Field studies were performed to compare the effectiveness of traffic control countermeasures commonly used at uncontrolled midblock crosswalks. Various crosswalk treatments were evaluated at 31 low-speed midblock crosswalks located near three public universities. The study locations included unmarked crosswalks, in addition to standard and continental crosswalk markings, some including an additional enhancement device such as the pedestrian hybrid beacon (PHB), rectangular rapid flashing beacon (RRFB), or an in-street R1–6 sign. Driver yielding compliance during staged pedestrian crossing events was used as the measure of effectiveness. To isolate the crosswalk treatment effects, several roadway and traffic characteristics were included in the analysis, including the crossing distance, median presence, vehicular and pedestrian volumes, travel lane of the subject vehicle, and the subject vehicle’s position in a queue. A mixed effects logistic regression model was used to account for correlation in yielding rates within the same sites as well as for unobserved heterogeneity across locations. The results indicate that the type of crosswalk treatment has a strong influence over driver yielding compliance. While yielding compliance improved substantially when crosswalk markings were used, the highest compliance rates were achieved when an additional enhancement device (i.e., RRFB, PHB, or R1–6 sign) was also provided. Yielding compliance showed little sensitivity to the particular travel lane of the subject vehicle at locations where a crosswalk enhancement device was used, further validating the effectiveness of these treatments. Finally, yielding compliance rates were generally higher across each of the crosswalk enhancement devices compared with prior studies performed in the same areas, suggesting compliance improves as drivers become more familiar with these devices.

Safety performance functions (SPFs) were developed for rural two-lane county roadway segments in Michigan. Five years of crash data (2011 to 2015) were analyzed for greater than 6,500 mi of rural county roadways, covering 29 of Michigan’s 83 counties and representing all regions of the state. Three separate models were developed to estimate annual deer-excluded total and injury crashes on rural county roadways: 1) paved federal-aid segments, 2) paved non-federal-aid segments, and 3) paved and gravel non-federal-aid segments with fewer than 400 vpd. To account for the unobserved heterogeneity associated with differing county design standards, mixed effects negative binomial models with a county-specific random effect were utilized. Not surprisingly, the county segment SPFs generally differed from traditional models generated using data from state-maintained roadways. County federal-aid roadways general showed greater crash occurrence than county non-federal-aid roadways, the Highway Safety Manual (HSM) two-lane rural roadways model, and rural state highways in Michigan. County non-federal-aid paved roadways showed crash occurrence rates that were remarkably similar to the HSM base rural two-lane roadway model, whereas gravel roadways showed greater crash occurrence rates. The presence of horizontal curves with design speeds below 55 mph had a strong association with the occurrence of total and injury crashes across all county road classes. Increasing driveway density was also found to be associated with increased crash occurrence. However, lane width, roadway surface width, and paved shoulder width had little to no impact on total or injury crashes.

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.

Deer–vehicle crashes (DVCs) continue to be a problem in the United States, with 1.2 million such crashes occurring annually. DVCs are a particular issue on two-lane rural highways in Michigan, accounting for more than 60% of crashes. Such a high proportion of DVCs limits the transferability of existing safety models, including those found in the Highway Safety Manual (HSM), that are often based on data from states with considerably lower proportions of deer crashes. To counter this, a cross-sectional analysis of deer crashes was performed using data from Michigan. The data were analyzed across four categories of rural two-lane roadways, including: state highways, federal aid county roadways, non-federal aid county roadways, and unpaved (gravel) county roadways. Mixed effects negative binomial regression models utilizing spatial and temporal random effects were generated separately for each of the rural two-lane roadway types. Results showed speed-related factors, including lane width, shoulder width, horizontal curvature, and peak level of service, had a significant effect on DVC occurrence for most types of rural two-lane roadways in Michigan. Wider lanes were associated with a greater occurrence of deer crashes, perhaps because of higher prevailing travel speeds. Conversely, horizontal curves with design speeds lower than the statutory speed limit were associated with fewer deer crashes, perhaps because of lower travel speeds through curves. Wider shoulders, which afford greater separation between the travel lanes and the roadside, were found to have significantly lower deer crash occurrence. The number of available hunting licenses did not have a consistent effect on DVCs.

Much of the earlier work on rural safety focused on state-maintained roadways and little is known about the safety performance of low-volume county-maintained roads. This study involved the estimation of safety performance for rural county roadways (paved and gravel). This was accomplished through the development of safety performance functions (SPFs) to estimate the number of annual crashes at a given highway segment, crash modification factors to determine the impacts associated with various roadway and geometric characteristics, and severity distribution functions (SDFs) to predict the crash severity. County road segment data were collected across a sample of 30 counties representing all regions of Michigan. Because of the overwhelming proportion of deer crashes, only non-deer-related crashes were considered. To minimize the influence of variability among counties, the random effect negative binomial model was used to develop SPFs. In addition, a multinomial logit model was used to develop SDFs. Paved county roadways showed approximately double the crash occurrence rate of typical state-maintained two-lane rural highways, and gravel roadways showed a substantially greater crash occurrence rate than paved county roadways across the equivalent range of traffic volumes. The economic analysis showed that it is beneficial to pave a gravel road when the traffic volume is greater than 600 vehicles per day. The random effect variable is significant in all the calibrated models, which shows that there is a considerable variability among counties that cannot be captured with the available variables. Not considering the random effects will result in biased estimation of crashes.