Evaluation of Dynamic Speed Feedback Signs on Curves: A National Demonstration Project

CHAPTER 1. BACKGROUND

INTRODUCTION

The Federal Highway Administration (FHWA 2009) estimates that 58 percent of roadway fatalities are lane departures, while 40 percent of fatalities are single-vehicle (SV) run-off-road crashes. Addressing lane-departure crashes is therefore a priority for national, State, and local agencies. Horizontal curves are of particular interest because they have been correlated with overall increased crash occurrence. Curves have approximately three times the crash rate of tangent sections (Glennon et al. 1985).

Curve-related crashes have a number of causes, including roadway and driver factors. The frequency and severity of curve-related crashes have been correlated to roadway geometric curve factors, including radius, degree of curve, length of curve, type of curve transition, lane and shoulder widths, and preceding tangent length.

A primary driver factor is excessive speed. Factors that contribute to excessive speed include driver workload and distraction, fatigue, sight distance, misperception of the degree of roadway curvature, and situational complexity. The National Highway Traffic Safety Administration (NHTSA 2008) reports that approximately 31 percent of fatal crashes are speed related. A large number of run-off-road fatal crashes on curves are speed related.

The amount of speed reduction needed to traverse a curve has an impact on the frequency and severity of crashes on curves. Large differences between the posted speed limit and speed appropriate to negotiate the horizontal alignment has been suggested as a major cause of crashes on rural two-lane roadways (Luediger et al. 1988). Higher crash rates are experienced on horizontal curves that require greater speed reductions (Anderson et al. 1999).

Driver speed is a major factor in whether drivers will be able to negotiate a curve successfully. Dynamic speed feedback sign (DSFS) systems are one type of traffic control device that has been used to reduce vehicle speeds successfully and, subsequently, crashes, in applications such as traffic calming on urban roads. DSFS systems consist of a speed-measuring device, which may be loop detectors or radar, and a message sign that displays feedback to those drivers who exceed a predetermined speed threshold. The feedback may be the driver's actual speed, a message such as SLOW DOWN, or activation of some warning device, such as beacons or a curve warning sign.

To better understand the effectiveness of DSFS systems in reducing speeds on curves, the Center for Transportation Research and Education (CTRE) at Iowa State University conducted a national field evaluation of these systems on horizontal curves on rural two-lane roadways. The project was sponsored by the FHWA, the Midwest Transportation Consortium, the Iowa Department of Transportation (Iowa DOT), the Iowa Highway Research Board, and the Texas Department of Transportation. The Texas Transportation Institute and Portland State University were partners in the research.

Project Scope and Objectives

Project objectives included the following:

• Conduct a national demonstration project to evaluate DSFS systems in terms of speed and crash reduction on curves on two-lane rural roadways.
• Provide traffic safety engineers and other professionals with additional tools to manage speeds more effectively and decrease crashes on horizontal curves on rural roadways.

The project scope included the following:

• Select a group of geographically representative States to participate in the study.
• Select high-crash curve sites within participating States to serve as treatment and control sites.
• Select types of DSFS systems to be evaluated.
• Collect speed and volume data before and at regular periods after installation of the DSFS system at treatment sites.
• Compare the effectiveness of the DSFS system in reducing speed on curves at treatment sites.
• Conduct statistical analyses to compare the impact of the DSFS system on reducing crashes at using treatment and control sites.
• Report results.

Researchers selected seven States either during the Request for Proposals stage of the project or after the project commenced. A total of 22 DSFS systems were installed in the seven States over a 22-month period. This report presents a summary of how sites were selected, describes how DSFS system types were selected, describes the speed and volume data collection methodology, and presents final results of speed and crash analyses.

Background

This section provides information about the relationship between roadway geometry, vehicle speeds, and crashes on horizontal curves, and the effectiveness of various applications of DSFS systems installed to date.

Relationship Between Curve Crash Rate and Geometry

As previously mentioned, curves have about three times the crash rate of tangent sections (Glennon et al. 1985). Preston (2009) reported that 25 to 50 percent of severe road departure crashes in Minnesota occurred on curves, even though curves account for only 10 percent of the system mileage. Shankar et al. (1998) evaluated divided State highways without median barriers in Washington State and found a relationship between the number of horizontal curves per kilometer and median crossover crashes. Farmer and Lund (2002) evaluated SV fatal and injury rollover crashes using Fatality Analysis Reporting System (FARS) data and data from Florida, Pennsylvania, and Texas. Using logistic regression, Farmer and Lund found that the odds of having a rollover on a curved section were 1.42 to 2.15 times that of having a rollover on a straight section.

The majority of crashes on curves involve lane departures. A total of 76 percent of curve-related fatal crashes are single vehicles leaving the roadway and striking a fixed object or overturning. Another 11 percent of curve-related crashes are head-on collisions (AASHTO 2008).

The frequency and severity of curve-related crashes have been correlated to a number of geometric factors, including radius, degree of curve, length of curve, type of curve transition, lane and shoulder widths, preceding tangent length, and required speed reduction.

Luediger et al. (1988) found that crash rates increased as the degree of curve increased, even when traffic warning devices were used to warn drivers of the curve. Miaou and Lum (1993) found that truck crash involvement increased as horizontal curvature increased, depending on the length of curve. Council (1998) found that the presence of spirals on horizontal curves reduced crash probability on level terrain but did not find the same effect for hilly or mountainous terrain. Vogt and Bared (1998) evaluated two-lane rural road segments in Minnesota and Washington State using Highway Safety Information System (HSIS) data and found a positive correlation between injury crashes and the degree of horizontal curve.

Zegeer et al. (1991) evaluated curves on two-lane roads in Washington State using a linear regression model. Researchers found that the degree of curve was positively correlated with crashes while total surface width and presence of spirals were negatively correlated. Zegeer et al. (1992) also evaluated 10,900 horizontal curves on two-lane roads in Washington State using a weighted linear regression model. They found that crash likelihood increased as the degree and length of curve increased. Mohamedshah et al. (1993), however, found a negative correlation between crashes and degree of curve for two-lane roadways.

Preston (2009) examined severe road departure crashes and found that 90 percent of fatal crashes and 75 percent of injury crashes occurred on curves with a radius of less than 1,500 ft. Milton and Mannering (1998) evaluated 4,386 km of highway in Washington State using a negative binomial model and reported that an increase in radius was associated with decreases in crash frequency. They also found that a shorter tangent length between horizontal curves was associated with decreases in crash frequency. They speculated that drivers might be traveling at lower speeds and were therefore more likely to be paying attention when tangent lengths between curves were short.

Alternatively, Deng et al. (2006) evaluated head-on crashes on 729 segments of two-lane roads in Connecticut using an ordered probit model. They included geometric characteristics in the analysis but did not find that the presence of horizontal or vertical curves was significant.

Taylor et al. (2002) evaluated the relationship between speed and crashes on rural single-carriageway roads in England. The authors collected data from 174 road sections with 60 mph speed limits in a wide range of conditions. Data collected included injury crash data, traffic volume, speed data, and roadway geometry. Speed and flow were measured at each site for 1 or 2 days, and various speed metrics, including mean speed, 85th percentile speed, and standard deviation (SD) of speed, were calculated.

The authors found that crashes were more highly correlated with mean speed than any other speed metric. They also found that crash frequency increased with mean speed. In general, a 10-percent increase in mean speed resulted in a 26-percent increase in the frequency of injury crashes. Results indicated that total crashes increased by 13 percent with each additional curve per kilometer. SV crashes increased by 34 percent per additional sharp curve per kilometer.

Relationship Between Curve Crash Rate and Speed of Curve Negotiation

Although curve-related crashes are correlated to geometric factors, driver factors, such as speeding, also contribute to curve-crash frequency and outcome. Driver factors include driver workload, driver expectancy, and speeding.

Speeding, defined by FHWA as "exceeding the posted speed limit or driving too fast for conditions," in general is problematic. Council et al. (2005) evaluated FARS, General Estimates System, and HSIS data to assess the impact of speeding on fatal crashes. Using 2005 FARS data, they found that 29.5 percent of fatal crashes were speed related. They conducted several different types of analyses and found the SV run-off-road crashes were more likely to be speed related than multivehicle crashes. Crashes on curves were more likely to be speed related than tangent section and nighttime crashes. In addition, FARS data indicated that 54 percent of speed-related rollover/ overturn, jackknife, or fixed object crashes were on curves (Council et al. 2005).

Turner and Tate (2009) collected data for 488 curves on sections of State highways in New Zealand and found that speed was a contributing factor in 35 percent of fatal and 28 percent of serious crashes on rural roads in New Zealand (in 2003).

FHWA estimates that approximately 56 percent of run-off-road fatal crashes on curves are speed related. The vehicle speed reduction from the tangent section required for traversing a curve has an impact on the frequency and severity of crashes in curves. Abrupt changes in operating speed resulting from changes in horizontal alignment are suggested to be a major cause of crashes on rural two-lane roadways (Luediger et al. 1988).

Anderson and Krammes (2000) developed a model comparing mean speed reduction and mean crash rate for 1,126 horizontal curves on rural two-lane roadways. They report that the relationship between mean crash rate and required speed reduction to negotiate the curve is roughly linear. This finding is also supported by Fink and Krammes (1995), who indicate that curves requiring no speed reduction did not have significantly different mean crash rates than their preceding roadway tangents.

Thompson and Perkins (1983) evaluated crash data for 3 years at 25 rural, isolated curves. They developed models using regression analysis and found that one of the strongest predictors was speed differential between posted and advisory speed.

Driver errors on horizontal curves are often due to the inappropriate selection of speed and the inability to maintain lane position. Drivers' speed selection at curves depends on both explicit attentional cues and implicit perceptual cues (Charlton 2007). A driver's speed prior to entering a curve has a significant effect on their ability to negotiate the curve successfully (Preston and Schoenecker 1999). Inappropriate speed selection and lane positioning can be a result of a driver failing to notice an upcoming curve or misperceiving the roadway curvature.

Driver workload plays an important role in driver speed maintenance. Distracting tasks, such as radio-tuning or cellular telephone conversations, can draw a driver's attention away from speed monitoring, detection of headway changes, lane keeping, and detection of potential hazards (Charlton 2007). Other factors include sight distance issues, fatigue, or complexity of the driving situation (Charlton and DePont 2007, Charlton 2007).

Preston and Schoenecker (1999) evaluated vehicle paths through a curve on a two-lane rural roadway as part of an evaluation of a dynamic curve message sign. The roadway had a posted speed limit of 55 mph and an annual average daily traffic (AADT) of 3,250 vehicles per day (vpd). The researchers collected data over a 4-day period and randomly selected and evaluated 589 vehicles. A total of 340 of the vehicles (58 percent) were traveling over 55 mph, and the rest were traveling at or below the speed limit. The authors evaluated whether each vehicle successfully negotiated the curve. Vehicles that crossed a left or right lane line on one or more occasions were defined as "not successfully navigating the curve."

A logistic regression model was developed to determine the relationship between initial speed and the probability of a vehicle unsuccessfully navigating the curve. Researchers found there was a 20-percent better chance for vehicles that were traveling at or below the speed limit to navigate the curve successfully than for vehicles that were traveling over the speed limit, with the difference being statistically significant at 99 percent. They found that 45 percent of vehicles traveling at or above 65 mph were unable to negotiate the curve compared with 30 percent for vehicles that were traveling under 65 mph, with the difference being statistically significant at the 90-percent confidence interval (CI).

Turner and Tate (2009) evaluated driver behavior on six 20-km rural road sections with curves. Twelve male drivers, 17 to 24 years old, drove each section in a test vehicle with data logging equipment. The researchers found that the speed at which drivers chose to negotiate a curve was more closely related to the radius of the curve than the design speed. In general, radius did not begin to affect negotiation speed until curve radius was less than 300 m. They found that drivers did not lower their speeds from 100 km/h until the curve radius fell below 200 m to 300 m.

Hassan and Easa (2003) found that driver misperception of curvature was greatest when vertical curvature was combined with horizontal curvature. This was particularly a problem when a crest vertical curve was superimposed on a severe horizontal curve, or when a sag vertical curve was combined with a horizontal curve, causing the horizontal curve to appear less severe and resulting in drivers underestimating the curve.

Charlton (2007) conducted a simulator study and evaluated driver speed adjustments on several types of curves with several types of signage. Charlton found that, in general, drivers approached and entered curves at higher speeds when engaged in cellular telephone tasks than in non-distraction scenarios.

Effectiveness of DSFS Systems

DSFS systems have been used in only a few cases to reduce speeds and warn drivers of upcoming curves. They have been used more extensively for a number of other related applications. A summary of information about application of DSFS systems on curves and in related situations is provided below.

Bertini et al. (2006) studied the effectiveness of a DSFS system on Interstate 5 near Myrtle Creek, OR. The system consisted of two displays that provided different messages to drivers based on the speed detected, as shown in table 23.

Table 23. Advisory messages for Interstate 5 dynamic speed-activated feedback sign system.

The curve has an advisory speed of 45 mph with an AADT of 16,750 vpd. Before the DSFS system was in place, there was what they termed "dual overhead horizontal alignment/advisory speed combination sign assemblies with four flashing beacons." The DSFS system was put in place alongside one of the existing signs in both the northbound (NB) and southbound (SB) directions. Each system consisted of the actual dynamic message sign, a radar unit, a controller unit, and computer software. Figure 27 through figure 30 show the system.

Source: Oregon Department of Transportation. See Bertini et al. 2006.
Figure 27. Photo. Interstate 5 DSFS systems in Oregon.

Source: Oregon Department of Transportation. See Bertini et al. 2006.
Figure 28. Photo. Interstate 5 DSFS systems in Oregon.

Source: Oregon Department of Transportation. See Bertini et al. 2006.
Figure 29. Photo. Interstate 5 DSFS systems in Oregon.

Source: Oregon Department of Transportation. See Bertini et al. 2006.
Figure 30. Photo. Interstate 5 DSFS systems in Oregon.

Researchers collected speed data using a laser gun. Results indicated that, after installation of the DSFS system, passenger vehicle speeds were reduced by 2.6 mph and commercial truck speeds were reduced by 1.9 mph, with the results being statistically significant at the 95-percent confidence level. The distribution of speeds shifted to the left after installation of the signs, and the differences were found to be statistically significant based on a 95-percent confidence level using the chi-square test.

Results of a driver survey indicated that 95 percent of drivers surveyed noticed the DSFS system, and 76 percent said they slowed down because of the system.

Another type of DSFS system, a vehicle-activated curve warning sign, was tested on curves in the United Kingdom (Winnett and Wheeler 2002). Three curve warning signs were placed on two-lane roads in Norfolk, Wiltshire, and West Sussex. The signs, shown in figure 31, were placed 50 to 100 m before the apex of a curve.

©TRL (Transport Research Laboratory) 2002
Figure 31. Photo. DSFS in Norfolk, UK.

The signs were blank when the driver was under a specified speed threshold and displayed the curve sign when a driver exceeded the threshold. The speed threshold was set at the 50th percentile speed for the sign location because the researchers wanted to target the upper half of driver speeds. Once activated, the bend warning display was shown for 4 s. The researchers had calculated this time as sufficient for drivers to register and understand the message based on previous research.

Speed data were collected for a minimum of 7 days before the signs were installed, and again 1 month and 1 year after installation. Data were collected at the 1 year after point to determine whether habituation occurs (i.e., drivers become immune to treatments and stop responding). Data were collected using pneumatic tubes at two sites and a radar gun at the third site. Mean speeds were reduced by 2.1 mph at West Sussex, 3.0 mph at Wiltshire, and 6.9 mph at Norfolk.

Crash data were available for two sites, and the researchers found that crashes decreased 54 percent at the Norfolk bend site and 100 percent at the Wiltshire Bend site. A public survey found that drivers approved of the signs.

The City of Bellevue, WA, installed and evaluated 31 DSFS systems, including two used as curve advisory warnings (figure 32). Both were on urban arterials with 35 mph speed limits and 25 mph advisory speeds. Speeds were collected before and between 18 months and 2 years after installation of the signs. One sign showed a 3.3 mph reduction in 85th percentile speed, and the other showed a 3.5 mph reduction.

©City of Bellevue Transportation Department, 2009
Figure 32. Photo. DSFS in Bellevue, WA.

Preston and Schoenecker (1999) also evaluated the safety effect of a DSFS system on County Highway 54 in Minnesota, which is a two-lane rural roadway with a speed limit of 55 mph and an AADT of 3,250 vpd. The curve has an advisory speed of 40 mph. The DSFS system had a changeable message sign and radar unit. A field test was conducted over a 4-day period with a unit that consisted of a closed circuit television camera, a video cassette recorder, and a personal computer. A portable trailer housed the entire system.

The sign showed the following display:

• CURVE AHEAD (from 6 to 10 a.m., 11 a.m. to 2 p.m., and 4 to 7 p.m.).
• No message during other times of the day unless activated.

During all times of the day, when the radar unit detected a vehicle traveling 53 mph or more, the camera activated and recorded the vehicle for 18 s. Using a random number generator, the computer either continued displaying the message CURVE AHEAD or no message, depending on time of day, or displayed the message CURVE AHEAD-REDUCE SPEED.

The team randomly selected 589 of the vehicles captured during data collection and evaluated whether each vehicle successfully negotiated the curve. Successful negotiation was defined as a vehicle remaining within the lane lines as it traversed the curve. Vehicles that crossed a left or right lane line on one or more occasions were defined as "not successfully navigating the curve."

The team found that approximately 35 percent of the drivers who received the message were unable to negotiate the curve successfully. Vehicles that received the CURVE AHEAD sign were more likely to negotiate the curve successfully, but the difference was not statistically significant. Only 26 percent of vehicles that received the CURVE AHEAD-REDUCE SPEED sign were unable to negotiate the curve successfully, and the difference was statistically significant at the 90-percent level of confidence.

Mattox et al. (2006) looked at the effectiveness of a DSFS system on secondary highways in South Carolina. This system consisted of a radar device and a 4 ft by 4 ft yellow sign with 6-inch lettering reading YOU ARE SPEEDING IF FLASHING. In addition, there were two 1 ft by 1 ft orange flags and a type B flashing beacon light. Teams collected data in a before-and-after study upstream of the sign, at the sign, and then downstream of the sign. Results showed a significant reduction in speed at the sign and downstream of the sign. Overall mean speed and 85th percentile speeds were reduced by approximately 3 mph.

A report by the California Department of Transportation (Caltrans) (2010) provided a summary of the effectiveness of safety treatments in one California district. A changeable message sign was installed at five locations along Interstate 5 to reduce truck collisions. Caltrans reported that truck crashes decreased from 71 to 91 percent at four of the sites, while truck crashes increased by 140 percent at the fifth site.

A study by the 3M Company evaluated driver speed back signs in the United Kingdom (updated 2006). Signs were tested at various locations in Doncaster, including semi-rural roadways. The signs displayed the approaching drivers' speed. The sites had speed limits of 40 mph, and reductions up to 7 mph in 85th percentile speeds were noted.

Tribbett et al. (2000) evaluated dynamic curve warning systems for advance notification of alignment changes and speed advisories at five sites in the Sacramento River Canyon on Interstate 5. The roadway has high traffic volumes (7,650 to 9,300 vpd), mountainous terrain, and a number of heavy vehicle crashes. The signs were a 10 ft by 7 ft full matrix light-emitting diode (LED) panel that could be programmed to display a variety of messages. Messages used by the researchers included curve warning (shown in figure 33) and driver speed feedback.

©Patrick McGowen. See Tribbett et al.
Figure 33. Photo. Speed warning sign in the Sacramento River Canyon.

The researchers collected speed data using stopwatches. Data were collected before installation of the signs and at several times after the signs were installed. However, the researchers did not indicate when these after periods were. Speed results at the point of curvature (PC) include the following:

• Site 1: Statistically significant decreases in mean truck speeds from 2.4 to 5.4 mph and decreases in mean passenger car speeds from 3.0 to 4.5 mph.
• Site 2: No statistically significant changes in truck or passenger car speeds for any time periods.
• Site 3: Statistically significant decreases in mean truck speeds from 1.9 to 3.7 mph and increases in passenger cars from 5.2 to 7.8 mph.
• Site 4: No statistically significant change in mean truck speed and a 1.4 mph decrease for passenger cars for one time period that was statistically significant.
• Site 5: Statistically significant change in mean truck speed of 4.5 mph for one time period and decrease in mean passenger car speeds from 2 to 3 mph.

The researchers also compared 5 years of crash data before installation of the signs and 6 months after. However, owing to the very short after period, the results were determined to be unreliable.