times for first-to-stop vehicles, deceleration rates for first-to-stop vehicles, distinguishing characteristics and prediction of first-to-stop versus last-to-go events, and distinguishing characteristics and prediction of red-light-running events. Consumer-grade video cameras temporarily installed at four high-speed and two low-speed intersections in the Madison, Wisconsin, area recorded dilemma zone vehicles. Several factors were measured for each last-to-go (n = 435) and first-to-stop (n = 463) vehicle in each lane during each yellow interval, including approach speed; distance upstream at start of yellow; brake-response time; deceleration rate; vehicle type; headway; tailway; action of vehicles in adjacent lanes; presence of side-street vehicles, pedestrians, bicycles, or opposing vehicles waiting to turn left; flow rate; length of yellow interval; and cycle length. The observed 15th, 50th, and 85th percentile brake-response times for first-to-stop vehicles were 0.7, 1.0, and 1.6 s, respectively; their observed deceleration rates were 7.2, 9.9, and 12.9 ft/s2, respectively. Vehicles were more likely to go through than to stop under the following conditions: shorter estimated travel time to intersection at start of yellow; longer yellow interval; the subject was a heavy vehicle (truck, bus, recreational vehicle); absence of side-street vehicles, bicycles, pedestrians, and opposing left-turn vehicles; and presence of vehicles in adjacent lanes that went through. Heavy vehicles were more likely than passenger vehicles to run a red light. Vehicles were more likely to run a red light when vehicles in adjacent lanes that also went through were present and when side-street vehicles, bicycles, pedestrians, and opposing left-turn vehicles were absent.

This paper provides formulations of traffic operational capacity in mixed traffic, consisting of automated vehicles (AVs) and regular vehicles, when traffic is in equilibrium. The capacity formulations take into account (1) AV penetration rate, (2) micro/mesoscopic characteristics of regular and automated vehicles (e.g., platoon size, spacing characteristics), and (3) different lane policies to accommodate AVs such as exclusive AV and/or RV lanes and mixed-use lanes. A general formulation is developed to determine the valid domains of different lane policies and more generally, AV distributions across lanes with respect to demand, as well as optimal solutions to accommodate AVs.

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The use of autonomous vehicles is attracting more and more attention as a promising approach to improving both highway safety and efficiency. Most previous studies on autonomous intersection management relied heavily on custom-built simulation tools to implement and evaluate their control algorithms, but the use of nonstandard simulation platforms makes the comparison of systems almost impossible. Furthermore, without support from standard simulation platforms, reliable and trustworthy simulation results are hard to obtain. In this context, this paper explores a way to model autonomous intersections through the use of VISSIM, a standard microscopic simulation platform. A reservation-based intersection control system named autonomous control of urban traffic (ACUTA) was introduced and implemented in VISSIM through the use of VISSIM's external driver model. The operational and safety performance characteristics of ACUTA were evaluated with VISSIM's easy-to-use evaluation tools. In comparison with the results obtained with optimized signalized control, significantly reduced delays, along with a higher intersection capacity and lower volume-to-capacity ratios under various traffic demand conditions, resulted from the use of ACUTA. The safety performance of ACUTA was evaluated by use of the surrogate safety measure model, and few conflicts between vehicles within the intersection were detected. Moreover, the key steps and elements for implementation of ACUTA in VISSIM were introduced. These steps and elements can be useful for other researchers and practitioners implementing their autonomous intersection control algorithms in a standard simulation platform. By use of a standard simulation platform, the performance characteristics of autonomous intersection control algorithms can eventually be compared.