Research was undertaken to determine effective messaging strategies and sign positions for dynamic speed feedback signs (DSFS) when used for speed management at freeway ramp curves. A field evaluation was performed in this setting to assess the impacts of a DSFS on driver speed selection and braking characteristics while approaching and entering the curve. Three feedback messaging strategies were evaluated at three sign positions in advance of the curve. Compared with the existing site (without the DSFS), the DSFS reduced curve entry speeds and improved brake response across all test conditions, particularly for heavy trucks. Overall, considering the combination of both sign position and feedback messaging strategy, the greatest benefits to driver behavior were attained when the DSFS was positioned 255 ft upstream of the curve and the feedback message included the speed number alternating with a SLOW DOWN message. The inclusion of an advisory speed panel with the DSFS did not have a substantive impact on driver behavior. Based on the findings, the continued use of DSFS as a speed reduction treatment at freeway ramp curves is recommended. Specifically, the sign should be positioned to provide adequate time for drivers to perceive and react to the message, such that comfortable braking can be accommodated while approaching the curve. However, the sign should not be placed too far in advance of the curve, as drivers may be more likely to disregard such a premature warning message. Further evaluation of DSFS under various alternative ramp configurations is recommended.
A series of field evaluations was performed at three freeway interchange ramps in Michigan that possessed significant horizontal curvature to assess the impacts of a dynamic speed feedback sign (DSFS) on driver speed selection and brake response while approaching and entering the ramp curve. A DSFS with a 15 in. full-matrix display was temporarily installed at each of the three exit ramp locations. The sign was programmed to display the same feedback message at each location, which included the speed number for all approaching vehicles, which alternated with a “Slow Down” message for vehicles approaching above 40 mph. The effectiveness of the feedback sign was tested across various sign locations (at the point of curvature versus 350 ft upstream), interchange types (system versus service), time of day (peak versus off-peak), light conditions (daylight versus darkness), and vehicle types (passenger vehicles versus trucks). Compared with the pre-DSFS site condition, the DSFS reduced curve entry speeds and improved brake response at two of the three ramp locations. In general, the greatest beneficial effects on driver behavior were achieved when the DSFS was positioned at the point of curvature, during which curve entry speeds were reduced by approximately 2 mph. These findings were consistent between the system interchanges and service interchanges, and across all vehicle types. The DSFS was also found to be most effective during daytime off-peak periods compared with peak periods and at night. Further evaluation of DSFS at additional ramp locations, and considering an expanded set of conditions, is recommended.
Since the increasing spread of COVID-19 in the U.S., with currently the highest number of confirmed cases and deaths in the world, most states in the nation have enforced travel restrictions resulting in drastic reductions in mobility and travel. However, the overall impact and long-term implications of this crisis to mobility still remain uncertain. To this end, this study develops an analytical framework that determines the most significant factors impacting human mobility and travel in the U.S. during the pandemic. In particular, we use Least Absolute Shrinkage and Selection Operator (LASSO) to identify the significant variables influencing human mobility and utilize linear regularization algorithms, including Ridge, LASSO, and Elastic Net modeling techniques to model and predict human mobility and travel. State-level data were obtained from various open-access sources for the period from January 1, 2020 to June 13, 2020. The entire data set was divided into a training data-set and a test data-set and the variables selected by LASSO were used to train four different models by ordinary linear regression, Ridge regression, LASSO and Elastic Net regression algorithms, using the training data-set. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that among all models, the Ridge regression provides the most superior performance with the least error, while both LASSO and Elastic Net performed better than the ordinary linear model.
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.
Understanding speed selection behavior of drivers following speed limits 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, though 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 miles 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 3.8 to 5.1 mph. The control sites experienced marginal changes in speeds, which suggests 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 as to the nature of driver speed selection and the impacts of speed limit increases.
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