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This study investigated a less explored autonomous vehicle (AV) related topic (non-motorists and AVs) through the following two research questions: (1) Does the perception towards AVs differ in non-motorists based on stakeholder nature? and (2) Does prior interaction with AVs alter perception on AVs? The present study examined survey data collected by BikePGH in Pittsburgh, Pennsylvania. This study used multiple correspondence analysis (MCA) to identify the response patterns of participants and sort the responses into several clusters. The results show that perception measures vary among participants based on the nature of the stakeholder. The reception of AVs among the participants was mixed. The results also show that participants with real AV interactions have higher expectations and interest in AVs than the participants with no experience. Additionally, the findings show that participants who see no safety potentials for AVs are also against using Pittsburgh as an AV proving ground. It is anticipated that the results will help in improving the strategic management of AVs to make non-motorist mobility safer. [ABSTRACT FROM AUTHOR]

As the demand for additional capacity on urban freeways continues to increase, there is a need to better understand how to optimize the lane and shoulder configurations so as to improve capacity without adversely impacting operating speed. The overall objective for this research effort was to identify the operational implications of using reduced lane and shoulder widths on freeways. The operational analysis found speeds 3.5 km=h (2.2 mph) higher for a 3.7 m (12 ft) lane as compared to a 3.4 m (11 ft) lane. The authors developed an equation that can be used to predict speeds per lane for freeway locations with boundary conditions similar to those present in the study database. [ABSTRACT FROM AUTHOR]

General freeway design practice assumes deceleration for a vehicle exiting the freeway occurs between the point when the vehicle clears the through-traffic lane and the point of limiting design speed for the ramp proper. The length provided between these points should be at least as great as the distance needed to accomplish the appropriate deceleration, which is governed by the speed of traffic on the through lane and the speed to be attained on the ramp. The deceleration length values used in design are based on assumed running speed for the limited-access highway and the ramp, along with deceleration rates based on 1930s studies. The need to update the speed assumption for the highway and the ramp curve is clear, although determining appropriate deceleration rates is not as simple. Previous research has demonstrated that drivers select speeds at or above the design speed on horizontal curves, rather than the much lower average running speed that previously had been assumed for several design elements, including exit ramps. Although the assumption that drivers choose speeds less than the design speed in a free-flow situation is certainly questionable, there is some evidence that drivers do decelerate in the travel lane before moving into the deceleration lane. Updates for the current assumptions were identified on the basis of research, including studies on speed behavior on highways and detailed decelerating behavior for 27 exit maneuvers at two ramps. The paper includes suggested deceleration lengths. [ABSTRACT FROM AUTHOR]

Left-turn gap acceptance at an unsignalized intersection is dependent on many factors. The Highway Capacity Manual (HCM) uses a single value of critical gap for all types of intersections; however, this may be oversimplistic and lead to inaccurate estimates of left-turn delay and capacity. Most existing studies also do not differentiate between gap and lag when evaluating gap acceptance. In this paper, binary logit models were developed to estimate the probability of accepting or rejecting a given gap or lag for a left-turning vehicle from a major road at an unsignalized intersection considering a number of potential influencing factors. Gap acceptance behavior was found to be influenced by the type of gap presented to the driver (gap or lag). Gap duration, total wait time, time to turn, distance to next signal downstream, and median type were found to be significant factors in predicting the probability of accepting or rejecting a gap. In the model for lag acceptance lag duration, time to turn, crossing width, speed limit, and distance to next signal downstream were found to be significant. Equations for estimating the critical gap and lag were developed. Critical gap and lag were found to be varying over a wide range of values depending on the type of intersection. The range was smaller for critical gaps than lags. The findings from this study can improve operational analysis of left turns at unsignalized intersections by using different critical gaps for different traffic and geometric conditions. [ABSTRACT FROM AUTHOR]

Agencies are seeking a better understanding of those roadway or roadside features that affect safety. The objectives of this study were to develop a horizontal curve accident modification factor (AMF) for rural four-lane divided and undivided highways and to determine if the effect of driveway density is different for horizontal curves as compared to tangent sections. Data available for use in the evaluation included 194.8 centerline km (121 centerline mi) of rural four-lane highways. Negative binomial regression models were used to determine the effects of independent variables on crashes. Variables considered in developing the base models included driveway density, lane width, outside shoulder width, median width (which included inside shoulder width), median type, degree of curve, segment length, and average daily traffic. Five years (1997–2001) of driveway and segment crashes were examined. An AMF for horizontal curves was identified. Reviewing the findings with respect to driveway density revealed that the effect of driveway density is different for horizontal curves and tangents; however, the differences were relatively minor. [ABSTRACT FROM AUTHOR]

Right-turn lanes are used to provide space for the deceleration and storage of turning vehicles and to separate the turning vehicles from the through movement. When larger corner radii are used at the right turn, vehicles can turn at higher speeds (thereby minimizing speed differential between turning and through vehicles). A concern with the higher operating speed is the challenge it provides pedestrians. This project analyzed the impact of right-turn treatments on right-turn vehicles speeds at 19 urban approaches. Each approach had an exclusive right-turn lane that was separated from the through lane with either a lane line or a raised corner island. The 85th percentile free-flow speed near the middle of the right turn ranged from 20.9 to 33.8 km/h (13–21 mi./h) while on the approach it ranged from 27.4 to 46.7 km/h (17–29 mi./h). Speed prediction equations were developed as part of this study. [ABSTRACT FROM AUTHOR]

The transportation profession is seeking to better understand the features that influence roadway speeds. Engineers need an appreciation of which roadway design features, or the values for the features, clearly relate to the speed performance on a facility. This study utilized free-flow speed data collected at 79 tangent sites in suburban/urban and rural areas in seven cities located in six states. Key variables used to select sites included functional classification (arterial, collector, and local), edge treatment (curb and gutter versus shoulder), and speed limit. Technicians collected speed data at the midpoint of long straight sections during daylight conditions. For each site, roadway and roadside characteristics were collected such as number of access points within the study section, roadside development type, lane width, and others. Statistical testing revealed that posted speed limit was the only statistically significant variable at a 5% alpha level. Access density was next with an approximate 20% alpha level. Several variables other than posted speed limit and access density showed signs of influence on the 85th percentile free-flow operating speed, including: median type, parking along the street, and pedestrian activity level. A followup cluster analysis revealed the following noteworthy features: pedestrian activity, parking, centerline, median treatment, roadside development, area type, and signal density. [ABSTRACT FROM AUTHOR]