Traffic Safety Research Research Articles

Linear chaos for the Quick-Thinking-Driver model

In recent years, the topic of car-following has experimented an increased importance in traffic engineering and safety research. This has become a very interesting topic because of the development of driverless cars (Google driverless cars, http://en.wikipedia.org/wiki/Google_driverless_car). Driving models which describe the interaction between adjacent vehicles in the same lane have a big interest in simulation modeling, such as the Quick-Thinking-Driver model. A non-linear version of it can be given using the logistic map, and then chaos appears. We show that an infinite-dimensional version of the linear model presents a chaotic behaviour using the same approach as for studying chaos of death models of cell growth.

Extending the value of police crash reports for traffic safety research: collecting supplemental data via surveys of drivers

BackgroundPolice crash reports have been used to advance motor vehicle safety research, though their value is limited by their focus on the crash event rather than outcomes of the crash.ObjectiveTo...

A latent class analysis of single-vehicle motorcycle crash severity outcomes

Unobserved heterogeneity has been recognized as a critical issue in traffic safety research that has not been completely addressed or often overlooked, and can lead to biased estimates and incorrect inferences if inappropriate methods are used. This paper uses a latent class approach to investigate the factors that affect crash severity outcomes in single-vehicle motorcycle crashes. Motorcycle crash data from 2001 to 2008 in Iowa were collected with a total of 3644 single-vehicle motorcycle crashes occurring during that time period. A latent class multinomial logit model is estimated that addresses unobserved heterogeneity by identifying two distinct crash data classes with homogeneous attributes. The estimation results show a significant relationship between severe crash injury outcomes and crash-specific factors (such as speeding, run-off road, collision with fixed object and overturn/rollover), riding on high-speed roads, riding on rural roads, riding on dry road surface, riding without a helmet, age (riders older than 25 years old) and impaired riding (riders under the influence of drug, alcohol or medication). The model fit and estimation results underline the need for segmentation of crashes, and suggest that the latent class approach can be a promising tool for modeling motorcycle crash severity outcomes.

Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model

The extant traffic safety research literature includes numerous examples of studies that assess those factors affecting the degree of injury sustained by crash-involved motor vehicle occupants. One important methodological concern in such work is the potential correlation in injury outcomes among occupants involved in the same crash, which may be due to common unobserved factors affecting such occupants. A second concern is unobserved heterogeneity, which is reflective of parameter effects that vary across individuals and crashes. To address these concerns, a random parameters bivariate ordered probit model is estimated to examine factors affecting the degree of injury sustained by drivers involved in angle collisions. The modeling framework distinguishes between the effects of relevant factors on the injury outcomes of the at-fault and not-at-fault parties. The methodological approach allows for consideration of within-crash correlation, as well as unobserved heterogeneity, and results in significantly improved fit as compared to a series of independent models with fixed parameters. While the factors affecting injury severity are found to be similar for both drivers, the magnitudes of these effects vary between the at-fault and not-at-fault drivers. The results demonstrate that injury severity outcomes are correlated for drivers involved in the same crash. Further, the impacts of specific factors may be over- or under-estimated if such correlation is not accounted for explicitly as a part of the analysis. Various factors are found to affect driver injury severity and the random parameters framework shows these effects to vary across crashes and individuals. The analytical approach utilized provides a useful framework for injury severity analysis.

Relating crash frequency and severity: Evaluating the effectiveness of shoulder rumble strips on reducing fatal and major injury crashes

To approach the goal of “Toward Zero Deaths,” there is a need to develop an analysis paradigm to better understand the effects of a countermeasure on reducing the number of severe crashes. One of the goals in traffic safety research is to search for an effective treatment to reduce fatal and major injury crashes, referred to as severe crashes. To achieve this goal, the selection of promising countermeasures is of utmost importance, and relies on the effectiveness of candidate countermeasures in reducing severe crashes. Although it is important to precisely evaluate the effectiveness of candidate countermeasures in reducing the number of severe crashes at a site, the current state-of-the-practice often leads to biased estimates. While there have been a few advanced statistical models developed to mitigate the problem in practice, these models are computationally difficult to estimate because severe crashes are dispersed spatially and temporally, and cannot be integrated into the Highway Safety Manual framework, which develops a series of safety performance functions and crash modification factors to predict the number of crashes. Crash severity outcomes are generally integrated into the Highway Safety Manual using deterministic distributions rather than statistical models. Accounting for the variability in crash severity as a function geometric design, traffic flow, and other roadway and roadside features is afforded by estimating statistical models. Therefore, there is a need to develop a new analysis paradigm to resolve the limitations in the current Highway Safety Manual methods. We propose an approach which decomposes the severe crash frequency into a function of the change in the total number of crashes and the probability of a crash becoming a severe crash before and after a countermeasure is implemented. We tested this approach by evaluating the effectiveness of shoulder rumble strips on reducing the number of severe crashes. A total of 310 segments that have had shoulder rumble strips installed during 2002–2009 are included in the analysis. It was found that shoulder rumble strips reduce the total number of crashes, but have no statistically significant effect on reducing the probability of a severe crash outcome.

Application of Poisson random effect models for highway network screening

In recent years, Bayesian random effect models that account for the temporal and spatial correlations of crash data became popular in traffic safety research. This study employs random effect Poisson Log-Normal models for crash risk hotspot identification. Both the temporal and spatial correlations of crash data were considered. Potential for Safety Improvement (PSI) were adopted as a measure of the crash risk. Using the fatal and injury crashes that occurred on urban 4-lane divided arterials from 2006 to 2009 in the Central Florida area, the random effect approaches were compared to the traditional Empirical Bayesian (EB) method and the conventional Bayesian Poisson Log-Normal model. A series of method examination tests were conducted to evaluate the performance of different approaches. These tests include the previously developed site consistence test, method consistence test, total rank difference test, and the modified total score test, as well as the newly proposed total safety performance measure difference test. Results show that the Bayesian Poisson model accounting for both temporal and spatial random effects (PTSRE) outperforms the model that with only temporal random effect, and both are superior to the conventional Poisson Log-Normal model (PLN) and the EB model in the fitting of crash data. Additionally, the method evaluation tests indicate that the PTSRE model is significantly superior to the PLN model and the EB model in consistently identifying hotspots during successive time periods. The results suggest that the PTSRE model is a superior alternative for road site crash risk hotspot identification.

Methodology for fitting and updating predictive accident models with trend

Reliable predictive accident models (PAMs) (also referred to as Safety Performance Functions (SPFs)) have a variety of important uses in traffic safety research and practice. They are used to help identify sites in need of remedial treatment, in the design of transport schemes to assess safety implications, and to estimate the effectiveness of remedial treatments. The PAMs currently in use in the UK are now quite old; the data used in their development was gathered up to 30 years ago. Many changes have occurred over that period in road and vehicle design, in road safety campaigns and legislation, and the national accident rate has fallen substantially. It seems unlikely that these ageing models can be relied upon to provide accurate and reliable predictions of accident frequencies on the roads today. This paper addresses a number of methodological issues that arise in seeking practical and efficient ways to update PAMs, whether by re-calibration or by re-fitting. Models for accidents on rural single carriageway roads have been chosen to illustrate these issues, including the choice of distributional assumption for overdispersion, the choice of goodness of fit measures, questions of independence between observations in different years, and between links on the same scheme, the estimation of trends in the models, the uncertainty of predictions, as well as considerations about the most efficient and convenient ways to fit the required models.

Large-Scale Observational Study of Drivers’ Cell Phone Use

This report details the 2012 observational study used to determine drivers’ cell phone use in Massachusetts; the study was completed as a component of the annual seat belt observation study by the University of Massachusetts Traffic Safety Research Program. This study identified the magnitude of risky driving behavior throughout Massachusetts. The location, the time of day, and the direction of travel for each observation were determined by random selection with consideration given to the region's fatality rate and length of roadway. Throughout the state, the drivers’ cell phone use at any given daylight hour was determined; the timing allowed for comparisons between different populations. The apparent cell phone use of 17,677 drivers was observed at 145 locations, with a finding of an average cell phone use of 7.0%, a handheld use of 5.6%, and a text messaging use of 1.4%. Females were observed having handheld conversations at a greater rate than males, but there was little difference in text messaging use. Teens were observed text messaging more often than adults and elders, while the handheld conversation rate was similar for teens and adults. Drivers observed wearing seat belts had a lower cell phone use than those who were not wearing seat belts, for text messaging and handheld conversations. Notable variations in cell phone use were identified across various driver demographics, road types, and times of observation and furthered the understanding of drivers’ cell phone use and providing an opportunity for targeted countermeasures.

Conversation with Robert Voas

Conversation with <scp>R</scp>obert <scp>V</scp>oas

A Review of Near-Collision Driver Behavior Models

This article provides a review of recent models of driver behavior in on-road collision situations. In efforts to improve traffic safety, computer simulation of accident situations holds promise as a valuable tool, for both academia and industry. However, to ensure the validity of simulations, models are needed that accurately capture near-crash driver behavior, as observed in real traffic or driving experiments. Scientific articles were identified by a systematic approach, including extensive database searches. Criteria for inclusion were defined and applied, including the requirement that models should have been previously applied to simulate on-road collision avoidance behavior. Several selected models were implemented and tested in selected scenarios. The reviewed articles were grouped according to a rough taxonomy based on main emphasis, namely avoidance by braking, avoidance by steering, avoidance by a combination of braking and steering, effects of driver states and characteristics on avoidance, and simulation platforms. A large number of near-collision driver behavior models have been proposed. Validation using human driving data has often been limited, but exceptions exist. The research field appears fragmented, but simulation-based comparison indicates that there may be more similarity between models than what is apparent from the model equations. Further comparison of models is recommended. This review provides traffic safety researchers with an overview of the field of driver models for collision situations. Specifically, researchers aiming to develop simulations of on-road collision accident situations can use this review to find suitable starting points for their work

A comparison of pharmacoepidemiological study designs in medication use and traffic safety research

In order to explore how the choice of different study designs could influence the risk estimates, a case–crossover and case–time–control study were carried out and their outcomes were compared with those of a traditional case–control study design that evaluated the association between the exposure to psychotropic medications and the risk of having a motor vehicle accident (MVA). A record-linkage database availing data for 3,786 cases and 18,089 controls during the period 2000–2007 was used. The study designs (i.e., case–crossover and case–time–control) were derived from published literature, and the following psychotropic medicines were examined: antipsychotics, anxiolytics, hypnotics and sedatives, and antidepressants, stratified in the two groups selective serotonin reuptake inhibitors (SSRIs) and other antidepressants. Moreover, in order to further investigate the effects of frequency of psychoactive medication exposure on the outcomes of the case–crossover analysis, the data were also stratified by the number of defined daily doses (DDDs) and days of medication use in the 12 months before the motor vehicle accident. Three-thousand seven-hundred fifty-two cases were included in this second part of the case–crossover analysis. The case–crossover design did not show any statistically significant association between psychotropic medication exposure and MVA risk [e.g., SSRIs—Adj. OR = 1.00 (95 % CI: 0.69–1.46); Anxiolytics—Adj. OR = 0.95 (95 % CI: 0.68–1.31)]. The case–time–control design only showed a borderline statistically significant increased traffic accident risk in SSRI users [Adj. OR = 1.16 (95 % CI: 1.01–1.34)]. With respect to the stratifications by the number of DDDs and days of medication use, the analyses showed no increased traffic accident risk associated with the exposure to the selected medication groups [e.g., SSRIs, <20 DDDs—Adj. OR = 0.65 (95 % CI: 0.11–3.87); SSRIs, 16–150 days—Adj. OR = 0.55 (95 % CI: 0.24–1.24)]. In contrast to the above-mentioned results, our recent case–control study found a statistically significant association between traffic accident risk and exposure to anxiolytics [Adj. OR = 1.54 (95 % CI: 1.11–2.15)], and SSRIs [Adj. OR = 2.03 (95 % CI: 1.31–3.14)]. Case–crossover and case–time–control analyses produced different results than those of our recent case–control study (i.e., case–crossover and case–time–control analyses did not show any statistically significant association whereas the case–control analysis showed an increased traffic accident risk in anxiolytic and SSRI users). These divergent results can probably be explained by the differences in the study designs. Given that the case–crossover design is only appropriate for short-term exposures and the case–time–control design is an elaboration of this latter, it can be concluded that, probably, these two approaches are not the most suitable ones to investigate the relation between MVA risk and psychotropic medications, which, on the contrary, are often use chronically.

Development and Validation of a Crash Culpability Scoring Tool

Objective: Several traffic safety research techniques require researchers to separate crash-involved drivers into culpable and nonculpable. Nonculpable drivers are assumed to be randomly involved in crashes by external factors and to approximate a noncollision control population. If this is true, factors that increase crash risk should be found more often in culpable than in nonculpable drivers. Though a culpability scoring tool has been developed for research purposes, that tool does not adequately address winter driving conditions (Robertson and Drummer 1994). Moreover, traditional culpability scoring requires assessors to read and score individual collision reports. The purpose of this study is to develop and validate an automated, rule-based Canadian culpability scoring tool that is capable of rapidly scoring police crash reports from large administrative datasets. Methods: We used an iterative approach to develop and validate our tool. First, the Robertson-Drummer culpability scoring tool was modified to include the extensive police report data collected in the British Columbia Traffic Accident System (TAS) and to account for winter driving conditions. This was done in consultation with traffic safety experts. The scoring tool was automated, employing a rule-based decision model that avoids interpretation of free-text reports. The scoring tool was applied to 73 collisions (134 drivers). Two experts also reviewed these collisions and determined the culpability of each driver. Discrepant cases were discussed to understand why the scoring tool differed from the expert assessment and the scoring tool was modified accordingly. The final tool was compared with expert assessment on another sample of 96 crashes. The tool was also applied to a sample of 2086 crash-involved drivers with known blood alcohol concentrations (BACs) and the adjusted odds of culpability were calculated for several BAC ranges. Results: The final scoring tool included 7 factors and had content validity for traffic safety experts. It had excellent agreement with expert scoring on the first set of collisions (kappa = 0.83, 95% confidence interval [CI]: 0.75–0.91) and on the second set (kappa = 0.84, 95% CI: 0.77–0.92). When applied to crash-involved drivers with known BAC levels, the scoring tool exhibited predictive validity: the odds of culpability increased with higher BACs, consistent with the known dose effect of BAC on crash risk. Conclusions: We have developed an automated culpability scoring tool contextualized to Canadian driving conditions. This tool will allow road safety researchers to assess collision responsibility in large administrative data sets derived from police reports.

Measuring direct and indirect treatment effects using safety performance intervention functions

Recent traffic safety research has advocated the use of the Full Bayes (FB) approach to conduct controlled before–after safety evaluations. The FB approach was shown to offer both methodological and data advantages. However, there is still a lack of complete understanding of the role that the model parameters play in the formulation of treatment effectiveness. This paper offers a novel approach to compute components related to direct and indirect treatment effects under a linear intervention model for the Safety Performance Function (SPF). The isolation of a component corresponding to the direct treatment effects enables the analyst to assess the effectiveness of the countermeasures apart from local (site-related) environmental factors. Two case studies are used to demonstrate the approach. The first case study is based on the results published from a previous safety evaluation in Iowa and the second uses a new data set from British Columbia based on the Insurance Corporation of British Columbia Road Improvement Program. The various direct and indirect components have yielded valuable insight into the effects of the model parameters on treatment impacts. Moreover, the calculation of the treatment effectiveness indices have been simplified by providing straightforward equations in terms of model parameters for the computation of their components without resorting to additional algorithms. More importantly, the results can have a significant impact on the economic evaluation of safety programs and countermeasures by allowing the calculation of collision modification factors (CMFs) that vary with time.

Taking a look behind the wheel: An investigation into the personality predictors of aggressive driving

The present study evaluated a theoretical model of the relationships among six aspects of driver personality (i.e., driving anger and the Big Five personality factors), aggressive driving, and two outcomes of aggressive driving: motor vehicle crashes and moving violations. Data from 308 drivers recruited from two vehicle licensing offices were analyzed using structural equation modeling. As expected, aggressive driving predicted crashes and moving violations. Based on the zero-order correlations, emotional stability, agreeableness, and conscientiousness were related to aggressive driving in the expected directions; however, the picture changed when the joint effects of all variables were examined via structural equation modeling. A model in which driver personality predicted aggressive driving, which in turn predicted crashes and moving violations was supported. Drivers who were high on driving anger and low on agreeableness reported driving more aggressively. Implications for traffic safety professionals and researchers are discussed.

Editorial

Editorial

Managing Open Innovation: Exploring Challenges at the Interfaces of an Open Innovation Arena

Collaborating with peers to gain access to knowledge is an attractive alternative for organizations keen to improve their innovativeness, and the rising popularity of open innovation has resulted in the emergence of new actors in the innovation process. Previous research focuses mainly on the firms that collaborate with these actors. This paper adopts the perspective of an open innovation actor and the managerial challenges involved. It is based on a case study of SAFER, a Swedish traffic and vehicle safety research unit with 22 collaborating partners. The unit, which is here called an open innovation arena, differs from an intermediary in that it both enables open innovation within a specific field of expertise and envisages itself as a key player in that same field. The case study reveals three types of challenges for the management of an open innovation arena: challenges that arise at the interface with partner organizations, challenges related to collaboration between the partners, and challenges related to the arena itself.

Exploratory Research of Driving Fault-Tolerance of Road Traffic Devices: A New Traffic Safety Concept

This paper analyses the source and development of fault tolerance theory, and discusses the working process of fault tolerance, and illustrates the workflow and dependability of fault tolerance. So there is a widespread belief that this is feasible that fault tolerance theory is used in the traffic safety study. Base on summarizing fault tolerance factors, the paper gives fault tolerance conceptual model primarily which could be used in the road traffic safety research. Above all, the paper presents a new idea which study traffic safety issues from the perspective of fault-tolerant , and this is important core content of the paper, and is the purpose of writing this article too.

Development of crash prediction models with individual vehicular data

Typical engineering research on traffic safety focuses on identifying either dangerous locations or contributing factors through a post-crash analysis using aggregated traffic flow data and crash records. A recent development of transportation engineering technologies provides ample opportunities to enhance freeway traffic safety using individual vehicular information. However, little research has been conducted regarding methodologies to utilize and link such technologies to traffic safety analysis. Moreover, traffic safety research has not benefited from the use of hurdle-type models that might treat excessive zeros more properly than zero-inflated models. This study developed a new surrogate measure, unsafe following condition (UFC), to estimate traffic crash likelihood by using individual vehicular information and applied it to basic sections of interstate highways in Virginia. Individual vehicular data and crash data were used in the development of statistical crash prediction models including hurdle models. The results showed that an aggregated UFC measure was effective in predicting traffic crash occurrence, and the hurdle Poisson model outperformed other count data models in a certain case.

An Exploratory Study on Road Traffic Safety Based on Fault-Tolerant Concept

Base on researching the source and development of fault tolerance theory, this paper discusses the working mechanism of fault tolerance, and illustrates the workflow and dependability of fault tolerance. So there is a widespread belief that this is feasible that fault tolerance theory is used in the traffic safety study. Through summarizing the progress of the fault-tolerant study in road traffic field at home and abroad, the paper gets some enlightenment from these literatures, finally gives the prospect of fault-tolerant theory which is used in the road traffic safety research.

Measuring Influence of Accessibility on Accident Severity with Structural Equation Modeling

Structural equation modeling (SEM) is a confirmatory, multivariate technique used to examine causal relationships between variables. Related to path analysis with a goal of selecting a model that best explains underlying relationships between variables, SEM is a useful tool for traffic safety research. This study examined the severity of crashes in terms of factors commonly attributed to accidents. These factors included human, vehicle, and roadway factors, along with accessibility measures that were considered relevant in previous studies. In this study, SEM was used to test an a priori model of crash severity. The analysis was carried out in a two-step process. The measurement model was first tested with a confirmatory factor analysis. After the validity of the measurement model was established, a four-latent-factor structural model was run. With an acceptable model fit, the magnitude of standardized path coefficients from the exogenous latent variables provided a means to assess the relative importance of the latent factors on crash severity. The results showed that the human latent factor was the most influential. Although a positive statistical relationship existed between roadway factors and crash severity, accessibility factors had the opposite effect on crash severity, that is, increased accessibility was shown to reduce crash severity.