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State-of-the-practice models for estimation of the number of vehicle stops at signalized intersections are first reviewed, and then two approaches are introduced for the computation of the number of vehicle stops at undersaturated and oversaturated signalized intersections. The first approach uses a microscopic model that computes instantaneous partial and full stops for undersaturated and oversaturated conditions by using second-by-second speed measurements. This model, in particular, has been introduced in the INTEGRATION traffic simulation software. The second model is an analytical formulation derived from the proposed microscopic model that computes the number of vehicle stops for oversaturated approaches over a given analysis period. Finally, comparisons of the stop estimates produced by the two proposed models with estimates obtained from current state-of-the-practice analytical models demonstrate the validities of both models in their respective domains of application.

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Driver inattentiveness and distraction resulting in unsafe vehicle maneuvers are a significant safety concern because such behavior can directly lead to crashes. An effective technical countermeasure is to detect unsafe driving events and provide drivers with advanced warning information. This study presents an intervehicle safety warning information system. An inertial measurement unit consisting of an accelerometer and gyro sensor in addition to a Global Positioning System receiver was used to collect data for the developed algorithm. Vehicle position, speed, acceleration, and angular velocity data were analyzed and were used as inputs for the algorithm. A support vector machine classifier was also incorporated into the algorithm to identify further the severity of unsafe driving events. The performance evaluation results showed that the detection algorithm could capture longitudinal and transverse unsafe driving events. In addition, a prototype of the proposed warning information system was implemented on a test bed in support of vehicle-to-vehicle and vehicle-to-infrastructure communications. Extensive field tests have been conducted in the test bed to fine-tune the prototypical system. These results demonstrate that the system holds promise for improving drivers' safety and mitigating crash risks.

Pedestrian-related accidents are considered to be the most serious of traffic accidents due to the associated high fatality rates. In Korea, pedestrian fatalities accounted for approximately 40% of all traffic-related fatalities in 2004. Significant efforts have been made to develop effective countermeasures for pedestrian-vehicle collisions. A basis for devising such countermeasures is to understand the characteristics of pedestrian-vehicle collisions. This study develops a pedestrian fatality model capable of predicting the probability of fatality in pedestrian-vehicle collisions. Binary logistic regression and a probabilistic neural network (PNN) are employed to estimate the probability of pedestrian fatality. Pedestrian age, vehicle type and collision speed are used as independent variables of the fatality model. The models developed herein are valuable tools that can be used to direct safety policies and technologies associated with pedestrian safety.