1 Roadway segments that include h orizontal curves experience a disproportionate number of crashes 2 compared to straight segments. M any of these crashes are lane departure -related , and e xcessive 3 speed is often a contributing factor . One parti cularly vulnerable area for such crashes is freeway 4 interchange ramps, which require a substantial reduction in speed to be safely negotiated. While 5 dynamic speed feedback signs (DSFS) have been found to be an effective speed and crash 6 reduction countermeasure at horizontal curves, the use of such signs on freeway interchange ramps 7 has been limited nat ionwide. Consequently, the effectiveness of DSFS as a speed reduction 8 countermeasure in such settings has remained largely untested. A before -and-after field evaluation 9 was performed at three freeway exit ramps to assess the impacts of a DSFS on driver sp eed 10 selection and braking characteristics while approaching and entering the ramp curve s. The 11 effectiveness of the feedback sign was tested across various conditions, including sign location, 12 interchange type, time of day, light condition, and vehicle typ e. In general, the greatest benefits 13 to driver behavior were achieved with the DSFS positioned at the point of curvature, during which 14 curve entry speeds were reduced by approximately 2 mph compare d to the pre -DSFS condition . 15 These findings were consiste nt between the system - and service -interchanges and across all 16 vehicle types. The DSFS was also found to be most effective during daytime off -peak periods 17 compared to peak periods and at night . Based on the study findings, the continued use of DSFS 18 as a speed reduction treatment at free way exit ramps is recommended. 19 20 21 Keywords: dynamic speed feedback sign, freeway ramps, horizontal curves, lane departure 22 crashes, driver behavior 23 2 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen INTRODUCTION 1 Horizontal curves pose a serious safety concern due to the increased probability of lane departures, 2 particularly at high speeds. Histori cally, a disproportionate number of crashes have occurred on 3 horizontal curves compared to tangent roadway sections. On two -lane rural highways, segments 4 containing horizontal curves have been found to have crash rates that are three times greater than 5 similar tangent segments ( 1). Furthermore, nearly 25 percent of fatal crashes occur on curves ( 2), 6 and approximately 75 percent of these fatal crashes were lane departure crashes involving a single 7 vehicle running off the road. In 2018, there were 16,794 l ane departure fatal crashes nationally, 8 which accounted for approximately half of the total fatal crashes ( 3). In Michigan alone, there were 9 400 fatalities due to lane departures in 2018 ( 4). Although the crashes on horizontal curves were 10 mostly correlated to the geometric factors (5), lane departure crashes on curves are largely 11 influenced by driver -related factors including driver distraction, impairment, and most 12 importantly, speeding. Approximately half of the fatal crashes on curves were related to spe eding 13 and single vehicle run -off-road ( 6). 14 One particularly vulnerable area for curve -related lane departure crashes is freeway ramps, 15 which, due to right -of-way constraints and other factors, often include horizontal curves requiring 16 a significant reduct ion in speed to be safely negotiated . Nationwide, 697 fatal crashes occurred on 17 exit or entrance ramps in 2018 ( 3), the majority of which are single -vehicle lane departure. Several 18 traffic control strategies have been deployed nationwide to counter lane departure crashes on 19 freeway ramp curves, including signs, warning be acons, and horizontal signing. Despite these 20 efforts, the problem continues to persist and new treatments are needed. 21 A promising countermeasure to reduce curve speeds on freeway interchange ramps is the 22 dynamic speed feedback sign (DSFS), which uses real -time speed detection to provide targeted 23 warning messages to drivers. These signs have been found to effectively reduce speeds on mainline 24 horizontal curves ( 5, 7), although their use on freeway interchange ramps has been limited. Thus, 25 the effectiveness of the signs in such settings is not well established. A recent study by Gates et al. 26 (8) presented a before -and-after field evaluation at a freeway interchange ramp using various DSFS 27 messaging and positioning strategies. The study found a significant reduction in curve entry speeds 28 when the DSFS was positioned near or at the point of curvature (PC) and when the speed number 29 display alternat ed with a “SLOW DOWN” message. While tha t study provided encouraging 30 results, an expanded evaluation at additional freeway ramp locations was required to further 31 confirm effectiveness of the DSFS. 32 To that end, research was undertaken to determine the effectiveness of DSFS applications 33 on mult iple freeway ramps with significant horizontal curvature. The primary objective of this 34 research was to determine the effect of DSFS on ramp curve speeds across a variety of conditions, 35 including sign location s (point of curvature vs. upstream), times-of-day (peak vs. off -peak), light 36 condition s (daylight vs. darkness), interchange type s (system vs. service), and vehicle type s 37 (passenger vehicles vs. trucks). 38 39 LITERATURE SUMMARY 40 DSFS relay active real -time speed feedback information to approaching drivers on a digital 41 display. Such devices have been evaluated as a speed - and crash -reduction countermeasure across 42 a variety of settings, including school zones ( 9), work zones ( 10, 11), speed transition zones ( 9, 43 12, 14), sharp horizontal curves ( 7, 9, 15), and high-speed arterials ( 9, 13, 14, 16). Studies have 44 also evaluated the effectiveness of DSFS on driver behavior based on the road geometry ( 17), sign 45 placement ( 18), and the message being displayed ( 19). 46 3 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen Evaluation of DSFS at rural highway curves ha s been relatively limited. In this setting, 1 DSFS have been found to reduce curve speeds by approximately 2 mph for both passenger cars 2 and trucks ( 7, 9, 15), and these speed reductions have been sustained over the first 24 months of 3 sign operation ( 7). The most c omprehensive evaluation of crashes involving DSFS use at 4 horizontal curves found a 5 to 7 percent reduction in crashes during the first three yea rs after 5 installation of DSFS at 22 two -lane rural highway curves across seven states ( 7). 6 The effecti veness of DSFS also depends on their positioning and message. On straight 7 sections of roadway, the greatest speed reductions are observed upstream of the sign ( 20, 21), with 8 diminished effects downstream of the sign ( 21). These findings have led to the rec ommendation 9 of that the sign be positioned at or before the critical location or hazard ( 22). The message that the 10 DSFS displays can also impact a driver’s behavior. Full matrix displays that are able to provide 11 messages such as ‘SLOW DOWN’ when the drive r is exceeding a certain speed threshold have 12 been found to outperform signs that simply display the driver’s speed ( 7, 8, 9, 11, 15, 19 ). 13 While these studies collectively suggest that DSFS are effective for reducing speeds and 14 speed -related crashes on ru ral highway curves, none considered the effects of DSFS on interchange 15 ramp curves, thereby further supporting the need for additional research. Further, while prior work 16 by the authors ( 8) have shown promising effects on driver behavior associated with t he use of a 17 DSFS on a single freeway interchange ramp, further research was needed to explore the conditions 18 under which DSFS are most effective when used as a speed warning treatment on freeway ramps. 19 20 METHODOLOGY 21 A before -and-after field evaluation was performed at three freeway ramp curve locations to 22 determine the effectiveness of DSFS on curve entry speeds . The effectiveness of the DSFS was 23 tested across various sign locations (point of curvature vs. upstream), interchange type s (syste m 24 vs. se rvice), vehicle type s (passenger vehicles vs. trucks) , times -of-day (peak vs. off -peak), and 25 light conditions (daylight vs. darkness). The following subsections detail the various aspects of 26 this evaluation, including sign selection, site selectio n, sign test conditions, and field data 27 collection. 28 29 Test Sign 30 A single DSFS test sign was utilized for this field study. This sign, which is shown in Figure 1 31 displaying example messages , was a 40 -inch by 31 -inch sign with microprismatic reflective yell ow 32 sheeting with black “YOUR SPEED” text and a full matrix amber LED feedback display capable 33 of displaying characters of up to 15 inches in height. For testing purposes, the sign included a 140 34 amp-hour battery system that powered the radar feedback sign for a minimum of three weeks on a 35 single charge. The radar unit was specified by the manufacturer to have a detection range for a 36 typical passenger vehicle of approximately 400 feet, which extended up to 600 feet for trucks. 37 The sign was compliant with the Michigan Department of Transportation’s (MDOT) draft s pecial 38 provision for DSFS. Further details pertaining to sign selection can be found in reference 8. 39 40 4 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen 1 FIGURE 1 Dynamic speed feedback s ign for field evaluation . 2 3 Site Selection 4 Three freeway exit ramp locations were selected for this study based on the following criteria: 5 • High frequency of lane departure crashes, 6 • Advisory speed of 30 mph or below, 7 • High traffic volumes, including a considerable proportion of trucks, 8 • DSFS sign installation capability (i.e., no roadside obstructions or terrain issues) , and 9 • Suitab le for data collection. 10 To compare the signs effects based on the interchange type, it was also desirable to include 11 at least one system interchang e and one service interchange. Ultimately, three exit ramp locations 12 were selected for t his study , which included westbound (WB) I -96 to southbound (SB) I -69 (Site 13 1), northbound (NB) US -127 to Round Lake Road (Site 2) , and eastbound (EB) I-96 to 36th S treet 14 (Site 3) . Site 1 was a freeway -to-freeway interchange ramp and the other two were exit ramps at 15 service interchanges . Detailed information on the three study s ites is provided in Table 1. A typical 16 plan view of the existing signage layout, DSFS location , and dat a collection camera locations for 17 each of the three sites is provided in Figure 2. Each of the sites possessed MUTCD compliant 18 warning signag e, and were considered representative of freeway exit ramps in Michigan. 19 20 TABLE 1 Site c haracteristics and DSFS test conditions 21 No Location Mainline Speed Limit/Ramp Advisory Speed (mph) Interchange Type Feedback Message DSFS Location 1 WB I -96 to SB I-69 70/30 System Speed number with alternating “Slow Down” message and advisory speed panel Curve PC 2 NB US -127 to Round Lake R d 75/30 Service Speed number with alternating “Slow Down” message Curve PC 3 EB I-96 to 36th St 70/20 Service Speed number with alternating “Slow Down” message 350 feet upstream of curve PC Note: PC = point of curvature 22 5 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen 1 a. Site 1. WB I -96 to SB I -69 b. Site 2. NB US -127 to Round Lake Rd 2 3 4 c. EB I -96 to 36th Street 5 FIGURE 2 Freeway exit ramp locations for DSFS field evaluation . 6 6 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen Test Conditions 1 In addition to testing the general effectivene ss of the DSFS as a driver behavior measure, it was 2 also desirable to assess differences in the sign eff ectiveness across various conditions. One such 3 condition was to vary the DSFS location with respect to the point of curvature between sites . The 4 DSFS was positioned at the point of curvature (PC) at Sites 1 and 2. However, the DSFS was 5 positioned 350 upstream of the curve at site 3. Furthermore, Site 1 was a freeway -to-freeway 6 system interchange, while Sites 2 and 3 were service interchanges , which further allowed for 7 assessment of differences in DSFS effective ness between interchange types. Finally time -of-day 8 and light condition effects were assessed by using a data collection trailer to measure curve entry 9 speeds before and after installa tion of the DSFS at Site 2. 10 One aspect that remained uniform between the three locations was t he sign messaging 11 strategy . During all test conditions, t he DSFS was programmed to display the speed of vehicles 12 that were approaching below 40 mph, alternating with a “SLOW DOW N” message at 0.5 sec 13 intervals for vehicles traveling 40 mph and above. This messaging strategy followed the current 14 MDOT draft spec ial provision for DSFS and was found in the i nitial field study to provide the 15 greatest impact on driver behavior ( 8). A standard advisory speed panel was also installed with 16 the DSFS at Site 1. However, t he use of an advisory speed panel was not found to impact the 17 effectiveness of the DSFS during the initial field study ( 8). 18 19 Video Data Collection 20 Data were collected from each study location in two phases: 1) under the existing site conditions 21 without the DSFS present and 2) after the installation of the DSFS. This data collection was done 22 during clear weathe r conditions during weekday off -peak hours. The existing signage at the sites 23 were not modified or changed in any way. The sign remained in operation for approximately three 24 weeks at each site. D ata collection was delayed until at least seven days after the DSFS installation 25 to mitigate any novelty effect associated with the new device . 26 A series of three pole-mounted high definition video cam eras were installed at various 27 points on the approach to and within the curve. The cameras were temporarily attached to the 28 existing roadsi de signposts, similar to that shown in Figure 3. The cameras were elevated to a 29 height of 15 f eet and aimed towards a predetermined location on the roadway. Each camera 30 recorded ap proximately 3 hours of video during each data collection period . Data were collected 31 across multiple days before and after DSFS installation at each site. The cameras were installed 32 at the sam e locations to provide approximately identical views during each data collection period. 33 The three camera setup locations included the following approximate locations : 34 • 1,000 feet upstream of the curve to provide coverage of the area upstream of the DSFS 35 detection zone; 36 • 450 feet upstream of the curve to provide coverage of the area where the DSFS typically 37 became illuminated for approaching vehicles; and 38 • 150 feet after the point of curvature to provide coverage of the entry to the curve and 39 approximately 200 feet beyond the point of curvature . 40 An example view from each of these three camera l ocations is displayed in Figure 3 . 41 42 7 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen a. Example camera setup b. View from upstream camera c. View from the middle camera d. View from cu rve camera FIGURE 3 Example camera setup and field of view provided by each camera . 1 2 After completion of the field data collection, the videos were manually reviewed by a team 3 of trained technicians to assess various characteristic of driver behavior. The view afforded by the 4 elevated camera setup s allows direct observ ation of numerous veh icular and operational attributes, 5 including vehicle type, headway, speed, and brake lights. Further, various aspects related to the 6 DSFS and site condition were also assessed from the videos, including location of the vehicle at 7 DSFS message onset, DSFS m essage displayed to each vehicle, traffic volumes, weather, and 8 interference from vehicles parked on the shoulder. 9 The videos were reviewed and e ach vehicle was sequentially tracked through the videos 10 across the three camera setups. Quicktime software was utilized, which allowed for frame -by- 11 frame review to determine the relevant vehicular location and time information. The video s were 12 recorded at a rate of 60 frames per second, allowing time to be recorde d to the nearest 0.0167 13 seconds based on the frame n umber displayed in the video player. Paint marks placed on the 14 shoulder at 50 -ft intervals (shown in Figure 3d) were used as field reference markers for 15 determining the relative location of a vehicle with respect to the point of curvature at any point in 16 time. The following information was obtained from the video s for each vehicle traveling through 17 the site: 18 • Time to traverse the following speed measurement zones: 19 o 1000 -ft upstream of the point of curvature (prior to the DSFS detection area), 20 o At the point of curvature , 21 • Time headway from the prior vehicle, 22 • Location (with respect to the point of curvature ) of initial braking , 23 • Whether a message was displayed on the DSFS, and 24 • Vehicle type: 25 o Passenger vehicle (car, SUV, pickup, van, minivan, motorcycle ) without a trailer, 26 o Passenger vehicle with a trailer, 27 o Single unit truck/bus/RV , or 28 8 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen o Tractor trailer truck. 1 Speed Trailer Data Collection 2 Although collection of driver behavior data using video cameras affords vehicle tracking 3 capabilities , their use is limited to daylight conditions and data extractio n is very labor -intensive, 4 which further limits sample size s. Thus, i n order to obtain a comprehensive sample of curve entry 5 speeds a cross various times of day and light conditions, a speed data collectio n trailer was 6 positioned at Site 1 (US127/Round Lake Rd) just beyond the right shoulder near the point of 7 curvature (Figure 4). The speed trailer utilized an elevated Wavetronix SmartSensor HD side- 8 firing radar unit , which was aimed and calibrated to contin uously measure speed and length data 9 for vehicles at the ramp curve entry point . 10 The speed trailer remained in a fixed position and continuously recorded for a 36 day period 11 from November 6, 2019 through December 12, 2019. Data were collected under the existing site 12 condition, without the DSFS, for the initial 20 days. The DSFS was installed at the point of 13 curvature, immediately in advance of the speed trailer, on November 26, 2019 , thus allowing for 14 16 days of data collection with the DSFS in operation . To limit data file -size, the radar was 15 programmed to bin data into 30 second intervals. To isolate free -flowing vehicles, the data were 16 screened to include only those intervals that included a single vehicle. Further screening was 17 conducted to remove p otential mainline observations and other anomalies. 18 A total of 17, 433 vehicles were included in the final dataset for further analysis . The 19 dataset included 16,839 passenger vehicles and 594 heavy vehicles, which were defined in this 20 study as vehicles longer than 32 feet. To account for changes in weather at the site, weather 21 condition data were collected from a nearby National Weather Service station and combined with 22 the speed data. Furthermore, sunrise and sunset data were also included to determine any 23 differential effects of the DSFS on curve entry speeds under varied lighting conditions. 24 25 26 FIGURE 4 DSFS and speed t railer at US -127 e xit to Round Lake Rd. 27 28 29 9 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen Data Screening 1 The video data collected from three sites were combined, organized , and coded into a single file 2 for detailed analysis. The data set w as screened to include only vehicle observations with a 3 headway of 3 seconds or greater to eliminate the effects of vehicle platooning. Cases where no 4 feedback message was displayed on the DSFS for an approaching vehicle were also removed from 5 the dataset. The final combined data set included a complete record for 1,758 vehicle observations 6 collected from three sites. It should be noted that brake light data could not be discerned from one 7 data collection period due to a slight misalignment of the camera, which reduced the sample size 8 for the braking data to 1,651 . 9 Turning to the speed trailer data, initial analysis found curve entry spe eds to vary widely 10 during poor weather conditions, including rain, sleet, ice, and snow. This is not surprising, as it is 11 difficult to characterize the impacts to visibility and pavement surface during such conditions. 12 Thus, the speed trailer data were l imited to only those periods that occurred during clear, cloudy , 13 or fair conditions. Using the time -stamps of each data bin, the speed trailer data were also coded 14 for further analysis based on time s-of-day (peak vs. off -peak) and light condition s (daylig ht vs. 15 darkness). For this study, the peak period was defined as 7:00 – 9:00 AM and 3:30 to 6:00 PM. 16 Daylight and darkness were coded based on sunrise (between 7:19 and 8:00 AM) and sunset 17 (between 5:05 and 5:26 PM) for each day during the data collectio n period. The data we re then 18 classified for analytical purposes as follows: peak, daytime off -peak , and nighttime. Thus, all 19 daytime off -peak occurred during daylight periods, all nighttime occurred during dark periods, 20 while peak included a mix of daylig ht, twilight, and dark periods. In using this coding structure, 21 it was possible to assess whether the DSFS had a variable effect on the behavior of peak (e.g. 22 commuter) vs. off -peak drivers and during different lighting conditions. 23 To determine any obvi ous trends in the data, sources for potential bias, and data 24 distributions, the authors initially compared the descriptive statistics (i.e., mean, standard 25 deviation, percentiles, etc.) and simple graphical representations (i.e., frequency distribution , box 26 plot, scatterplot ) of the vehicular data across each data collection period. Table 2 presents the 27 descriptive statistics of the combined dataset that included 1,758 vehicle observations from the 28 videos across the three sites and 17,433 observations from the speed trailer. 29 30 31 10 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen TABLE 2 Descriptive Statistics 1 Video Camera Data (N = 1,758) Variable Unit Min Max Mean Std Dev Speed 1000 feet upstream of the pc mph 30.303 83.832 58.949 7.701 Speed at point of curvature ( i.e., curve entry) mph 17.045 57.273 38.388 5.496 Speed within 15 mph of advisory speed 0 1 0.656 0.475 Braking first initiated 200 -600 ft prior to PC* 0 1 0.321 0.467 Passenger vehicle without trailer 0 1 0.820 0.385 Passenger vehicle with trailer 0 1 0.018 0.132 Single unit truck/bus/RV 0 1 0.039 0.194 Tractor trailer truck 0 1 0.123 0.329 Existing site condition (No DSFS) 0 1 0.557 0.497 DSFS at point of curvature - I-96/I-69 0 1 0.136 0.343 DSFS at point of curvature - US-127/Round Lake Rd 0 1 0.133 0.340 DSFS at 350 ft upstream of point of curvature - I-96/36th 0 1 0.173 0.379 Speed Trailer Data (N = 17,433 ) Variable Unit Min Max Mean Std Dev Speed at point of curvature ( i.e., c urve entry) mph 25.100 54.900 39.039 5.459 Passenger vehicle 0 1 0.966 0.181 Heavy vehicle ( longer than 32 ft) 0 1 0.034 0.181 Daytime Off -Peak ( 9:00 AM – 3:30 PM) 0 1 0.394 0.489 Peak ( 7:00 AM – 9:00 AM; 3:30 PM – 6:00 PM) 0 1 0.178 0.382 Nighttime (6:00 PM – 7:00 AM ) 0 1 0.428 0.495 No DSFS 0 1 0.557 0.497 DSFS at point of cu rvature 0 1 0.443 0.497 *Sample size, N= 1,651 2 3 ANALYSIS 4 Several measures of effectiveness related to curve entry speed and brake response were analyzed 5 to determine the effects of the DSFS as a function of sign location, inte rchange type, and time -of- 6 day. Three primary analyses were performed using appropriate statistical procedures. The 7 dependent variables for these analyses included: 8 • Speed at the point of curvature ( i.e., curve entry) , 9 • Probability of initial brake response occurring between 200 and 600 ft upstream of the PC; 10 this range was selected as it afforded a comfortable braking distance prior to curve entry 11 and also corresponded with the detection range of the DSFS radar, and 12 • Probability of a vehicle entering the curve within 15 mph of the advisory speed . 13 Preliminary models suggested only minor differences in DSFS effectiveness between 14 passenger vehicles and heavy vehicles and further analysis of vehicle -specific DSFS effect s was 15 not performed . For all models, the upstream approach speed was included as a covariate to control 16 for general driving behavior. All analyses were performed using RStudio . Curve entry speeds were 17 analyzed using linear regression including a random effect (intercept) term , with the form shown 18 in equation 1: 19 20 𝑌𝑖= 𝛽0+𝛽1𝑋𝑖1+𝛽2𝑋𝑖2+⋯+𝛽𝑘𝑋𝑖𝑘+𝜀𝑖+𝛿𝑖 (1) 21 22 11 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen where Yi is the measured speed at the point of curvature for vehicle i, 𝑋𝑖1 to 𝑋𝑖𝑘 are indep endent 1 variables affecting this speed (including DSFS test condition) , β0 is an intercept, β1 to βk are 2 estimated regression coefficients for each independent variables, and 𝜀𝑖 is a normally distributed 3 error term with variance 𝜎2. The 𝛿𝑖 term is a random intercept term, which accounts for i mportant 4 unobserved factors affecting driver behavior duri ng the data collection periods . A site -specific 5 random intercept term was utilized in the video speed data model s to account for the unobserved 6 differences between sites. As the speed trailer was only used at a single site, a date-specific random 7 intercep t term was utilized for these models to account for unobserved day -to-day differences. 8 Binary l ogistic regression was utilized to analyze the binary response variables, which 9 included the probability of braking 200 -600 ft in advanc e of the curve and the probability of a 10 vehicle entering the curve below within 15 mph of the advisory speed . The binary logistic 11 regression model has the form: 12 13 𝑌𝑖=𝑙𝑜𝑔𝑖𝑡 (𝑃𝑖)=𝑙𝑛(𝑃𝑖 1− 𝑃𝑖)= 𝛽0+𝛽1𝑋𝑖1+𝛽2𝑋𝑖2+⋯+𝛽𝑘𝑋𝑖𝑘+ 𝛿𝑖 (2) 14 15 where the response variable, Yi, is the logistic transformation of the probability of vehicle i braking 16 200-600 ft in advance of the curve, or entering the curve within 15 mph of the advisory speed . 17 This probability is denoted as Pi. As in the linear regr ession model, 𝑋𝑖1 to 𝑋𝑖𝑘 are independent 18 variables aff ecting driver behaviour (including DSFS test condition) , β0 is an intercept, β1 to βk 19 are estimated regression coefficients for each independent variables , and 𝛿𝑖 is a random intercept 20 term similar to the previous one discussed earlier. 21 22 RESULTS AND DISCUSSION 23 The linear regression results for curve entry speeds (e.g. at the point of curvature) obtained from 24 the video data are displayed in Table 3 , while the binary logistic regression results for excessive 25 curve entry speeds and brake response location are displayed in Table s 4 and 5, respectively . 26 Again, to control for unobserved variations between the sites, a site -specific random effect 27 (intercept) was included in each model. 28 29 TABLE 3 Random Intercept Linear R egression Results for Curve Entry Speed 30 Response Variable: Speed At Point of Curvature (N=1,758) Parameter Estimate Std. Error t-value p-value Intercept 14.983 1.175 12.746 <0.001 Speed 1000 -ft Upstream of the Point of Curvature 0.414 0.014 29.208 <0.001 Passenger Vehicle with No Trailer Base Condition Passenger Vehicle With Trailer -1.339 0.665 -2.015 0.044 Single Unit Truck/ Bus -1.969 0.463 -4.252 <0.001 Tractor Trailer Truck -3.290 0.309 -10.648 <0.001 Existing Site Condition (No DSFS) Base Condition DSFS at Point of Curvature - I-96/I-69 -2.290 0.299 -7.665 <0.001 DSFS at Point of Curvature - US-127/Round Lake Rd -1.975 0.331 -5.970 <0.001 DSFS at 350 ft upstream of Point of Curvature - I-96/36th 0.967 0.283 3.415 <0.001 31 32 12 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen TABLE 4 Random Intercept Logistic R egression Results for Excessive Curve Entry Speed 1 Response Variable: Speed at Point of Curvature within 15 mph of the Advisory Speed (N=1,758) Parameter Estimate Std. Error t-value p-value Exp(B) Intercept 12.846 1.390 9.245 <0.001 379268 Speed 1000 -ft Upstream of the Point of Curvature -0.201 0.014 -13.936 <0.001 0.818 Passenger Vehicle with No Trailer Base Condition Passenger Vehicle With Trailer 1.959 0.977 2.006 0.045 7.092 Single Unit Truck/ Bus 1.650 0.486 3.395 0.001 5.207 Tractor Trailer Truck 2.387 0.411 5.809 0.000 10.881 Existing Site Condition (No DSFS) Base Condition DSFS at Point of Curvature - I-96/I-69 0.548 0.296 1.851 0.064 1.730 DSFS at Point of Curvature - US-127/Round Lake Rd 0.901 0.275 3.276 0.001 2.462 DSFS at 350 ft upstream of Point of Curvature - I-96/36th -0.300 0.215 -1.395 0.163 0.741 2 TABLE 5 Random Intercept Logistic R egression Results for Brake Response Location 3 Response Variable: Braking Began 200 -600 ft Before Curve (N=1, 651) Parameter Estimate Std. Error t-value p-value Exp(B) Intercept -2.357 0.500 -4.713 <0.001 0.095 Speed 1000 -ft Upstream of the Point of Curvature 0.026 0.008 3.155 0.002 1.026 Passenger Vehicle with No Trailer Base Condition Passenger Vehicle With Trailer -0.148 0.408 -0.364 0.716 0.862 Single Unit Truck/ Bus -0.379 0.309 -1.230 0.219 0.685 Tractor Trailer Truck -0.087 0.191 -0.458 0.647 0.419 Existing Site Condition (No DSFS) Base Condition DSFS at Point of Curvature - I-96/I-69 0.368 0.155 2.375 0.018 1.445 DSFS at Point of Curvature - US-127/Round Lake Rd 0.599 0.151 3.953 <0.001 1.820 DSFS at 350 ft upstream of Point of Curvature - I-96/36th -0.120 0.149 -0.803 0.422 0.887 Note: Each model included a random intercept for site. Represents data collected from video cameras. 4 5 First, as expected, the speed of vehicles measured 1,000 ft prior to the cur ve was strongly 6 correlated with each of the driver response variables. Specifically, this suggests that faster drivers 7 tended to maintain such behaviors regardless of the DSFS presence at the site, and is aligned with 8 prior behavioral research. Further discussion of the primary variables of interest, including the 9 effects of sign location, interchange type, and time of day on DSFS performance are provided in 10 the sections that follow. 11 12 Effect of Sign Location 13 The parameter estimates in Table 3 indicate that curve entry speeds were approximately 2.0 to 2.3 14 mph lower with the DSFS present at the two ramp locations where the sign was positioned directly 15 at the point of curvature. The DSFS installed at the point of curvature also decreased the 16 occurrence of excessive curve entry speeds , as in dicated in Table 4 . Specifically, compared to the 17 existing site condition, drivers were 1.7 to 2.5 times mor e likely to enter the curve within 15 mph 18 of the curve advisory speed with the DSFS installed at the point of curvature. Furthermore, Table 19 5 suggests that the DSFS also improved brake response when posted at the point of curvature. 20 With the DSFS at this location, drivers were 1.4 to 1.8 times more likely to initiate braking between 21 200 and 600 ft upstream of the curve. It is also worth noting that the DSFS had a similar effect on 22 driver response across all vehicle categories. These results are aligned with prior findings 23 associated with DSFS use in similar freeway ramp settings ( 8). 24 In contrast, the DSFS was found to be ineffective across all measures of driver response 25 when posted 350 ft upst ream of the point of curvature. At this particular ramp location, curve entry 26 13 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen speeds were found to increase by approximately 1 mph with the DSFS present . The DSFS had no 1 significant effect on reducing excessive curve entry speeds or improving brake response location 2 at this particular s ite. 3 4 Effect of Interchange Type 5 It was also of interest to compare the effects of DSFS on driver response across different 6 interchange types, including freeway -to-freeway system interchanges and service interchanges. 7 This effect was evaluated by comparing the DSFS results between the I -96/I-69 (system 8 interchange) and US -127/Round Lake Road (service interchange) ramps. Each of these ramps was 9 a cloverleaf -type interchange with a 30 mph ramp advisory speed. The DSFS was installed directly 10 at the point of curvature in both cases , which, as previously noted, elicited improved driver 11 response compared to the further upstream sign location. 12 Considering curve entry speeds, the DSFS had a slightly (0.3 mph) greater speed reduction 13 effect at the I -96 freeway -to-freeway interchange. However, th e DSFS was somewhat more 14 effective at reducing excessive curve entry speeds and improving brake response when used at the 15 US-127 service interchange. These findings suggest that the DSFS is equally effective at system 16 and service interchange exit ramps wh en insta lled at the point of curvature. Figure 5 provides a 17 graphical representation of the site-specific marginal effects , which demonstrate the effects of the 18 DSFS on curve entry speeds related to the sign location and interchange type. 19 20 21 FIGURE 5 Mean curve entry speeds by site and DSFS presenc e. 22 23 24 14 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen Effect of Time of Day 1 Analysis of the speed trailer data allowed for an assessment of the effects of the DSFS on curve 2 entry speeds across various times of day and light conditions. Again, these data ar e specific to the 3 US127/Round Lake Rd exit ramp, where the speed trailer , along with the DSFS for a portion of 4 the 36 days, were positioned near the point of curvat ure. Separate regression models were 5 generated for curve entry speeds based on time of day a nd light condition, including peak, daytime 6 off-peak, and nighttime. Using these separate models, it was possible to assess whether the DSFS 7 had different effects on the behavior of peak (e.g. commuter) vs. off -peak drivers and during 8 different lighting c onditions. The linear regression results for curv e entry speeds are displayed in 9 Table 6. Again, to control for u nobserved variations between each daily data collection period, a 10 date-specific random effect (intercept) was included in each model. 11 Table 6 presents several interesting findings related to the effectiveness of the DSFS by 12 time-of-day. Most notably, the DSFS has the greatest effect on reducing curve entry speeds during 13 daytime off -peak periods. The magnitude of this DSFS speed reduction eff ect (0.9 mph) is nearly 14 double that observed during peak periods (0.5 mph) and triple that observed during nighttime 15 periods (0.3 mph). This finding suggests that the DSFS has the greatest effects on non -commuter 16 drivers compared to commuter drivers, who are generally more familiar with the site. It should be 17 noted that preliminary models did not sho w any discernable differences in the DSFS effects on 18 curve entry speeds across the vehicle type categories. G raphical representation s of the curve entry 19 speeds by time -of-day and DSFS presence are provided in Figure 6. 20 21 TABLE 6 Random Intercept Linear R egression Results for Curve Ent ry Speed by 22 Peak/Off -Peak Period and Light Condition 23 Daytime Off -Peak (9:00 AM – 3:30 PM) (N=6,869) Parameter Estimate Std. Error t-value p-value Intercept 40.176 0.301 133.566 <0.001 Passenger Vehicle Base Condition Heavy Vehicle -4.911 0.294 -16.727 <0.001 Existing Site Condition ( No DSFS ) Base Condition DSFS at Point of Curvature -0.914 0.442 -2.068 0.048 Peak (7:00 AM – 9:00 AM and 3:30 PM to 6:00 PM) (N=3,096) Parameter Estimate Std. Error t-value p-value Intercept 39.694 0.292 135.733 <0.001 Passenger Vehicle Base Condition Heavy Vehicle -5.597 0.517 -10.825 <0.001 Existing Site Condition ( No DSFS ) Base Condition DSFS at Point of Curvature -0.488 0.435 -1.123 0.270 Nighttime (6:00 PM – 7:00 AM) (N=7,468 ) Parameter Estimate Std. Error t-value p-value Intercept 38.249 0.241 158.946 <0.001 Passenger Vehicle Base Condition Heavy Vehicle -3.671 0.453 -8.110 <0.001 Existing Site Condition ( No DSFS ) Base Condition DSFS at Point of Curvature -0.286 0.359 -0.796 0.432 Response variable: Speed at point of curvature (curve entry) . Each model included a random intercept for the date. 24 Represents data collected by the speed trailer at US -127/Round Lake Rd. 25 15 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen 1 FIGURE 6 Mean c urve entry s peeds by DSFS presence and time of day. 2 3 It is important to note that unlike the video data, it was not possible to track vehicles 4 through the site, as th e speed trailer only records a single speed measurement for each vehicle. 5 Thus, it was not possible to correlate driver speeding tendencies upstream of the site with their 6 curve entry speed. As a driver’s upstream speed was found to be highly correlated w ith curve entry 7 speed during the analysis of the video data, the inability to control for general speed selection 8 tendencies was an important limitation to any inference drawn from the speed trailer data. 9 10 SUMMARY, CONCLUSIONS , AND RECOMMENDATIONS 11 A series of field evaluations were performed at three freeway interchange ramps with significant 12 horizontal curvature to assess the impacts of a dynamic speed feedback sign on driver speed 13 selection and brake response while approaching and entering the curve. A single speed fe edback 14 sign with a 15 -inch full -matrix display was utilized at each of the three exit ramp locations. The 15 sign was programmed to display the same feedback message at each location, which included the 16 speed number for all approaching vehicles, which alternated with a SLOW DOWN message for 17 vehicles approaching above 40 mph. The effectiveness of the feedback sign was tested across 18 various sign locations (at the point of curvature vs. 350 ft upstream), interchange typ es (system vs. 19 service), times-of-day (peak vs. off -peak), light condition s (daylight vs. darkness) , and vehicle 20 types (passenger vehicles vs. trucks) . 21 Compared to the pre-DSFS site condition , the DSFS reduced curve entry speeds and 22 improved brake response at two of the three ramp locations. In general , the greatest benefits to 23 driver behavior were achieved when the DSFS was positioned at the point of curvature, during 24 which curve entry speed s were reduced by approximately 2 mph. T hese findings were consistent 25 16 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen between the sy stem - and service -interchanges and across all vehicle types. The feedback sign was 1 found to have an ineffective impact on driver behavior when positioned 350 -ft upst ream of the 2 point of curvature. Consistent with prior speed research ( 8), it was concluded that drivers are more 3 likely to disregard speed warning message s when provided too far in advance of the hazard. 4 The DSFS was also found to be most effective during daytime off -peak periods than during 5 peak periods and during darkness. This finding suggests that the DSFS has the greatest effects on 6 non-commuter drivers, and may be reflective of such drivers being more likely to respond to the 7 speed warning message due to a reduced familiarity with the ramp geometry. On the other hand, 8 commuter drivers are less likely to respond to the DSFS due to familiarity with the site, which may 9 also at least partially explain the lack of an effect for nighttime drivers. 10 Based o n the study findings, the continued use of DSFS as a speed reduction treatment at 11 freeway ramp curves is recommended. Specifically, the sign should be positioned to provide 12 adequate time for drivers to perceive and react to the message, such that comfortab le braking can 13 be accommodated prior to reaching the curve. However , the authors caution against placing the 14 sign at too great a distance upstream of the curve , as drivers may be more likely to disregard such 15 an early warning message. 16 Finally, t his study has provided useful insight s into the effectiveness of DSFS on freeway 17 interchange ramps that include significant horizontal curvature. However, further evaluation of 18 DSFS at freeway ramps under an expanded set of conditions is recommend ed. This incl udes the 19 assessment of DSFS under varying ramp geometries, evaluating whether the behavioral effects are 20 sustained over the long -term, effect of size of the sign, and the effect of mainline separation 21 distance on the ability for the radar to detect ramp ve hicles. Testing of the DSFS with different 22 sign position s at additional sites is also recommended . Furthermore, future research should also 23 include an assessment of the effectiveness of the DSFS on reducing the frequency and /or severity 24 of lane departure c rashes on freeway exit ramp s. 25 26 DISCLAIMER 27 Funding for this research was provided by the Michigan Department of Transportation ( MDOT ). 28 This publication is disseminated in the interest of information exchange. MDOT expressly 29 disclaims any liability, of any kind, or for any reason, that might otherwise arise out of any use of 30 this publication or the information or data provided in the publication. MDOT further disclaims 31 any responsibility for typographical errors or accuracy of the informati on provided or contained 32 within this publication. MDOT makes no warranties or representations whatsoever regarding the 33 quality, content, completeness, suitability, adequacy, sequence, accuracy or timeliness of the 34 information and data provided, or that the contents represent standards, specifications, or 35 regulations. 36 37 CONTRIBUTIONS 38 The authors confirm contribution to the paper as follows: study concept and design: Timothy 39 Gates, Md Shakir Mahmud, and Anthony Ingle ; data collection: Timothy Gates , Matt Motz , and 40 Travis Holpuch ; analysis and interpretation of results: Md Shakir Mahmud , Timothy Gates , and 41 Peter Savolainen ; draft manuscript preparation: Md Shakir Mahmud, Matt Motz, and Timothy 42 Gates. All authors reviewed the results and approved the final versio n of the manuscript. 43 44 45 46 17 Mahmud, Motz, Holpuch, Ingle, Gates, and Savolainen