Real World Crash Evaluation of Vehicle Stability Control (VSC) Technology

Abstract

This study quantifies the effect of Vehicle Stability Control (VSC) in reducing crash involvement rates for a subset of vehicles in the US fleet. Crash rates for a variety of impact types before and after VSC technology was implemented are compared. Police-reported crashes from six available US state files from 1998–2002 were analyzed including 13,987 crash-involved study vehicles not equipped with the technology and 5,671 crashes of vehicles equipped with VSC as a standard feature. Overall, an 11.2% (95% CI: 2.4%, 21.1%) reduction in multi-vehicle frontal crash involvement was identified for VSC-equipped vehicles. A 52.6% (95% CI: 42.5%, 62.7%) reduction in single-vehicle crash rates was found.

In recent years, numerous technological advances have improved the active safety of vehicles. These technologies—anti-lock brakes, traction control, active yaw-control, and active-roll control to name a few—are being developed to assist drivers with crash avoidance. Such active safety technologies may reduce the severity of a crash or even eliminate the crash from occurring.

The primary function of VSC is to assist the driver in maintaining control of the vehicle during sudden maneuvers or adverse weather conditions. VSC can be classified as an active yaw-control technology, which also uses the functions of anti-lock brakes and traction control. With VSC, a deceleration force and an appropriate inward moment are produced to help prevent front-wheel-skid and improve course-tracking performance. Outward moments also may be applied to avoid rear-wheel-skid and maintain vehicle posture.

VSC technology evaluates an occupant’s steering input compared to the true attitude of the vehicle. If differences are detected, the stability control system will utilize the vehicle braking system and engine power to compensate for these differences. In an oversteer situation, control of the vehicle’s rear end is lost. In this case, the VSC system would mainly apply the outside, front brake to redirect the vehicle to its intended course. In an understeer scenario, the front end of the car tends to slide out. To correct for this, the VSC system will mainly apply the inside, rear brake to redirect the vehicle to its intended course.

Similar systems have been introduced by other vehicle manufacturers under names such as Electronic Stability Control (ESC), Vehicle Dynamic Control (VDC), or AdvanceTrac.

Starting with the 1998 model year, certain vehicles were equipped with VSC as standard equipment. Table 1 lists these vehicle models and the year that the VSC was first implemented in each. Other safety technologies and core vehicle structural attributes were generally similar before and after the date of implementation of the VSC systems. As such, the performance changes in these vehicles under real-world driving conditions should be measurable when comparing crash rates before and after the implementation of VSC technology.

Table 1.

Toyota and Lexus Vehicle Models and Model Years with and without VSC as Standard Equipment

Make and Model	Final Year w/o VSC	VSC Standard
Passenger Cars
Lexus LS430/400	1997	1998
Lexus GS430/400/300	1997	1998
SUVs
Toyota Land Cruiser	1999	2000
Lexus LX470	1999	2000
Toyota 4Runner	2000	2001
Lexus RX300	2000	2001

Several previous studies directly relate to this work. In 1995, Evans (1998) conducted a statistical review of state accident data to assess the effectiveness of anti-lock braking systems. He proposed methods for exposure control based on circumstances where the technology should be most effective versus crashes where anti-lock brakes (ABS) is not. For these analyses, dry weather crashes were used to control for exposure whereas counts of wet weather frontal crashes were assessed for vehicles with and without the technology.

Like the analysis of VSC technology presented here, the Evans study of anti-lock braking included vehicles that were not equipped with the technology in one year, followed by an introduction as standard equipment in the next model year. Methods to correct for the influence of vehicle age on crash occurrence and other confounding factors were applied during the Evans study.

Regarding the need for active control systems, several researchers have reported on the pre-impact characteristics of crashes. Based on German insurance data, Langwieder (1999) reported that 25–30% of crashes involved some type of pre-impact skidding. In 67% of those cases, he noted that this skidding occurred over a 40–70 meter range (131–230 ft.). Sferco et al. (2001), using the European Accident Causation Survey, determined that yaw-stability control systems would have influence in 67% of fatal crashes and 42% of injury accidents where “loss of vehicle control” was classified as the cause of the crash. For all types of crashes, this corresponded to 34% of fatal crashes and 18% of injury crashes.

Tingvall et al. (2003) studied the performance of ESP-equipped vehicles in Sweden from 2000–2002, using analysis techniques similar to Evans’ study on anti-lock braking systems. Rear-end impacts in dry weather conditions were selected as the crash scenario least sensitive to the ESP technology. This provided a normalization criterion for crash exposure and calculation of ESP effectiveness. The study did not account for the increased exposure of older-model-year vehicles. The overall effectiveness of ESP was calculated to be 22.1 ± 21%. On wet roads, the effectiveness was 31.5 ± 23.4%, and in ice and snow, it was 38.2 ± 26.1%.

In 2004, Dang (2004) and the National Highway Traffic Safety Administration (NHTSA) released an evaluation note indicating that a 35% (95% CI: 29%, 41%) reduction in single-vehicle crashes for passenger cars was found for ESC-equipped vehicles. A 67% (95% CI: 60%, 74%) reduction for SUVs was shown. These reductions were established using US State Crash Data files. For fatal crashes using FARS data, NHTSA reported a 30% reduction (95% CI: 10%, 50%) in passenger car single-vehicle crashes and a 63% (95% CI: 44%, 81%) fatality reduction for SUVs.

A 2004 study by Farmer (2004) and the Insurance Institute for Highway Safety (IIHS) identified significant reductions in single vehicle and fatal crashes for vehicles equipped with stability control technology. Using US state crash data, a 7% (95% CI: 3%, 10%) reduction in overall crash involvement for stability-control-equipped vehicles was reported. A 41% (95% CI: 33%, 48%) reduction in single-vehicle crash risk was observed, and a 34% (95% CI: 21%, 45%) overall fatal crash risk reduction was found. The method used vehicle registration counts as a control and accounted for vehicle age to evaluate the effectiveness of stability control systems for a series of vehicle makes and models.

Masami Aga and Akio Okada (2003) used Japanese field data from the Institute for Traffic Accident Research and Data Analysis (ITARDA) to investigate the performance of VSC for three Toyota vehicles. Based on the data resources used, the study concluded that VSC was most effective in reducing single-vehicle crashes (35% reduction) and vehicle-to-vehicle frontal impacts (30% reduction). It also indicated that more severe crashes, based on vehicle damage extent, would experience an even greater reduction. This study was based on 3 common passenger vehicle platforms in Japan including 980,000 vehicle years without VSC and 390,000 vehicle years with VSC.

The analysis presented here expands on the work of Aga and Okada to evaluate Toyota vehicles in a larger sample of crashes occurring in the United States. The analysis described below also benefits from a larger number of vehicle platforms and model years in service for inclusion in the study. The goal of this study is to identify reductions in certain crash types for VSC-equipped vehicles using high-volume crash data.

METHODS

This study identifies the rate of crash involvement for the subset of vehicles shown in Table 1. US state crash data were used to evaluate crash involvement of the study vehicles by crash type. Adjustments were made to account for influential factors including vehicle exposure to crash involvement as well as the influence of vehicle age on the likelihood of involvement for certain crash types. Final crash odds are compared for platforms before and after the addition of VSC technology as a standard feature.

DATA SOURCES

NHTSA’s State Crash Data files and the Federal Highway Administration’s (FHWA’s) State Highway Safety Information System (HSIS) were used for this study. The state data files are a census of crashes whose severity exceeds that required to file a police report. State files are compiled by each state DOT and adapted for use by the NHTSA and the FHWA.

State crash files offer the largest census of crashes occurring within a given region of the United States; however, data elements and their definitions vary considerably between state files. Table 2 identifies states, an average annual vehicle involvement count, and available data years that were used for this analysis. These states were selected due to the availability of vehicle make, model, and model year information. For each state listed in Table 2, with the exception of Texas, vehicle identification numbers (VIN) were decoded to identify appropriate vehicles for the study. Texas provides unique vehicle codes and model year information within its crash files. These codes were used to identify the crash-involved study vehicles of interest for the Texas data.

Table 2.

US State Crash File Availability and Annual Vehicle Crash Records Collected

State	Available Data Years	Vehicle Crash Records (annual average)
Florida	1998–2001	496,944
Illinois	2000–2003	746,995
Maryland	1998–2002	186,141
Missouri	1998–2001	356,868
Texas	1998–2001	587,400
Utah	1998–2002	99,339

During the analysis of state-collected crash data files, several features prevent simple aggregation of data files across states. First, the variable sets collected are not common across all states. As an example, the definition of crash direction is classified according to clock direction for Kansas, whereas Florida, Illinois, Maryland, Missouri, and Utah categorize 18 potential damage regions for each vehicle. New Mexico does not code an indicator of crash direction. Texas codes a 3-digit vehicle damage type that contains indicators of location of damage and degree of damage for each vehicle according to the Vehicle Damage Scale for Crash Investigators by the National Safety Council.

Although each state’s presentation of direction can be classified into general vehicle body regions (i.e., front, left side, right side, and rear), slight variations in the definitions of each could influence crash proportions when compared across states.

A second influential characteristic of the state-collected data is the varied reporting criteria for Police Accident Reports (PARs). For example, Florida PARs include the following: a crash causing at least one fatality, a crash causing a personal injury, a crash involving damage to an attended vehicle, a crash causing damage to property where a vehicle has left the scene, a crash where at least one driver is under the influence of alcoholic beverages or chemical substances, or a crash deemed severe enough to report by the investigating officer. Police-reported crashes in Illinois must have property damage of $500 or more, a fatality, or a personal injury associated with the crash. Maryland crashes reported by police must have at least one vehicle involved in the crash towed away from the scene, a personal injury, or a fatality associated with the crash. In Texas, a police report is required for a crash resulting in at least $250 of property damage, a personal injury, or a fatality associated with the crash.

Because rear-impact crashes do not often result in occupant injury or very high levels of property damage, significant variations in crash population could result due to only slight variations in state crash reporting criteria.

For this analysis, crash rates have been calculated separately by state to avoid possible inconsistencies due to varied sampling criteria. Further, clustering of crashes within geographic regions with common weather and roadway types may influence crash-involvement rates as well. For these reasons, the state samples have been analyzed independently; however, data files have been aggregated across available years within each state for this analysis. Care has been taken to ensure that variable definitions and case collection criteria remain constant from year to year for each state over the period evaluated.

Table 3 shows registration counts for study vehicles by study state. Registration counts were derived from the RL Polk National Vehicle Population Profile (NVPP) dataset by state and partitioned based on the presence of VSC as shown.

Table 3.

Vehicle Registration and Crash Involvement Counts for Study Vehicles

	Study Vehicle Registration Count (Calender Year 2001)		Study Vehicle Crash Count* (1998–2002)
State	Pre VSC	Post VSC	Pre VSC	Post VSC
Florida	52,302	55,752	3,356	1,181
Illinois	22,491	23,991	3,405	2,115
Maryland	12,864	12,048	1,320	479
Missouri	5,720	5,018	660	199
Texas	51,607	46,206	4,081	1,288
Utah	3,496	2,618	658	183
Total	155,383	150,828	13,987	5,671

^*crash counts may include different data years per state (see Table 2)

Study vehicle crash counts listed in Table 3 were compiled for multiple years for each state used. Some variation exists in available data years for each state so that high-volume crash states, including Florida and Texas, may be somewhat underrepresented due to missing datasets at the time of this analysis.

CRASH TYPES AFFECTED BY VEHICLE STABILITY CONTROL

VSC systems act to correct conditions involving a loss of driver control. These conditions may be brought on by excessive steering inputs or adverse weather conditions. To monitor historical crash data to recognize the influence of VSC systems, crash types that are associated with loss of control have been isolated.

As reported by the NHTSA Fatality Analysis Reporting System (FARS), loss-of-control fatal crashes are most often associated with single-vehicle crashes, multivehicle frontal crashes. Side-impact and rear-impact crashes involve a loss of control for the struck vehicle infrequently, but loss of control by the striking vehicle occurs often. This study will consider the influence of VSC on multivehicle frontal crashes and single-vehicle crashes in all directions.

CALCULATION OF CRASH ODDS

Crash odds were calculated using the “induced exposure” method as applied by Tingvall et al. (2003). Using this method, crash counts where VSC is assumed to be influential are divided by a population of crashes where the technology is assumed to have no effect. The unaffected group acts as a population control.

As described above, VSC technology helps drivers to maintain their intended course during difficult driving situations. For this reason, VSC is assumed to be influential for single-vehicle crashes andmultivehicle frontal crashes where loss of control is often a key factor leading to a crash. Frontal crashes are defined as any crash where frontal damage occurred in the study vehicle or the initial impact point was in the vehicle front regardless of damage region in the struck vehicle (i.e. frontal crashes were not limited to head-on collisions). _Counts of these crash types that may be positively affected by VSC technology are the numerator of the crash odds calculation shown in Equation 1.

In the denominator of Equation 1, crashes that should not be affected by VSC technology are used as a control group. This crash count will indicate variations in exposure of a particular make, model, and model year to potential crash events. Rear-impact crash counts will serve as this proxy for the number of vehicles in service and the frequency that these vehicles are driven (i.e., exposed to potential crashes).

$${CrashOdds}_{VSC}=\frac{{NF}_{(VSC)}}{{NR}_{(VSC)}}$$ (1)

Where:

NF_{(vsc )} = Count of Multivehicle Frontal or Single-Vehicle Crashes for post VSC vehicles

NR_(vsc) = Count of Rear Crashes for post VSC-equipped vehicles as struck vehicle

For rear-impact crashes (where the VSC-equipped vehicle is the struck vehicle), the stability of the struck vehicle is assumed to have little to no influence on the likelihood of being impacted. The frequency of rear-impact involvement gives an indication of the relative population of vehicles and conditions when the vehicle is driven without being influenced by the technology in question.

For comparison with Equation 1, the odds ratio for crash involvement may be calculated for vehicles without the technology in the same way (see Equation 2).

$${CrashOdds}_{preVSC}=\frac{{NF}_{(preVSC)}}{{NR}_{(preVSC)}}$$ (2)

Where:

NF_(preVSC) = Count of Multivehicle Frontal or Single-Vehicle Crashes for study vehicles pre VSC

NR_(preVSC) = Count of Rear Crashes for study vehicles pre VSC as struck vehicle

If the ratios calculated using Equations 1 and 2 are significantly different than one another, where other conditions besides the implemented technology remain the same, then technology can be considered influential. If VSC is effective in aiding drivers to keep in control of their vehicles and avoiding a particular crash scenario, the overall magnitude of CrashOdds_VSC should be less than CrashOdds_preVSC. The odds ratio that results when CrashOdds_VSC is divided by CrashOdds_preVSC indicates the overall effect of the technology in reducing a given crash type. If the technology is helpful in reducing crash occurrence, the odds ratio calculated using Equation 3 will be less than 1.

$${OddsRatio}_{VSC:preVSC}=\frac{{CrashOdds}_{(VSC)}}{{CrashOdds}_{(preVSC)}}$$ (3)

INFLUENTIAL FACTORS

As noted by Evans during an analysis of ABS systems, vehicle age (or model year effect) is known to significantly affect crash involvement. In the 1998 study of anti-lock brake effectiveness, Evans reports that vehicles only one year older showed nearly 9% higher crash involvement than their younger counterparts. A more recent study by Poindexter (2003) indicates that these effects are much less pronounced based on NASS/GES data; however, an increase in crash risk does exist.

For this study, it is important to account for the influence of vehicle age on the ratio of the study conditions relative to rear-impact crashes as described by Equations 1 and 2. If the age effect of vehicles influences rates of crash involvement in the numerators or denominators of these equations, then the effect must be accounted for to accurately assess the contribution of VSC.

Figure 2 shows the relative percentage of frontal versus side versus rear crashes within the six state data files in use for this study. These trends indicate an increase in the proportion of frontal crashes for the population of reported crashes versus vehicle age. Conversely, the proportion of rear impacts reported appears to decrease with vehicle age. Caution must be used in interpreting the trends shown in Figure 2. It is possible that vehicles are involved in frontal crashes more frequently as they age; however, this trend also may result due to decreasing numbers of reported rear impacts as vehicles age. This may be caused by vehicle attrition rate or a reduction in rear-impact crash cases reported to police where a PAR was filed. The effect is accounted for in the analysis presented here.

Figure 2.

Vehicle Crash Ratios by Crash Mode Based on Vehicle Age.

To adjust for this effect of vehicle age on the ratio of frontal versus rear-crash case counts, a second odds ratio may be calculated. The ratio shown in Equation 4 identifies the odds of crash involvement relative to rear impacts for a vehicle that is N years old.

$${CrashOdds}_{Nyrs}=\frac{{NF}_{(Nyrs)}}{{NR}_{(Nyrs)}}$$ (4)

Where:

NF_(Nyrs) = Count of Multivehicle Frontal or Single-Vehicle Crashes for vehicles N years old

NR_(Nyrs) = Count of Rear Crashes for vehicles N years old

By dividing the odds of crash involvement for a vehicle that is one year old by the ratio shown in Equation 4, the odds ratio of crash occurrence for a vehicle N years old compared with a vehicle only one year old is found (see Equation 5).

$$OddsRati{o}_{1yr:Nyrs}=\frac{{CrashOdds}_{(1yr)}}{{CrashOdds}_{(Nyrs)}}$$ (5)

Finally, to account for the influence of vehicle age on the odds of crash occurrence with and without VSC technology implemented, crash odds are adjusted for each age group beyond one-year-old vehicles. Each odds ratio calculated per Equation 3 is adjusted as shown in Equation 6. This calculation was performed for study vehicles by vehicle age per state reviewed. This age adjusted odds ratio is the measure presented in the results section below.

$$OddsRatio{(Age)}_{VSC:preVSC}={}^{\frac{{CrashOdds}_{(VSC)}}{{CrashOdds}_{(preVSC)}}}/_{\frac{{CrashOdds}_{(1yr)}}{{CrashOdds}_{(Nyrs)}}}$$ (6)

STANDARD ERROR ESTIMATES

The standard error of the odds ratios are calculated as shown in Equation 7. This is the standard error of the log odds ratio.

$${\sigma }_{\text{ln}(R)}=\sqrt{\sum _{i=1}^8\frac{1}{n_i}}$$ (7)

Where n_i = age adjusted crash count per VSC and crash involvement category from Equations 1 and 2. Based on equation 7, it can be seen that small counts of either the case populations (multivehicle frontal or single vehicle) or control crash populations (rear impacts) will significantly increase the standard error of the odds ratios presented. All confidence limits presented below are calculated for the 95% range.

RESULTS

In calculating odds for each crash type discussed above, data were aggregated across multiple years for each state during calculations. Each state’s crash data was partitioned by crash direction for VSC-equipped and non-VSC-equipped vehicles. Also, single-vehicle crash counts were tabulated for calculations below.

CRASH ODDS

Table 4 shows the odds of crash involvement for multivehicle frontal crash impacts. Each value reported represents the crash odds where the percent reduction in crash occurrence with VSC technology can be determined using the following equation.

Table 4.

Multivehicle Frontal Crash Odds Relative to Rear Impact Crashes

State	Odds Ratio Front:Rear	SE LN-OR	Lower 95% CI	Upper 95% CI
Florida	0.975	0.078	0.837	1.135
Illinois	0.819	0.059	0.729	0.921
Maryland	0.729	0.128	0.567	0.936
Missouri	1.048	0.183	0.731	1.501
Texas	0.885	0.079	0.758	0.973
Utah	0.902	0.191	0.621	1.311
Total	0.882	0.037	0.820	0.947

$$\%reduction=100*(1-OddsRati{o}_{vsc:preVSC})$$ (8)

As an example, the odds ratio for multivehicle frontal crash involvement relative to rear impacts with VSC is .819 for Illinois as shown in Table 4. This indicates an overall 18.1% reduction in crash rate per Equation 8 for this state. The 95% confidence limits indicate that, based on available information, one can be 95% certain that the true rate reduction falls between the lower bound of 72.9% and the upper bound of 92.1%. If this confidence interval spans 1.0, no significant effect can be observed in the data regardless of the reported odds ratio. Findings from Florida, Missouri, and Utah were non-significant based on available data for multivehicle frontal crashes relative to rear impacts.

As indicated previously, odds ratios based on data aggregated across multiple states are not possible due to differences in inclusion criteria and variable definitions. To summarize the odds ratios reported across the multiple state files queried, a total odds ratio is presented based on a weighted average of each state’s results. The contribution of each state to this weighted average is proportional to the number of study vehicles registered in each state. Confidence intervals were calculated by considering each estimate as a simple random sample. In doing so, the standard deviation of the individual state estimates can be computed. From these standard deviations, the 95% confidence limit estimates were derived. This method, as proposed by Kahane (1989), takes into account sampling error within states as well as state to state differences in crash definitions. It results in a very conservative estimate of the confidence limits. The total reduction in crash rates for multivehicle frontal crashes is 11.8% (95% CI: 2.4%, 21.1%) as shown in Table 4.

Table 5 indicates an average single-vehicle crash rate reduction of 52.6% (95% CI: 42.5%, 62.7). This category includes crashes where no other vehicles were involved during the impact. This category of crashes was found to include the largest population of injured occupants across all state data files. All odds ratios reported by state show statistically significant reductions in single-vehicle crash occurrence.

Table 5.

Single-Vehicle Crash Odds Relative to Rear Impact Crashes

State	Odds Ratio Single:Rear	SE LN-OR	Lower 95% CI	Upper 95% CI
Florida	0.554	0.145	0.417	0.736
Illinois	0.571	0.119	0.452	0.720
Maryland	0.251	0.217	0.164	0.384
Missouri	0.473	0.313	0.256	0.873
Texas	0.391	0.126	0.306	0.501
Utah	0.521	0.275	0.304	0.892
Total	0.474	0.065	0.417	0.538

DISCUSSION

The method used during this study to evaluate crash-reduction rates due to VSC considers vehicle exposure and the influence of vehicle age on the likelihood of crash occurrence. The method considers all crash occurrences relative to rear-impact crashes. This approach requires consistent reporting of rear impacts for each group of crashes analyzed. For this reason, it is not possible to report aggregate crash reductions across multiple state files. This point is important during the analysis of state data files for VSC as well as other topics of research.

A second considerable factor that was accounted for in this analysis was the effect of vehicle age on reported frontal versus rear impacts. The age adjustment presented here should account for this effect. It should be noted that 84% of the vehicles analyzed during this study were between 1 and 3 years old. For this reason, the effect of age on reported crashes was not extremely large in this population; therefore, an adjustment was made for this effect. Some Lexus sedans, as early as model year 1996 (i.e., up to 5 years old), were present in the pre-VSC-equipped crash populations.

The positive effects of VSC technology in reducing crash occurrences for frontal multivehicle crashes and single-vehicle crashes are significant and encouraging. However, the true benefit of this effect must be considered in the context of property damage reduction, injury reduction, and fatality reduction.

As previously presented by Aga and Okada (2003), VSC technology will increase the limits where a driver will remain in control of his vehicle. However, the technology will not prevent all loss-of-control crashes across all severities from occurring. Accordingly, a 52% reduction in single-vehicle crashes may not directly translate to the same reduction in single-vehicle fatal crashes or single-vehicle injury crashes. Although a significant number of injury and fatal crashes will be avoided, a portion of these high-severity loss-of-control events could be beyond the limits of dynamics correctable by VSC systems. Further analysis of the nature of crashes that are reduced by VSC technology is necessary and will be a topic of future analyses.

The US State Crash Data provides a resource to evaluate high volume police reported crash information. These datasets rely on information observed by police officers on-scene and on verbal accounts given by crash involved occupants. In some cases, crash reports are completed hours after a crash scene has been cleared. The completeness and accuracy of the data elements coded could be questionable for these reasons. Further, police are not required to perform a detailed crash investigation or reconstruction which may also lead to missing information or incorrect assumptions made by officers. It is understood and reasonable that the primary objective of police is to ensure the safety of the public and to restore roadways to normal operation as quickly as possible. Collection of data and reporting may become secondary in some situations.

The results presented here are similar to findings by other researchers who used different analytical techniques. Farmer (2004) indicated a 41% reduction in single-vehicle crashes controlling for exposure using vehicle registration data. NHTSA reported a 35% reduction for passenger cars and a 67% reduction for SUVs. The above analysis indicates a 52% reduction in single-vehicle crash rates for VSC-equipped vehicles using rear-impact crash counts as a control. Differences in the findings may be due, in part, to differences in study vehicle populations. Also, the above studies included different study states where varied weather and driving situations could influence the relative number of times where stability was a factor in the occurrence of crashes. Results from each analysis indicate a quantifiable benefit in reducing crashes with these systems in place.

CONCLUSIONS

This study identified the reduction in crash involvement for vehicles equipped with VSC compared with crash involvement for the same vehicle platforms before the technology was offered as standard equipment. Rear-impact crashes were used as a control group to account for vehicle exposure. This study also accounted for the influence of vehicle age on the likelihood of involvement in certain crash types.

Overall, it was shown that there is an 11.8% (95% CI: 2.4%, 21.1%) reduction in multivehicle frontal crashes for the VSC-equipped vehicles included in the study population. A 52.6% (95% CI: 42.5%, 62.7%) reduction in single-vehicle crash rates was shown for these vehicles.

Single-vehicle or run-off-road crashes account for more than 37% of the traffic fatalities on US roadways. A subset of these 14,000 fatal crashes occurs due to a loss of vehicle control. Findings of this study indicate that a significant portion of fatal and nonfatal single-vehicle crashes may be prevented with widespread implementation of stability control technology. Further analysis is required to fully quantify the ratio of property damage only, injury crashes, and fatal crashes that are reduced with the implementation of VSC.

Figure 1.

Effect of Vehicle Stability Control (VSC) During Oversteer and Understeer Scenarios