Vehicle delay and queue length are quantitative measures of intersection performance. The technological advancement in the field of vehicle sensors and traffic controllers has reached a point where it has enabled efficient measurement of these performance measures. Two techniques are presented for real-time measurement of vehicle delay and queue length at a signalized intersection, and these automated delay and queue estimates are compared with manually ground-truthed measurement. These techniques were evaluated at an instrumented intersection in Noblesville, Indiana. The root-mean-square error by both techniques was below 0.7 veh-s for estimation of average delay and less than 0.15 vehicle for estimation of average maximum queue length, both on a cycle-by-cycle basis.

In some states, frontage roads are frequently used along freeway and fully controlled principal arterial corridors. Their primary function is to provide access between the arterial, or freeway, and adjacent developed property. Little information about the safety performance of rural frontage roads exists. This paper describes the development of a safety performance function (SPF) and accident modification factors (AMFs) for rural frontage road segments. Both one-way and two-way frontage road operations are addressed. The AMFs quantify the relationship between specific changes in geometric design and road safety. The findings from this research show that wider lanes and shoulders are associated with a reduction in segment-related collisions. In addition, the data suggest that the presence of edge marking has an impact on rural two-way frontage road safety. The SPF developed for this research indicates that, for the same traffic volume, rural frontage road segments experience about the same number of severe crashes as typical rural two-lane highways.

Crash modification factors (CMFs) are listed in the Highway Safety Manual and other authoritative publications. This information does not allow the reader to distinguish between the predictions of safety effect that can be made confidently and are likely to lead to correct decisions and those that can easily be wrong. Nor can it be known how transferable past research results are to decisions about future actions to be implemented under different circumstances. The conceptual framework described in this paper aims to provide guidance for research about CMFs and for meta-analyses. The central claim is that CMFs are random variables and are not universal constants that apply everywhere at all times. The smaller the standard deviation of a CMF, the more confident the related decision making can be. Therefore, the aim of research into CMFs is to reduce their standard deviations. Ways to do so efficiently are indicated. The requisite theory and equations are provided.

Statistics indicate that red-light running has become a significant safety problem throughout the United States. There is a wide range of potential countermeasures to the problem of red-light running; an increase in yellow duration is one countermeasure. The objective of this research was to quantify the effect of a change in yellow-interval duration on the frequency of red-light violations. A before-after study is described and the resulting data are used to quantify the effect of increasing the yellow interval on the frequency of red-light violations. Based on this research, it was concluded that ( a) an increase of 1.0 s in yellow duration (such that it does not exceed 5.5 s) will decrease the frequency of red-light violations by at least 50%; ( b) drivers do adapt to the increase in yellow duration, but this adaptation does not undo the benefit of an increase in yellow duration; and ( c) increasing a yellow interval that is shorter than the value obtained from a proposed recommended equation published by the Institute of Transportation Engineers is likely to yield the greatest return (in terms of a reduced number of red-light violations) relative to the cost of retiming a yellow interval in the field.

This paper describes the development and calibration of a curve speed prediction model. The model includes variables for curve radius, deflection angle, superelevation rate, and tangent speed. The model is based on the hypothesis that drivers modify their side friction demand because of a desire for both safe and efficient travel. One model component reflects a general desire by motorists to have lower side friction demand on higher speed curves. This trend likely reflects the driver's desire to maintain an acceptable margin of safety against sliding out or rolling over. A second model component reflects a willingness by drivers to tolerate slightly higher side friction demand on sharper curves in an effort to minimize the amount of speed reduction. The increase in side friction demand that a driver accepts is found to be proportional to the energy required to slow the vehicle to the curve speed.