Traffic Safety Research Research Articles

Social desirability effects in driver behavior inventories

Problem The use of lie scales to control for common method variance in driver behavior inventories has been very limited. Given that such questionnaires often use self-reported safety variables as criteria, and have social implications, the risk of artefactual associations is high. Method A questionnaire containing scales from several well known driver inventories that have been claimed to predict traffic accident involvement was distributed three times to a group of young drivers in a driver education program, as well as a random group twice. The Driver Impression Management scale (DIM) was used to control for socially desirable responding. Results For all behavior scales, the correlation with the DIM scale was substantial. If a scale correlated with self-reported crashes, the amount of predictive power was more than halved when social desirability was controlled for. Results were similar for both samples and all waves. The predictive power of the behavior scales was not increased when values were averaged over questionnaire waves, as should have been the case if the measurement and predictive power were valid. Results were similar for self-reported penalty points. The present results indicate that even the most well-known and accepted psychometric scales used in driver research are susceptible to social desirability bias. Discussion As social desirability is only one of a number of common method variance mechanisms that can create artefactual associations, and the great popularity of the self-report methodology, the problem for traffic research is grave. Impact on industry Organizations that fund traffic safety research need to re-evaluate their policies regarding what methods are acceptable. The use of self-reported independent and dependent variables can lead to directly misleading results, with negative effects on traffic safety.

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.

Review of Recent Patents in Integrated Vehicle Safety, Advanced Driver Assistance Systems and Intelligent Transportation Systems

In Europe, improvement in traffic safety is a major topic since the economic cost of traffic accidents is estimated to be 2 of the gross national product (about 200 billion Euros per year). Research in traffic safety has led to numerous safety systems for avoidance and reduction of collision severity (primary or active safety), reduction of injury risk during an accident (secondary or passive safety) and post-crash treatment (tertiary safety). A recent trend in traffic safety is the introduction of various Advanced Driver Assistance Systems (ADAS) as primary safety systems acting in normal driving conditions and prior to collision as well as improved interaction of primary and secondary safety systems (Integrated Vehicle Safety). The present paper aims at giving an overview of recent innovations in the field of integrated vehicle safety and ADAS. The focus is on new ideas, methods, products and systems to improve the current situation. The paper is not intended for detailed description of the state of the art and analysis of different safety systems with respect to functionality and effectiveness. Forty-four patents out of several hundreds of relevant documents were reviewed. The patents were selected using search functions of electronic patent databases with the keywords listed below. To reduce their number, the relevancy score of the electronic database was used to decide upon their importance in a first automatic step. The date of publication was limited mainly to the years 2007 to 2009. Non-relevant patents were excluded from the list. The remaining patents are classified and discussed. The present review shows that revolutionary new ideas of safety systems are rare at the moment. Future developments will involve more reliable technologies at lower costs that can be implemented in the complete car fleet to become effective for a statistical relevant reduction of traffic accident consequences. Safe and reliable X-by-wire technologies will enable mechatronic systems with enhanced driver assist functionalities. It will be necessary to provide safety systems with intuitive usability and functionalities that involve the driver in the loop. Combination of safety and comfort functions will be extended in future to allow systems that are beneficial not only in emergency situations. An important issue is the standardisation, security and data privacy in communication technologies as well as solving legal aspects of introduction of advanced mechatronic technologies. Keywords: Integrated vehicle safety, driver assistance system, environment recognition system, human-machine interface, &, intelligent transportation systems

The effect of social desirability on self reported and recorded road traffic accidents

The use of lie scales has a fairly long history in psychometrics, with the intention of identifying and correcting for socially desirable answers. This represents one type of common method variance (bias introduced when both predictors and predicted variables are gathered from the same source), which may lead to spurious associations in self-reports. Within traffic safety research, where self-report methods are used abundantly, it is uncommon to control for social desirability artifacts, or reporting associations between lie scales, crashes and driver behaviour scales. In the present study, it was shown that self-reports of traffic accidents were negatively associated with a lie scale for driving, while recorded ones were not, as could be expected if the scale was valid and a self-report bias existed. We conclude that whenever self-reported crashes are used as an outcome variable and predicted by other self-report measures, a lie scale should be included and used for correcting the associations. However, the only existing lie scale for traffic safety is not likely to catch all socially desirable responding, because traffic safety may not be desirable for all demographic groups. New lie scales should be developed specifically for driver behaviour questionnaires, to counter potential bias and artifactual results. Alternatively, the use of a single source of data should be discontinued.

Helmets for preventing injury in motorcycle riders

Motorcycle crash victims form a high proportion of those killed or injured in road traffic crashes. Injuries to the head, following motorcycle crashes, are a common cause of severe morbidity and mortality. It seems intuitive that helmets should protect against head injuries but it has been argued that motorcycle helmet use decreases rider vision and increases neck injuries. This review will collate the current available evidence on helmets and their impact on mortality, and head, face and neck injuries following motorcycle crashes. To assess the effects of wearing a motorcycle helmet in reducing mortality and head and neck injury following motorcycle crashes. We searched the Cochrane Injuries Group Specialised Register, Cochrane Central Register of Controlled Trials (The Cochrane Library issue 2, 2007), MEDLINE (up to April 2007), EMBASE (up to April week 16, 2007), CINAHL (January 1982 to February 2003), TRANSPORT (up to issue 12, 2006) (TRANSPORT combines the following databases: Transportation Research Information Services (TRIS) International Transport Research Documentation (ITRD) formerly International Road Research Documentation (IRRD), ATRI (Australian Transport Index) (1976 to Feb 2003), Science Citation Index were searched for relevant articles. Websites of traffic and road safety research bodies including government agencies were also searched. Reference lists from topic reviews, identified studies and bibliographies were examined for relevant articles. We considered studies that investigated a population of motorcycle riders who had crashed, examining helmet use as an intervention and with outcomes that included one or more of the following: death, head, neck or facial injury. We included any studies that compared an intervention and control group. Therefore the following study designs were included: randomised controlled trials, non-randomised controlled trials, cohort, case-control and cross-sectional studies. Ecological and case series studies were excluded. Two authors independently screened reference lists for eligible articles. Two authors independently assessed articles for inclusion criteria. Data were abstracted by two independent authors using a standard abstraction form. Sixty-one observational studies were selected of varying quality. Despite methodological differences there was a remarkable consistency in results, particularly for death and head injury outcomes. Motorcycle helmets were found to reduce the risk of death and head injury in motorcyclists who crashed. From four higher quality studies helmets were estimated to reduce the risk of death by 42% (OR 0.58, 95% CI 0.50 to 0.68) and from six higher quality studies helmets were estimated to reduce the risk of head injury by 69% (OR 0.31, 95% CI 0.25 to 0.38). Insufficient evidence was found to estimate the effect of motorcycle helmets compared with no helmet on facial or neck injuries. However, studies of poorer quality suggest that helmets have no effect on the risk of neck injuries and are protective for facial injury. There was insufficient evidence to demonstrate whether differences in helmet type confer more or less advantage in injury reduction. Motorcycle helmets reduce the risk of death and head injury in motorcycle riders who crash. Further well-conducted research is required to determine the effects of helmets and different helmet types on mortality, head, neck and facial injuries. However, the findings suggest that global efforts to reduce road traffic injuries may be facilitated by increasing helmet use by motorcyclists.

Crosswalk Confusion

Traffic safety researchers have long argued that driver behavior outweighs physical elements (such as road design) as a causal factor in motor vehicle collisions. A fundamental causal component of pedestrian-vehicle collisions is also behavior: that of the driver and that of the pedestrian. One determinant of this behavior may be whether the driver, the pedestrian, or both understand the motor vehicle code, which demarcates the right-of-way in pedestrian-vehicle interactions. That is, inappropriate or unlawful behavior may occur because the law is not understood or is misunderstood. Previous studies have shown that drivers and pedestrians have a limited knowledge of pedestrian right-of-way laws. This research expands on these studies by specifically considering knowledge of right-of-way laws related to marked and unmarked crosswalks. Driver and pedestrian knowledge was assessed by the use of intercept surveys and focus groups conducted in the San Francisco Bay Area in California. The results confirm that a substantial level of confusion about pedestrian right-of-way laws exists. This confusion was exacerbated by intersections that had unstriped, or unmarked, crosswalks. The implications for engineering, education, and enforcement countermeasures in light of these findings are discussed; and areas for further research are proposed.

Driving task analysis as a tool in traffic safety research and practice

The paper explains the need for task analysis in the context of car driving, because the interaction between the car drivers‘ capabilities and the demands of the actual driving task determines the outcome in terms of a more or less safe driving behaviour. After reviewing past approaches, the main focus is on the presentation of a new procedure for driving task analysis and driver requirement assessment (SAFE: Situative Anforderungsanalyse von Fahraufgaben). A framework for task analysis is derived both from classifications of road traffic situations and a model of the drivers’ information processing. The first step of the procedure is to divide a given driving task into subtasks. These subtasks are appointed to defined stretches of the road and the time structure of the subtasks is determined. For each subtask an analysis format is used, that organizes different requirements into perception, expectation, judgement, memory, decision and driver action. Then, typical driver errors are attached to the subtasks, and all the information together is compressed to ratings of complexity and risk in order to derive the crucial subtasks. Finally, some examples of how the method can be applied are presented and its future usefulness is discussed.

California’s Graduated Driver License Law

Many traffic safety researchers believe Graduated Driver Licensing (GDL) laws save lives by imposing restrictions, stronger licensing requirements, and delayed licensure status on drivers under age 18. To determine the effects of California’s GDL law on traffic fatalities among older (age 18-19) and younger (age 16-17) teens by age, sex, accident characteristics, and license status, mortality data from California’s Center for Health Statistics, driver and accident data from the Fatality Analysis Reporting System, and population data from the California Department of Finance were analyzed for the 1995-2004 period. Compared to California who began driving before the GDL law took effect and to corresponding trends among Californians ages 20 through 44, 16- and 17-year-olds subject the GDL law experienced net decreases of 13% in motor vehicle fatality rates and 14% in driver involvements in fatal accidents. However, 18- and 19-year-olds subjected to GDL programs experienced net increases of 11% in traffic fatalities and 10% in involvements of drivers in fatal accidents, more than offsetting the declines among younger teenagers. These results support reassessment of the effects of the GDL law, including its specific requirements, on older teenagers.

Influence of Land Use, Population, Employment, and Economic Activity on Accidents

In this study, the relationships between land use, population, employment by sector, economic output, and motor vehicle accidents are explored. Through the use of comprehensive data from the largest county in Hawaii, the relationships are modeled in a uniform 0.1-mi2 (0.259-km2) grid structure and with various linear regression models. This method has an advantage over other approaches that have typically used unevenly sized and shaped traffic analysis zones, census tracts, or block groups. Positive, statistically significant relationships among population, job counts, economic output, and accidents are identified. After some of the general effects are sorted through, a negative binomial (NB) model is used to look at the absolute and relative effects of these factors on the number of pedestrian, bicycle, vehicle-to-vehicle, and total accidents. With a multivariate model, the different effects can be compared and the specific nature of the relationships between zonal characteristics and accidents can be identified. While there is, in general, a significant relationship between all these values, the effects are more pronounced with vehicular crashes than with those involving pedestrians or bicyclists. In addition to the general effects, the influences of employment, economic development, and various activities on the level and type of accidents are investigated. Some challenges associated with modeling these relationships are described, as are implications for traffic safety research. The paper adds to the growing volume of traffic safety research integrating NB regression models and geographic information systems.

Multilevel Modeling in Traffic Safety Research: Two Empirical Examples Illustrating the Consequences of Ignoring Hierarchies

Objectives: This commentary illustrates the advantages of multilevel modeling compared to statistical techniques that ignore hierarchies, based on two empirical traffic safety examples. Methods: The common concept shared by different definitions of multilevel modeling is identified and illustrated. Each definition defines multilevel modeling in its own way but they all refer to hierarchies. Conceptual issues inherently related to hierarchies are then pointed out and illustrated. Results: Broadly speaking there are two important consequences of ignoring a hierarchical structure in the data. The first consequence, underestimation of standard errors, is illustrated with data from an observational study on seatbelt behavior. Two effects that were significant at the 5% level in a single-level model were no longer found to be significant in a two-level model. The single-level model is therefore bound to lead to erroneous conclusions regarding variables that could have an impact on seatbelt use and, ultimately, on increasing the level of traffic safety. The second consequence, related to contextual information, is illustrated with data from a roadside survey on drink driving. Of particular interest is the relationship between Traffic Count, an aggregated level 2 explanatory variable and Odds of drink driving, an individual level 1 dependent variable. Conclusions: Like every statistical technique, multilevel models should be used with caution and reservation. However, given certain limitations, multilevel modeling is very useful and valuable to traffic safety research.

An Observational Study of Safety Belt Use Among Taxi Drivers in Boston

Although safety belt legislation exists for drivers in Massachusetts, there is an exemption for taxicab drivers. According to the most recent data from the Governor's Highway Safety Bureau, the observed safety belt use rate of drivers in Massachusetts is 64%. However, the safety belt use among taxicab drivers in Boston is unknown. An observational study was conducted to assess the prevalence of safety belt use among taxicab drivers in Boston. We compared our findings with state-level data obtained from the Governor's Highway Safety Bureau. Research staff made observations of taxicabs arriving at various sites within Boston (Logan Airport, Back Bay subway entrance, Government Center, and a hospital in Boston). The methodology used in this study was adapted from MASS-Safe, the Traffic Safety Research Program of the Governor's Highway Safety Bureau. In brief, research staff assessed the use of shoulder safety belts worn by taxicab drivers in Boston. Researchers observed traffic flow coming from a single direction at each observation site. These observers were instructed to include only taxicabs in motion, approaching in the nearest lane to the sidewalk. Two hundred fifty taxicabs were observed during the study period. Of these, 247 taxicab drivers were men (98.9%), whereas 3 were women (1.2%). Overall, 17 of 250 taxicab drivers (6.8%; 95% confidence interval 3.9% to 9.7%) wore safety belts, whereas 233 (93.2%) did not. The safety belt use among taxicab drivers in Boston is 6.8%, markedly lower than the state-level safety belt use of other drivers in Massachusetts. Stronger safety belt use legislation in Massachusetts may help to improve safety belt use among taxi drivers in Boston.

Role of Exposure in Analysis of Road Accidents: A Belgian Case Study

Exposure is a key variable in traffic safety research. In the literature, it is noted as the first and primary determinant of traffic safety. In many cases, however, no valid exposure measure is available. In Belgium, monthly traffic counts for 12 years are available. This offers the opportunity to investigate the added value of exposure in models, next to legal, economic, and climatologic variables. Multiple regression with autoregressive moving average (ARMA) errors is used to quantify the impact of these factors on aggregated traffic safety. For each dependent variable, a model with and without exposure is constructed. The models show that exposure is significantly related to the number of accidents with persons killed and seriously injured and to the corresponding victims, but not to the lightly injured outcomes. Moreover, the addition or deletion of exposure does not influence the effects of the remaining variables in the model. The effects of exposure clearly depend on the type of measure used and on the time horizon considered. The framework of a regression model with ARMA errors allows for missing variables being accounted for by the error term. Even without a variable such as exposure, valid models can be constructed.

Role of Exposure in Analysis of Road Accidents

Exposure is a key variable in traffic safety research. In the literature, it is noted as the first and primary determinant of traffic safety. In many cases, however, no valid exposure measure is available. In Belgium, monthly traffic counts for 12 years are available. This offers the opportunity to investigate the added value of exposure in models, next to legal, economic, and climatologic variables. Multiple regression with autoregressive moving average (ARMA) errors is used to quantify the impact of these factors on aggregated traffic safety. For each dependent variable, a model with and without exposure is constructed. The models show that exposure is significantly related to the number of accidents with persons killed and seriously injured and to the corresponding victims, but not to the lightly injured outcomes. Moreover, the addition or deletion of exposure does not influence the effects of the remaining variables in the model. The effects of exposure clearly depend on the type of measure used and on the time horizon considered. The framework of a regression model with ARMA errors allows for missing variables being accounted for by the error term. Even without a variable such as exposure, valid models can be constructed.

Diagnostic Methodology for the Detection of Safety Problems at Intersections

There is an established consensus among traffic safety researchers that a nonlinear relationship exists between traffic exposure and safety. This relationship is reflected by the safety performance functions (SPFs) calibrated for various classes of roads and intersections. One of the main uses of SPFs is to identify locations with potential for accident reduction. While this application is certainly important, the use of SPFs provides no information related to the nature of the accident occurrence. Without being able to relate accident frequency and severity to roadway geometrics, traffic control devices, roadside features, roadway condition, driver behavior, or vehicle type, it is not possible to develop effective countermeasures. A methodology was developed to provide guidance in diagnostics of safety problems, recognition of accident patterns, and development of appropriate countermeasures. Considering that traffic accidents can be viewed as random Bernoulli trials, it is possible to detect deviation from the statistical process by computing observed cumulative probability for each of the accident characteristics. Detection of an accident pattern at an intersection suggests the presence of an element in the roadway environment that triggered a deviation from a random statistical process in the direction of reduced safety. Identification of such an element always provides a critical clue to accident causality.

Computer-Intensive Methods in Traffic Safety Research

The analysis of traffic safety data archives has improved markedly with the development of procedures that are heavily dependent upon computers. Three such procedures are described here. The first procedure involves using computers to assist in the identification and correction of invalid data. The second procedure makes greater computational demands, and involves using computerized algorithms to fill in the “gaps” that typically occur in archival data when information regarding key variables is not available. The third and most computer-intensive procedure involves using data mining techniques to search archives for interesting and important relationships between variables. These procedures are illustrated using examples from data archives that describe the characteristics of traffic accidents in the USA and Australia.

Tecnología del simulador para evaluación de conductores

EVICA is a project carried out by the Traffic and Road Safety Research Institute at the University of Valencia (INTRAS) and the Mapfre Institute of Road Safety. This project uses driving simulator technology for assessing drivers. This assessment is done on two levels: Control (skill based) and Manoeuvre (Rule based). During the simulated drive, performance parameters are recorded which are later converted into a custom-tailored evaluation of the participants” driving styles. It is hoped that this information will the improve safety of other drivers on the road and their own while driving.

Finding Fault in Motorcycle Crashes in Hawaii: Environmental, Temporal, Spatial, and Human Factors

Patterns of fault among drivers and motorcycle riders involved in collisions in Hawaii are examined. Personal and behavioral characteristics of drivers and riders involved in crashes are described, then temporal, roadway, and environmental factors associated with crashes between motorcycles and other motor vehicles are discussed. An argument is made that focusing on fault provides a strategic starting point for educational and traffic enforcement programs for drivers and motorcycle riders alike. A fault model is built by using logistic regression to predict the odds of fault for motorcyclists and vehicles involved in crashes. The spatial distribution of at-fault motorcyclists and drivers is mapped to determine if there are distinct spatial patterns for enforcement and educational efforts. The implications for motorcycle safety, driver education, law enforcement, and traffic safety research are discussed.

Car-following: a historical review

In recent years, the topic of car-following has become of increased importance in traffic engineering and safety research. Models of this phenomenon, which describe the interaction between (typically) adjacent vehicles in the same lane, now form the cornerstone for many important areas of research including (a) simulation modelling, where the car-following model (amongst others) controls the motion of the vehicles in the network, and (b) the functional definition of advanced vehicle control and safety systems (AVCSS), which are being introduced as a driver safety aid in an effort to mimic driver behaviour but remove human error. Despite the importance of this area however, no overview of the models availability and validity exists. It is the intent of this paper therefore to briefly assess the range of options available in the choice of car-following model, and assess just how far work has proceeded in our understanding of what, at times, would appear to be a simple process.

Residence location of drivers involved in fatal crashes

Traffic safety researchers have long known that the majority of fatal crashes occur on rural roads, but it is not clear whether these crashes involve people who live in rural areas or residents of urban areas traveling on rural roads. `Geodemographic' market-research tools allow determination of the urbanization of drivers' residence locations from their postal `zip code.' Using data from the 1988–1992 files of the Fatal Accident Reporting System (FARS) maintained by the National Highway Traffic Safety Administration (NHTSA), this study determined the residence location of several subgroups of drivers involved in fatal crashes. Not only did the majority of fatal crashes occur in rural areas, but the majority of fatal crashes involved rural and small-town residents, and the majority of the rural and small-town residents involved in fatal crashes were traveling on rural roads.

9733251 Emission behavior of unregulated matter from cold start catalyst equipped vehicles with FTIR method Noriyuki Koike, Hisakazu Suzuki, Matsuo Odaka (Traffic Safety and Nuisance Research Institute)

9733251 Emission behavior of unregulated matter from cold start catalyst equipped vehicles with FTIR method Noriyuki Koike, Hisakazu Suzuki, Matsuo Odaka (Traffic Safety and Nuisance Research Institute)