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Understanding speed selection behavior of drivers following speed limit increases is critically important. To date, the literature has largely focused on freeways and the effects of speed limit changes on two-lane highways remains under researched. Prior research has generally focused on changes to mean speeds, although the speeds of both the highest and lowest drivers are also of great interest. This study investigates trends in free-flow travel speeds following 2017 legislation that increased the posted speed limit from 55 to 65 mph on 943 mi of rural highways in Michigan. Speed data were collected for over 46,000 drivers at 67 increase segments where speed limit increased and 28 control segments where speed limits remained unchanged, before and during each of the two successive years following the speed limit increases. Site-specific traffic, geometric, and cross-sectional information was also collected. Impacts of the speed limit increases on the 15th, 50th, and 85th percentile speeds were evaluated using quantile regression. Separate analyses were conducted for passenger cars and heavy vehicles. Locations where the speed limits were raised experienced increases in travel speeds ranging from 2.8 to 4.8 mph. The control sites experienced marginal changes in speeds, which suggests that any spillover effects of the higher speed limits have been limited. Significant differences were observed across the quantiles with respect to the effects of the speed limit increases, as well as numerous site-specific variables of interest. The results provide important insights about the nature of driver speed selection and the impacts of speed limit increases.

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Research was conducted at a freeway exit ramp with significant horizontal curvature to evaluate the effectiveness of dynamic speed feedback signs (DSFSs) as a speed reduction countermeasure. Several aspects of the DSFSs were evaluated, including display size, border type, lateral installation position, and vehicle detection range. Three different full-matrix DSFSs were utilized, which included 15-in. display panel with yellow border, 18-in. display panel with yellow border, and 18-in. display panel with no border. Each sign was individually installed and tested at identical locations near the start of the exit ramp curve, in both the traditional right-side-mount and an alternative forward-mount within the exit gore area. Speed data and message activation location were collected for vehicles approaching and entering the curve across the various sign test conditions. Overall, the presence of a DSFS positioned near the start of the curve resulted in curve entry speeds that were, on average, 3.5 mph lower than without a DSFS present. The lowest curve entry speeds were observed for cases in which the message activated when vehicles were within 250 to 400 ft of the curve. Interestingly, earlier message activation did not contribute to further speed reductions, although later activation substantially diminished the speed reduction effects. With regard to DSFS lateral position, both the side-mounted and forward-mounted DSFS installations resulted in similar curve entry speeds. Furthermore, there were no discernable differences in curve entry speeds between the 15- and 18-in. display panels, although the inclusion of a yellow sign border had greater speed reduction effects.

Cracks considerably reduce the life span of pavement surfaces. Currently, there is a need for the development of robust automated distress evaluation systems that comprise a low-cost crack detection method for performing fast and cost-effective roadway health monitoring practices. Most of the current methods are costly and have labor-intensive learning processes, so they are not suitable for small local-level projects with limited resources or are only usable for specific pavement types. This paper proposes a new method that uses an adapted version of the weighted neighborhood pixels segmentation algorithm to detect cracks in 2-D pavement images. The method uses the Gaussian cumulative density function (CDF) as the adaptive threshold to overcome the drawback of fixed thresholds in noisy environments. The proposed algorithm was tested on 300 images containing a wide range of noise representative of various pavement noise conditions. The method proved to be time and cost-efficient as it took less than 3.15 s per 320 × 480 pixels image for a Xeon (R) 3.70 GHz CPU processor to generate the detection results. This makes the proposed method a perfect choice for county-level pavement maintenance projects requiring cost-effective pavement crack detection systems. The validation results were promising for the detection of medium to severe-level cracks (precision = 79.21%, recall = 89.18%, and F1 score = 83.90%).