A Bayesian assessment of the effect of highway bypasses in Iowa on crashes and crash rate

Introduction

A common contention is that the construction of highway bypasses negatively impacts the economy of local communities by reducing pass-by traffic for businesses. However, as access to specific business' account records is limited, this impact is difficult to quantify. Another common contention is that bypasses contribute to a reduction in overall crashes in the community and in the surrounding areas. Even though a large number of bypasses have been constructed in the State of Iowa over the past several years, their actual impact in terms of traffic safety has not been quantified.

Objectives

This study seeks answers to the following questions: (a) Are bypasses in Iowa associated with a reduction in crash frequencies and crash rates on the bypassed highway? (b) Do bypasses in Iowa introduce a reduction of overall crash frequencies and rates or do they merely shift crashes from the highways through the communities to the bypasses with no significant overall reduction?

Method

We obtained crash information from the Iowa DOT at 19 sites on which a bypass was constructed sometime during the past 23 years. We also obtained the same information at six sites used as comparison sites on which no bypasses were constructed at least until 2005. We them employed a Bayesian approach to estimating the association between the construction of the bypass and crash rates, while also accounting for other factors.

Results

The construction of bypasses in Iowa is associated with a significant increase in traffic safety both on the main road through town and on the combined main road and bypass roadway.     

Impact of infrastructure characteristics on road crashes on two-lane highways

OBJECTIVE: The main purpose of this study is to estimate and quantify the contribution of the infrastructure to highway crashes and to develop an infrastructure coefficient, that represents the overall characteristics of the highway and could be used as an independent variable in a crash-prediction model. METHODS: The infrastructure is defined in this study as the highway and its geometric features, including alignment, road-side elements, sight-distances, presence of guardrails, access-points, roadway consistency, and additional variables that measure the overall quality of the highway alignment and elements. The analysis and developments are conducted for two-lane rural highways. The approach taken is to identify the high crash-rate roads, those with crash rates above 0.25 crashes per million vehicle-km, by Smallest Space Analysis. This type of analysis allows the aggregation of higher crash-rate roads versus lower-crash-rate roads only by their infrastructure coefficients, without consideration of their crash records. RESULTS: Crash rates that are attached by Smallest Space Analysis to the group of roads that had less desirable infrastructure features show a high correlation between the same roads and high crash rates vs. identified better infrastructures and low crash rates. Further analysis shows that low crash-rate infrastructure, as defined in this study for two-lane rural highways, can reduce the crash rate by 44% versus high crash-rate infrastructure, at the 99% confidence level, which is almost a certainty. A model for the prediction of crash rates based on a proposed infrastructure coefficient is calibrated and presented. CONCLUSIONS: It is suggested that this model be used in evaluating alternatives for new highways or in improving the alignment and road features of existing highways.     

Severity models of cross-median and rollover crashes on rural divided highways in Pennsylvania

Introduction

Crossover and rollover crashes in earth-divided, traversable medians on rural divided highways can lead to severe injury outcomes. This study estimated severity models of these two crash types. Vehicle, driver, roadway, and median cross-section design data were factors considered in the models. A unique aspect of the data used to estimate the models were the availability of median cross-slope data, which are not commonly included in roadway inventory data files.

Methods

A binary logit model of cross-median crash severity and a multinomial logit model of rollover crash severity were estimated using five years of data from rural divided highways in Pennsylvania.

Results

The highest probability of a fatal or major injury in cross-median and rollover crashes was found to occur in cases when a driver was not wearing a seatbelt. While flatter cross-slopes and narrower medians were associated with more severe cross-median crash outcomes, steeper cross-slopes and narrower medians significantly increased rollover crash severity outcomes. The presence of horizontal curves was associated with increased probabilities of high-severity outcomes in a median rollover crash.

Impact on Industry

Modeling results in this study confirmed that cross-median and median rollover crash severity outcomes are associated with median cross-section design characteristics. Based on the estimated models, it appears that flatter and narrower medians lead to more severe injury outcomes in cross-median crashes. Steeper median cross-slopes and narrower medians were associated with higher probabilities of more severe outcomes in median rollover crashes. The results presented in this study suggest that there is a trade-off between median cross-section design and cross-median and rollover crashes in earth-divided, traversable medians on rural divided highways. While the severity models can be included in a framework to develop design guidance in relation to this trade-off, models of crash frequency should also be considered.     

Random parameter models for accident prediction on two-lane undivided highways in India

Introduction

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation.

Method

The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models.

Results

The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations.

Conclusions

The paper is concluded with a discussion on modeling results and the limitations of the present study.     

Exploring factors contributing to crash injury severity on rural two-lane highways

ObjectiveCrash injury results from complex interaction among factors related to at-fault driver's behavior, vehicle characteristics, and road conditions. Identifying the significance of these factors which affect crash injury severity is critical for improving traffic safety. A method was developed to explore the relationship based on crash data collected on rural two-lane highways in China.MethodsThere were 673 crash records collected on rural two-lane highways in China. A partial proportional odds model was developed to examine factors influencing crash injury severity owing to its high ability to accommodate the ordered response nature of injury severity. An elasticity analysis was conducted to quantify the marginal effects of each contributing factor.ResultsThe results show that nine explanatory variables, including at-fault driver's age, at-fault driver having a license or not, alcohol usage, speeding, pedestrian involved, type of area, weather condition, pavement type, and collision type, significantly affect injury severity. In addition to alcohol usage and pedestrian involved, others violate the proportional odds assumption. At-fault driver's age of 25–39 years, alcohol usage, speeding, pedestrian involved, pavement type of asphalt, and collision type of angle are found to be increased crash injury severity.Practical ApplicationsThe developed logit model has demonstrated itself efficient in identifying the effect of contributing factors on the crash injury severity.     

Case-control and cross-sectional methods for estimating crash modification factors: comparisons from roadway lighting and lane and shoulder width safety effect studies

Problem

While observational before-after studies are considered the industry standard for developing crash modification factors (CMFs), there are practical limitations that may preclude their use in highway safety analysis. There is a need to explore alternative methods for estimating CMFs.

Method

This paper employs case-control and cross-sectional analyses to estimate CMFs for fixed roadway lighting and the allocation of lane and shoulder widths.

Results

Based on the case-control method, the CMF for intersection lighting is 0.886, while the cross-sectional study indicates a CMF of 0.881. The CMFs developed for lane and shoulder widths are also similar when comparing the two methods.

Conclusions

This paper suggests that case-control and cross-sectional studies produce consistent results if care is taken in the study design and model development.

Impact on industry

Case-control and cross-sectional studies may provide a viable alternative to estimate CMFs when a before-after study is impractical due to data restrictions.     

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

Introduction

The study aims at identifying traffic/highway design/driver-vehicle information significantly related with fatal/severe crashes on urban arterials for different crash types. Since the data used in this study are observational (i.e., collected outside the purview of a designed experiment), an information discovery approach is adopted for this study.

Method

Random Forests, which are ensembles of individual trees grown by CART (Classification and Regression Tree) algorithm, are applied in numerous applications for this purpose. Specifically, conditional inference forests have been implemented. In each tree of the conditional inference forest, splits are based on how good the association is. Chi-square test statistics are used to measure the association. Apart from identifying the variables that improve classification accuracy, the methodology also clearly identifies the variables that are neutral to accuracy, and also those that decrease it.

Results

The methodology is quite insightful in identifying the variables of interest in the database (e.g., alcohol/ drug use and higher posted speed limits contribute to severe crashes). Failure to use safety equipment by all passengers and presence of driver/passenger in the vulnerable age group (more than 55 years or less than 3 years) increased the severity of injuries given a crash had occurred. A new variable, ‘element’ has been used in this study, which assigns crashes to segments, intersections, or access points based on the information from site location, traffic control, and presence of signals.

Impact

The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes.     

Analyzing motorists’ responses to temporary signage in highway work zones

For decades, the importance of highway work zone safety has increased considerably with the continual increase in the number of highway work zones present on highways for repairs and expansion. Rural work zones on two-lane highways are particularly hazardous and cause a significant safety concern due to the disruption of regular traffic flow. In this study, researchers determined motorists’ responses to warning signs in rural, two-lane highway work zones. The researchers divided vehicles into three classes (passenger car, truck, and semitrailer) and compared the mean change in speed of these classes based on three different sign setups: portable changeable message sign (PCMS) OFF, PCMS ON with the message of Slow Down, Drive Safely, and a temporary traffic sign (W20-1, “Road Work Ahead”). Field experiments were conducted on two two-lane work zones with flagger control. Statistical analyses were performed to determine whether there was a significant interaction between motorists’ responses and the sign setups. Data analysis results show that a visible PCMS, either turned on or off, was most effective in reducing truck speeds in rural, two-lane work zones. The temporary traffic sign (W20-1) was more effective in reducing the vehicle speeds of passenger car and semitrailer. Results of this research project will help traffic engineers to better design the two-lane work zone setup and take necessary safety countermeasures to prevent vehicle crashes.     

Fatal crash trends for Australian young drivers 1997-2007: Geographic and socioeconomic differentials

Background

Little has been published on changes in young driver fatality rates over time. This paper examines differences in Australian young driver fatality rates over the last decade, examining important risk factors including place of residence and socioeconomic status (SES).

Methods

Young driver (17-25 years) police-recorded passenger vehicle crashes were extracted from New South Wales State records from 1997-2007. Rurality of residence and SES were classified into three levels based on drivers’ residential postcode: urban, regional, or rural; and high, moderate, or low SES areas. Geographic and SES disparities in trends of fatality rates were examined by the generalized linear model. Chi-square trend test was used to examine the distributions of posted speed limits, drinking driving, fatigue, seatbelt use, vehicle age, night-time driving, and the time from crash to death across rurality and socioeconomic status.

Results

Young driver fatality rate significantly decreased 5% per year (p < 0.05); however, stratified analyses (by rurality and by SES) showed that only the reduction among urban drivers was significant (average 5% decrease per year, p < 0.01). The higher relative risk of fatality for rural versus urban drivers, and for drivers of low versus high SES remained unchanged over the last decade. High posted speed limits, fatigue, drink driving and seatbelt non-use were significantly associated with rural fatalities, whereas high posted speed limit, fatigue, and driving an older vehicle were significantly related to low SES fatality.

Conclusion

The constant geographic and SES disparities in young driver fatality rates highlight safety inequities for those living in rural areas and those of low SES. Better targeted interventions are needed, including attention to behavioral risk factors and vehicle age.     

Age and gender differences in conviction and crash occurrence subsequent to being directed to Iowa's driver improvement program

Introduction

This paper investigates potential gender and age differences in conviction and crash occurrence subsequent to being directed to attend Iowa's Driver Improvement Program (DIP).

Methods

Binary logit models were developed to investigate the factors that influence conviction occurrence after DIP by gender and age. Because of the low crash occurrence subsequent to DIP, association rules were applied to investigate the factors that influence crash occurrence subsequent to DIP, in lieu of econometric models.

Results

There were statistical significant differences by driver gender, age, and conviction history in the likelihood of subsequent convictions. However, this paper found no association between DIP outcome, crash history, and crash occurrence.

Impact on industry

Evaluating the differences in conviction and crash occurrence subsequent to DIP between female and male drivers, and among different age groups can lead to improvements of the effectiveness of DIPs and help to identify low-cost intervention measures, customized based on drivers’ gender and age, for improving driving behaviors.     

Differences in urban and rural accident characteristics and medical service utilization for traffic fatalities in less-motorized societies

Introduction

Fatalities from traffic accidents in less-motorized societies are an important global issue. We aimed to characterize the geographic differences of fatalities in such societies to facilitate the development of targeted interventions.

Method

This study linked police reports, hospital data, and vital registration data from Taiwan with special reference to accident factors in pre-hospital deaths and medical care in hospital deaths.

Results

A higher percentage of pre-hospital deaths were observed following rural as compared to urban traffic accidents. The deaths due to rural accidents can be attributed to lower use of restraints (i.e., helmets or seat belts), lower percentage of motorcyclists, and more highway accidents. A higher percentage of victims in rural accidents were transported to distant medical centers rather than to local hospitals.

Conclusion

Specific interventions, such as intelligent emergency medical systems, campaigns for helmets and seat belt usage, enforcement of helmets and seat belt use, and speed control measures should be targeted to rural areas.

Impact on industry

Cooperation between the vehicle industry and emergency medical providers in rural traffic accident rescue teams may decrease the numbers of deaths in these regions.     

Driving self-restriction in high-risk conditions: how do older drivers compare to others?

Introduction

Many older drivers self-restrict or avoid driving under high-risk conditions. Little is known about the onset of driving self-restrictions or how widespread self-restrictions are among drivers of all ages.

Methods

The Second Injury Control and Risk Survey (ICARIS-2) was a nationwide cross-sectional, list-assisted random-digit-dial telephone survey from 2001 to 2003. National prevalence estimates and weighted percentages of those reporting driving self-restrictions were calculated. Multivariable logistic regression was used to explore associations between specific self-restrictions and age group, adjusting for other personal characteristics.

Results

More than half of all drivers reported at least one driving self-restriction. The most commonly reported restriction was avoidance of driving in bad weather (47.5%), followed by at night (27.9%) and on highways or high-speed roads (19%). A greater percentage of young adult women (18-24 years) reported self-restricting in bad weather compared to women in other age groups, and the percentage of drivers self-restricting at night, in bad weather, and on highways or high-speed roads increased steeply after age 64. We found that women, those in low income groups, and those who had driven low annual mileage were more likely to self-restrict.

Conclusions

In addition to assessing self-restrictions among older drivers, a new finding from our study is that self-restrictions are also quite prevalent among younger age groups. Driving self-restrictions may be better understood as a spectrum across ages in which drivers’ reasons for restriction change.

Impact on industry

Future research on the ability of driving self-restrictions to reduce actual crash risk and prevent injuries is needed.     

A traffic safety evaluation of California's traffic violator school citation dismissal policy

Introduction

This study evaluated California's traffic violator school (TVS) citation dismissal policy.

Method

This study identified and compared two large samples of drivers either completing a TVS (N = 210,015) or convicted of a traffic citation (N = 168,563).

Results

Prior to adjudication, the TVS group had characteristics (e.g., lower prior conviction rate and smaller proportion of males) that were predictive of a lower subsequent crash risk. However, the TVS group exhibited significantly more crashes than did the convicted group in the subsequent one-year period. The difference (4.83%) increased to 10% after adjusting for the more favorable characteristics of the TVS group. The TVS group also had a higher adjusted subsequent crash rate at each prior driver record entry level, reflecting a loss in the general and specific deterrence of the non-conviction masked status of TVS dismissed citations. It was reported that the TVS dismissal policy results in approximately 12,300 additional crashes annually with economic costs of approximately $398,000,000.

Conclusions

The avoidance of licensing actions resulting from the dismissal policy assists in explaining why the driving public is exposed to an increased crash risk. A number of recommendations are offered to reduce the negative traffic safety impact of the TVS citation dismissal policy.     

Implied-consent laws: A review of the literature and examination of current problems and related statutes

Problem

A substantial proportion of drivers arrested for DUI refuse the BAC test, thereby reducing the likelihood that they will be convicted and potentially increasing the number of high-risk multiple offenders contributing to alcohol-related crashes.

Method

This paper reviews the information on the current status of implied-consent laws (which impose a sanction on offenders who refuse the BAC test) in the 50 states and the other relevant traffic safety laws and policies that may influence state refusal rates.

Results

Although there appears to be only a weak relationship between state refusal rates and crash rates, there is strong evidence that BAC test refusals significantly compromise the arrest, prosecution, and sentencing of DUI suspects and the overall enforcement of DUI laws in the United States.

Discussion

Laws and policies that may reduce the number of refusals are discussed.

Impact on industry

Alcohol-related crash injuries are an important cost problem for U.S. industry because of property damage from crashes, crash injuries to employees that raise health costs, or the reduction of time on the job resulting from a highway injury.     

What vehicle features are considered important when buying an automobile? An examination of driver preferences by age and gender

Introduction

Certain vehicle features can help drivers avoid collisions and/or protect occupants in the event of a crash, and therefore, might play an important role when deciding which vehicle to purchase. The objective of this study was to examine the importance attributed to key vehicle features (including safety) that drivers consider when buying a car and its association with age and gender.

Methods

A sample of 2,002 Canadian drivers aged 18 years and older completed a survey that asked them to rank the importance of eight vehicle features if they were to purchase a vehicle (storage, mileage, safety, price, comfort, performance, design, and reliability). ANOVA tests were performed to: (a) determine if there were differences in the level of importance between features and; (b) examine the effect of age and gender on the importance attributed to these features.

Results

Of the features examined, safety and reliability were the most highly rated in terms of importance, whereas design and performance had the lowest rating. Differences in safety and performance across age groups were dependent on gender. This effect was most evident in the youngest and oldest age groups.

Conclusions

Safety and reliability were considered the most important features. Age and gender play a significant role in explaining the importance of certain features.

Impact on Industry

Targeted efforts for translating safety-related information to the youngest and oldest consumers should be emphasized due to their high collision, injury, and fatality rates.     

The Long Beach/Fort Lauderdale relative risk study

Problem

The role of alcohol as a major factor in traffic crash causation has been firmly established. However, controversy remains as to the precise shape of the relative risk function and the BAC at which crash risk begins to increase.

Methods

This study used a case-control design in two locations: Long Beach, California, and Fort Lauderdale, Florida. Data were collected on 2,871 crashes of all severities and a matched control group of drivers selected from the same time, location, and direction of travel as the crash drivers. Of the 14,985 sample drivers, 81.3% of the crash drivers and 97.9% of the controls provided a valid BAC specimen.

Results

When adjusted for covariates and nonparticipation bias, increases in relative risk were observed at BACs of .04-.05, and the elevations in risk became very pronounced when BACs exceeded .10.

Discussion

The results provide strong support for .08 per se laws and for state policies that increase sanctions for BACs in excess of .15.

Impact on Industry

This study provides further precision on the deleterious effects of alcohol on driving and, by implication, on other complex tasks.     

Crashes and near-crashes on horizontal curves along rural two-lane highways: Analysis of naturalistic driving data

IntroductionPrior research has shown the probability of a crash occurring on horizontal curves to be significantly higher than on similar tangent segments, and a disproportionally higher number of curve-related crashes occurred in rural areas. Challenges arise when analyzing the safety of horizontal curves due to imprecision in integrating information as to the temporal and spatial characteristics of each crash with specific curves.MethodsThe second Strategic Highway Research Program(SHRP 2) conducted a large-scale naturalistic driving study (NDS),which provides a unique opportunity to better understand the contributing factors leading to crash or near-crash events. This study utilizes high-resolution behavioral data from the NDS to identify factors associated with 108 safety critical events (i.e., crashes or near-crashes) on rural two-lane curves. A case-control approach is utilized wherein these events are compared to 216 normal, baseline-driving events. The variables examined in this study include driver demographic characteristics, details of the traffic environment and roadway geometry, as well as driver behaviors such as in-vehicle distractions.ResultsLogistic regression models are estimated to discern those factors affecting the likelihood of a driver being crash-involved. These factors include high-risk behaviors, such as speeding and visual distractions, as well as curve design elements and other roadway characteristics such as pavement surface conditions.ConclusionsThis paper successfully integrated driver behavior, vehicle characteristics, and roadway environments into the same model. Logistic regression model was found to be an effective way to investigate crash risks using naturalistic driving data.Practical ApplicationsThis paper revealed a number of contributing factors to crashes on rural two-lane curves, which has important implications in traffic safety policy and curve geometry design. This paper also discussed limitations and lessons learned from working with the SHRP 2 NDS data. It will benefit future researchers who work with similar type of data.     

The efficiency of using non-culpable crash-claim involvements from insurance data as a means of estimating travel exposure for road user sub-groups

Introduction

Induced exposure has a long history of development and usage in traffic safety research but a major question has always concerned the extent to which the accumulation of culpable and non-culpable involvements can be considered independent.

Method

Culpability assessments of 32,630 vehicles' crash-claim involvements adjudicated by insurance adjusters were matched with vehicle odometer readings taken at emission testing using consistent identification of vehicles and principal operators over a 5-year period.

Result

It was found that the accumulation of culpable crash involvements was not entirely independent of that for non-culpable involvements. However, the rate of non-culpable involvements was determined to be an acceptable surrogate for travel exposure rate where sample sizes were large.

Discussion

The relationship between the rate of non-culpable involvements and the rate of travel exposure for data subsets when both were normalized by the overall sample rates was reminiscent of an accident-volume curve for roadway locations in traffic engineering theory. This suggested that only a portion of non-culpable involvements actually related directly to travel and this lead to a correction factor that could be applied.

Impact on Industry

While lack of independence of involvement rates may be problematic for a direct risk ratio application, it does not invalidate the use of non-culpable involvements to predict travel. For insurers that have a need to estimate travel amounts for different driver/vehicle groups as part of the insurance rating purposes, this can be a useful application.     

When may road fatalities start to decrease?

Introduction

The comparative analysis of macroscopic trends in road safety has been a popular research topic. The objective of this research is to propose a simple and, at the same time, reliable multiple regime model framework for international road safety comparisons, allowing for the identification of slope changes of personal risk curves and respective breakpoints.

Method

The trends of road traffic fatalities in several EU countries have been examined through the temporal evolution of elementary socioeconomic indicators, namely motorized vehicle fleet and population, at the country level.

Results

Piece-wise linear regression models have been fitted, using a methodology that allows the simultaneous estimation of all slopes and breakpoints. The number and location of breakpoints, as well as the slope of the connecting trends, vary among countries, thus indicating different road safety evolution patterns.

Impact on industry

Macroscopic analysis of road accident trends may be proved beneficial for the identification of best examples and the implementation of appropriate programmes and measures, which will lead to important benefits for the society and the economy through the reduction of road fatalities and injuries. Best performing countries and the related programmes and measures adopted may concern several safety improvements at the processes of the road, the vehicle and the insurance industries.

Conclusions

Lessons from the analysis of the past road safety patterns of developed countries provide some insight into the underlying process that relates motorization levels with personal risk and can prove to be beneficial for predicting the road safety evolution of developing countries that may have not yet reached the same breakpoints. Furthermore, the presented framework may serve as a basis to build more elaborate models, including more reliable exposure indicators (such as vehicle-km driven).     

Analysis of bus collision and non-collision incidents using transit ITS and other archived operations data

Introduction

This paper analyzes factors contributing to bus operations safety incidents at TriMet, the transit provider for the Portland Oregon metropolitan region.

Method

The analysis focuses on 4,631 collision and non-collision incidents that occurred between 2006 and 2009. Empirical analysis of these incidents draws on a wide array of operator-level data recovered by transit ITS technologies in combination with information from TriMet's human resources, scheduling, and customer relations databases. Incident frequencies are estimated in relation to operators' demographic characteristics, employment status, assigned work characteristics, service delivery and performance indicators, temporal factors, and customer information.

Results

Apart from identifying factors that are empirically related to the frequency of safety incidents, the findings offer insights into operations policies and practices that hold promise for improving safety.

Impact on Industry

Potential for safety improvement based on analysis of archived operations and human resource data.