Rural Stop-controlled Intersections Research Articles

Safety Performance of Rural Offset-T Intersections

Offset-T intersections represent a special geometric configuration where two three-leg intersections adjoin the major road from opposite directions within a short separation distance. The prevalence of offset-T intersections in rural areas coupled with the lack of research evaluating their safety performance led to the development of a series of safety performance functions for rural stop-controlled intersections that considered the effects of the offset direction and separation distance. In addition, crash modification factors (CMFs) were developed to estimate the change in crash frequency associated with converting a rural offset-T intersection into a conventional four-leg intersection. A series of mixed-effect negative binomial models for crash occurrence was generated based on 10 years of crash data from a sample of 299 offset-T intersections and 301 four-leg intersections with minor stop control along rural two-lane highways in Michigan. Compared with conventional four-leg intersections, offset-T intersections exhibited 35% more crashes regardless of the offset distance or direction. Considering crash types, single motor vehicle crashes occurred more frequently at offset-T intersections, and increased as the offset distance increased. Rear-end crashes also occurred more frequently at offset-T intersections, with left offsets having greater crash occurrence than right offsets. However, angle crashes were 40%–69% lower at offset-T intersections because of the elimination of the direct crossing maneuver. Considering the ranges of offset distance and direction utilized within this study, the total (non-animal) CMF for converting an offset-T into a four-leg intersection was 0.74.

Safety Performance Functions for Low-Volume Rural Stop-Controlled Intersections

This study involved the development of safety performance functions for rural, low-volume, minor road stop-controlled intersections in Michigan. Facility types included three-leg stop-controlled (3ST) and four-leg stop-controlled (4ST) intersections under state or county jurisdiction and were sampled from each of Michigan’s 83 counties. To isolate lower-volume rural intersections, major roadway traffic volumes were limited to the range of 400–2,000 vehicles per day (vpd). Data were compiled from several sources for 2,023 intersections statewide. These data included traffic crashes, volumes, roadway classification, geometry, cross-sectional features, and other site characteristics covering the period of 2011–2015. Random effects negative binomial regression models were specified for each stop-controlled intersection type considering factors such as driveway density, lighting presence, turn lane presence, and intersection skew, in addition to volume. To account for the unobserved heterogeneity between counties, mixed effects negative binomial models with a county-specific random effect were utilized. Furthermore, unobserved temporal effects were controlled through the use of a year-specific random effect. Separate models were developed for fatal/injury crashes, property damage crashes, and select target crash types. The analysis found that skew angles of greater than five degrees led to significantly greater crash occurrence for both 3ST and 4ST intersections, while greater than two driveways near the intersection led to significantly greater angle crashes at 4ST intersections. Other factors were found to have little impact on crash occurrence. Comparison with the Highway Safety Manual (HSM) base models showed that the HSM models over-predict crashes on 4ST intersections and 3ST intersections with volumes between 1,200 and 2,000 vpd.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

Safety Evaluation of Stop-Sign Mounted Beacons—A Cross-Sectional Study

Agencies in Iowa have utilized both overhead flashing beacons and stop-sign mounted beacons. Although several studies have shown that overhead flashing beacons are effective, some concerns have been raised about driver confusion. The main concern is that a driver may interpret a multiface flashing beacon with a red indication for their approach as an all-way stop control. As a result, the Iowa DOT has been advocating use of stop-sign mounted beacons rather than overhead flashing beacons. Since little information is available about this countermeasure, data for intersections with (treatment) and without (control) stop-sign mounted beacons were identified and a cross-sectional analysis conducted (due to few confirmable installation dates). Rural stop-controlled intersections with stop-sign mounted beacons in Iowa (USA) were identified (40 in total). Intersection characteristics such as number of approaches, intersection angle etc. were extracted. Additionally, characteristics of individual approaches such as roadway surface (gravel/paved), advanced stop-sign rumble strips, and advance signing were recorded. One or more control locations were manually selected for each treatment intersection based on matching roadway configuration, presence of lighting, advance stop line rumble strips, number of approaches, channelization, traffic volume, and proximity. Propensity scores were estimated to match 40 control locations for comparison. Negative binomial models for different injury combinations at nighttime and daytime were developed with an indicator variable for presence and absence of stop-sign mounted beacons. Presence of stop-sign mounted beacons was associated with a 5% - 54% reduction in nighttime crashes. Injury nighttime crashes decreased by 54% and total nighttime crashes reduced by 18%.

Comparison of Crash Severity Risk Factors at Signalized and Stop-Controlled Intersections in Urban and Rural Areas in Alabama

Understanding the factors that affect crash severity at intersections is essential to develop strategies to alleviate safety deficiencies. This paper identifies and compares the significant factors affecting crash severity at signalized and stop-controlled intersections in urban and rural areas in Alabama using recent five-year crashes. A random forest model was used to rank variable significance and a binary logit model was applied to identify the significant factors at both intersection types in urban and rural areas. Four separate models (urban signalized, urban stop-controlled, rural signalized, and rural stop-controlled) were developed. New variables that were not previously explored were used in this study, such as the roadway type (one-way vs. two-way) and traffic control functioning (yes or no). It was found that one-way roadways were associated with a reduction in crash severity at urban signalized intersections. In all four models, rear-end crashes showed lesser severity than side impacts. Head-on crashes, higher speed limits, and curved sections showed higher severity in urban signalized and stop-controlled intersections. In rural stop-controlled intersections, right-turning maneuvers had a severity reduction. Female drivers showed 15% and 45% higher severity likelihood (compared to males) at urban and rural signalized intersections, respectively. Strategies to alleviate crash severity are proposed.

Investigation of Skew Angle and Other Factors Influencing Crash Frequency at High-Speed Rural Intersections

Intersections experience a disproportionate share of traffic crashes, injuries, and fatalities because of the increased number of conflicting traffic movements at these locations. These issues are particularly pronounced at rural locations where speeds are generally higher and most intersections are either stop- or yield-controlled. Ideally, intersecting roadways should be oriented at as close to a 90-degree angle as possible. However, intersection design can diverge from this preferred configuration and result in a skewed intersection. Skewed intersections have been found to cause safety and operational issues for road users. To determine the effect of intersection skewness on crash frequency, crash prediction models were estimated for rural stop-controlled intersections on high-speed two-lane highways throughout the state of Iowa. Separate analyses were conducted for three-leg and four-leg intersections. In both cases, crash frequency was estimated as a function of annual average daily traffic, skew angle, and other salient geometric characteristics. The results consistently showed crash frequency to increase with skew angle. A 10-degree deviation from 90 degrees would result in 3% more crashes at three-leg intersections and 4% more crashes at four-leg intersections. In addition, crashes were affected by the volume of heavy vehicles, whether the approaches were paved or unpaved and whether intersection lighting was present.

Concept evaluation of intersection decision support (IDS) system interfaces to support drivers’ gap acceptance decisions at rural stop-controlled intersections

Abstract Rural stop-controlled intersections pose a crash risk for drivers turning or crossing the intersection from the minor road. In particular, elderly drivers are at the highest risk of a collision in this situation. Errors made during gap detection, perception and acceptance are the main factors that influence crashes at this type of intersection. This study investigated young (20–40 years) and old (55–75 years) drivers’ gap acceptance performance in simulated day and night driving conditions in a Baseline condition (STOP sign only) and four intersection decision support (IDS) conditions. The four IDS conditions were initial infrastructure-based design concepts that provided varying levels of dynamic information about traffic conditions on the major road to crossing minor-road drivers. Signs that provided detailed gap information (i.e., time-to-arrival values, warning levels for gaps) as well as advisory information about unsafe conditions resulted in the best performance among old and young drivers in comparison to signs that did not provide specific gap-related information (i.e., detected vehicles approaching, but not size of gap or safety of gap). Comprehension, acceptance and usability ratings of the IDS signs were also highest for signs providing detailed gap and advisory information on the same sign. Recommendations for further design and development of the IDS system interface based on driver performance and acceptance of the technology is discussed.

Design of an intersection decision support (IDS) interface to reduce crashes at rural stop-controlled intersections

Rural, stop-controlled intersections pose a crash risk to drivers, particularly elderly drivers. This paper outlines the design phase of an infrastructure-based intersection decision support (IDS) system to help drivers make safer gap acceptance decisions at rural intersections. A human factors-based design process was conducted to determine the type of information that should be presented to drivers. Information considered important for presentation to the driver included showing the presence of gaps, indicating the size of available gaps, and/or judging the safety of available gaps. This paper discusses the process used to determine the appropriate design specifications for initial testing of the IDS system interface.

Speed Changes Due to Transverse Rumble Strips on Approaches to High-Speed Stop-Controlled Intersections

The objective of this research was to determine whether the presence of transverse rumble strips is an effective warning device for drivers approaching rural stop-controlled intersections. Rural highways are associated with long tangents and infrequent intersections, which can cause drivers to become inattentive. To evaluate the effectiveness of transverse rumble strips, vehicle speeds were measured at three locations along the approach to rural stop-controlled intersections both before and after the installation of transverse rumble strips. The researchers evaluated nine rural stop-controlled intersection approaches in Texas during both day and night conditions on both weekends and weekdays. Overall, the installation of transverse rumble strips generally produced small but statistically significant (p ⩽ .05) reductions in approach speeds. There were no clear indications that the transverse rumble strips had a greater or smaller impact during day, night, weekend, or weekday periods. In a few instances, sp...

Speed Changes Due to Transverse Rumble Strips on Approaches to High-Speed Stop-Controlled Intersections

The objective of this research was to determine whether the presence of transverse rumble strips is an effective warning device for drivers approaching rural stop-controlled intersections. Rural highways are associated with long tangents and infrequent intersections, which can cause drivers to become inattentive. To evaluate the effectiveness of transverse rumble strips, vehicle speeds were measured at three locations along the approach to rural stop-controlled intersections both before and after the installation of transverse rumble strips. The researchers evaluated nine rural stop-controlled intersection approaches in Texas during both day and night conditions on both weekends and weekdays. Overall, the installation of transverse rumble strips generally produced small but statistically significant ( p ≤ .05) reductions in approach speeds. There were no clear indications that the transverse rumble strips had a greater or smaller impact during day, night, weekend, or weekday periods. In a few instances, speed change reductions of greater than 1 mph occurred; however, the overall trend was for speed change reductions to be equal to or less than 1 mph.

Rural Stop-Controlled Intersection Decision Support Systems

Minnesota drivers are over-represented in accidents at rural stop-controlled intersections. There is also evidence that drivers in other states as well as older drivers are involved in many of these crashes. A task analysis and literature review showed that rural drivers could be getting into accidents because of problems detecting vehicles or gaps, perceiving or estimating the size of gaps, and/or judging gaps as safe. It is suggested that intersection decision support (IDS) systems present drivers with information elements that help them correctly identify and locate other vehicles and in some cases highlight (safe) gaps in approaching traffic. Several design concepts are proposed and research needs are discussed.