Identifying crash contributory factors at urban roundabouts and using association rules to explore their relationships to different crash types

The use of roundabouts improves intersection safety by eliminating or altering conflict types, reducing crash severity, and causing drivers to reduce speeds. However, roundabout performances can degrade if precautions are not taken during either the design or the operation phase. Therefore, additional information on the safety of the roundabouts is extremely helpful for planners and designers in identifying existing deficiencies and in refining the design criteria currently being used. The aim of the paper was to investigate the crash contributory factors in 15 urban roundabouts located in Italy and to study the interdependences between these factors.The crash data refer to the period 2003–2008. The identification of the crash contributory factors was based on site inspections and rigorous analyses performed by a team of specialists with a relevant road safety engineering background. Each roundabout was inspected once every year from 2004 to 2009, both in daytime and in nighttime. Overall, 62 different contributory factors and 2156 total contributory factors were identified. In 51 crashes, a single contributory factor was found, whereas in the other 223 crashes, a combination of contributory factors was identified. Given the large amount of data, the interdependences between the contributory factors and between the contributory factors and the different crash types were explored by an association discovery. Association discovery is the identification of sets of items (i.e., crash contributory factors and crash types in our study) that occur together in a given event (i.e., a crash in our study). The rules were filtered by support, confidence, and lift. As a result, 112 association rules were discovered.Overall, numerous contributory factors related to the road and environment deficiencies but not related to the road user or to the vehicle were identified. The most important factors related to geometric design were the radius of deflection and the deviation angle. In existing roundabouts, the improvement of these factors might be quite expensive, but the crucial role of a moderate radius of deflection and a large deviation angle in the design of new roundabouts was stressed. Many of the contributory factors were related to markings and signs, and these factors could be easily removed with low-cost safety measures. Furthermore, because of the association between the markings, signs, and geometric design contributory factors, the study results suggest that the improvement in markings and signs might also have a significant effect in the sites where geometric design deficiencies were identified as contributory factors.     

Crash reconstruction and crash modification factors

This paper addresses the following question: Under what conditions can reconstructed road crashes be used to estimate the effect of a safety-related countermeasure? Results developed by Pearl and his associates are used to draw two main conclusions. First, when one can (1) identify a structural equation describing a type of crash, (2) identify an additional structural equation describing the countermeasure's impact, and (3) estimate the initiating conditions for a set of reconstructed crashes, then a lower bound for a crash modification factor can be estimated by simulating whether or not each of the reconstructed crashes would still have occurred had the countermeasure been present. If the countermeasure's effect is monotonic this bound becomes tight. Second, in situations where it is not possible to reliably identify the structural equations needed for simulation, but where one can (1) identify a set of crash inputs which, when given, make the crash outcome conditionally independent of the countermeasure, and (2) predict how the distribution of these inputs will change in response to the countermeasure, then nonparametric estimation of the countermeasure's crash modification factor is possible. When it is not possible to predict the countermeasure's effect on the conditioning variables it may still be possible to identify constraints or specifications which the countermeasure should satisfy in order to realize a target crash modification.     

Validity of using linked hospital and police traffic crash records to analyse motorcycle injury crash characteristics

Linking hospital discharge and police traffic crash records has been used to provide information on causes and outcomes for hospitalised traffic crash cases. Motorcyclists are particularly vulnerable to injury in a traffic crash, but no published linkage studies have reported in detail on this road user group. The present study examined motorcycle traffic crash injury cases in New Zealand in 2000–2004 by probabilistically linking national hospital discharge records with police traffic crash reports. Injury cases had to have spent at least one night in hospital before being discharged and were defined as serious or moderate based on their International Classification of Disease Injury Severity Scores (ICISS). Despite a robust linkage process, only 46% of cases could be linked to a police record; 60% of the serious injuries and 41% of the moderate. The low linkage was most likely due to under-reporting of crashes to or by the police. While moderate injury cases were expected to be under-reported, the level of under-reporting of cases with serious threat-to-life injuries is concerning. To assess whether the linked dataset could provide valid information on the crash circumstances and injury outcomes of hospitalised motorcycle crash cases, the characteristics of the linked and unlinked hospital discharge cases were compared using chi-square tests and multivariate logistic regression. Serious injury cases were less likely to be linked if only one vehicle was involved, or the injured riders and passengers were younger than 20 years or spent less than one week in hospital. For moderate injury cases, there were also differences in linkage by injured body region and crash month. While these discrepancies need to taken into consideration when interpreting results, the linked hospital-police dataset has the potential to provide insights into motorcycle crash circumstances and outcomes not otherwise obtainable.     

Exploration and comparison of crash modification factors for multiple treatments on rural multilane roadways

As multiple treatments (or countermeasures) are simultaneously applied to roadways, there is a need to assess their combined safety effects. Due to a lack of empirical crash modification factors (CMFs) for multiple treatments, the Highway Safety Manual (HSM) and other related studies developed various methods of combining multiple CMFs for single treatments. However, the literature did not evaluate the accuracy of these methods using CMFs obtained from the same study area. Thus, the main objectives of this research are: (1) develop CMFs for two single treatments (shoulder rumble strips, widening shoulder width) and one combined treatment (shoulder rumble strips + widening shoulder width) using before–after and cross-sectional methods and (2) evaluate the accuracy of the combined CMFs for multiple treatments estimated by the existing methods based on actual evaluated combined CMFs. Data was collected for rural multi-lane highways in Florida and four safety performance functions (SPFs) were estimated using 360 reference sites for two crash types (All crashes and Single Vehicle Run-off Roadway (SVROR) crashes) and two severity levels (all severity (KABCO) and injury (KABC)).     

Identifying crash involvement among older drivers: agreement between self-report and state records

Older drivers have a high crash rate per vehicle mile of travel. Coupled with the growth of the number of older drivers on the road, this has generated interest in the identification of factors which place older drivers at increased risk. However, much of the existing research on medical and functional risk factors for crash involvement has generally been inconsistent. Methodological differences between studies have been hypothesized as being partly responsible for such inconsistencies. The source of information used to identify crash-involved drivers has been identified as one such difference. This paper reports on the agreement between self-report and state record for identifying crash involved-older drivers. We also sought to determine whether the prevalence of visual and cognitive impairment differs across crash-involved drivers identified by either or both sources. Finally, we assessed whether risk factors for crash involvement differed when crash-involved drivers were identified by either self-report or state records. Results indicated that there was a moderate level of agreement between self-reported and state-recorded crash involvement (kappa=0.45). However, we did find significant differences between crash-involved drivers identified via state records and/or self-report with respect to demographic (age, race), driving (annual mileage, days per week driven), and vision impairment (acuity, contrast sensitivity, peripheral visual field sensitivity, useful field of view). We also found that the possibility for biased measures of association is real. Useful field of view impairment was associated with both self-reported and state-recorded crash involvement; however, the magnitude of the associations was disparate. Moreover, glaucoma was identified as a significant risk factor when considering state-recorded crashes but not self-reported crashes. While validation of these findings is required, research designed to identify risk factors for crash involvement among older drivers should carefully consider the issue of case definition, particularly if self-report is used to identify crash-involved older drivers.     

Integrated traffic conflict model for estimating crash modification factors

Crash modification factors (CMFs) for road safety treatments are usually obtained through observational models based on reported crashes. Observational Bayesian before-and-after methods have been applied to obtain more precise estimates of CMFs by accounting for the regression-to-the-mean bias inherent in naive methods. However, sufficient crash data reported over an extended period of time are needed to provide reliable estimates of treatment effects, a requirement that can be a challenge for certain types of treatment. In addition, these studies require that sites analyzed actually receive the treatment to which the CMF pertains. Another key issue with observational approaches is that they are not causal in nature, and as such, cannot provide a sound “behavioral” rationale for the treatment effect. Surrogate safety measures based on high risk vehicle interactions and traffic conflicts have been proposed to address this issue by providing a more “causal perspective” on lack of safety for different road and traffic conditions. The traffic conflict approach has been criticized, however, for lacking a formal link to observed and verified crashes, a difficulty that this paper attempts to resolve by presenting and investigating an alternative approach for estimating CMFs using simulated conflicts that are linked formally to observed crashes. The integrated CMF estimates are compared to estimates from an empirical Bayes (EB) crash-based before-and-after analysis for the same sample of treatment sites. The treatment considered involves changing left turn signal priority at Toronto signalized intersections from permissive to protected-permissive. The results are promising in that the proposed integrated method yields CMFs that closely match those obtained from the crash-based EB before-and-after analysis.     

Analysis of left-turn crash injury severity by conflicting pattern using partial proportional odds models

The purpose of this study is to examine left-turn crash injury severity. Left-turning traffic colliding with opposing through traffic and with near-side through traffic are the two most frequently occurring conflicting patterns among left-turn crashes (Patterns 5 and 8 in the paper, respectively), and they are prone to be severe. Ordered probability models with either logit or probit function is commonly applied in crash injury severity analyses; however, its critical assumption that the slope coefficients do not vary over different alternatives except the cut-off points is usually too restrictive. Partial proportional odds models are generalizations of ordered probability models, for which some of the beta coefficients can differ across alternatives, were applied to investigate Patterns 5 and 8, and the total left-turn crash injuries. The results show that partial proportional odds models consistently perform better than ordered probability models. By focusing on specific conflicting patterns, locating crashes to the exact crash sites and relating approach variables to crash injury in the analysis, researchers are able to investigate how these variables affect left-turn crash injuries. For example, opposing through traffic and near-side crossing through traffic in the hour of collision were identified significant for Patterns 5 and 8 crash injuries, respectively. Protected left-turn phasing is significantly correlated with Pattern 5 crash injury. Many other variables in driver attributes, vehicular characteristics, roadway geometry design, environmental factors, and crash characteristics were identified. Specifically, the use of the partial proportional formulation allows a much better identification of the increasing effect of alcohol and/or drug use on crash injury severity, which previously was masked using the conventional ordered probability models.     

Developing crash modification functions to assess safety effects of adding bike lanes for urban arterials with different roadway and socio-economic characteristics

Although many researchers have estimated crash modification factors (CMFs) for specific treatments (or countermeasures), there is a lack of studies that explored the heterogeneous effects of roadway characteristics on crash frequency among treated sites. Generally, the CMF estimated by before–after studies represents overall safety effects of the treatment in a fixed value. However, as each treated site has different roadway characteristics, there is a need to assess the variation of CMFs among the treated sites with different roadway characteristics through crash modification functions (CMFunctions). The main objective of this research is to determine relationships between the safety effects of adding a bike lane and the roadway characteristics through (1) evaluation of CMFs for adding a bike lane using observational before–after with empirical Bayes (EB) and cross-sectional methods, and (2) development of simple and full CMFunctions which are describe the CMF in a function of roadway characteristics of the sites. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed. Moreover, socio-economic parameters were collected and included in CMFunctions and SPFs (1) to capture the effects of the variables that represent volume of bicyclists and (2) to identify general relationship between the CMFs and these characteristics. In order to achieve better performance of CMFunctions, data mining techniques were used.     

Prevalence and factors associated with road traffic crash among taxi drivers in Hanoi,Vietnam

Injury due to road traffic crash is a major cause of ill health and premature deaths in developing countries. Taxis provide a main mode of public transport in Vietnam but there has been little research on the risk of crash for taxi drivers. This retrospective study collected information on taxi crashes for the period 2006–2009 by interviewing drivers from five taxi companies in Hanoi, Vietnam, using a structured questionnaire. Of the total 1214 participants recruited, 276 drivers reported at least one crash, giving an overall crash prevalence of 22.7%. Among the crashed group, 50 drivers (18.1%) were involved in two to four crashes. Logistic regression analysis further identified age of driver, type of driving licence, employment status, perceived sufficiency of income, seat-belt usage, and traffic infringement history to be significantly associated with the crash risk. Further prospective and qualitative studies are recommended to provide detailed crash characteristics as well as behaviour and perception of taxi drivers, so that an effective intervention can be developed to improve road safety and to prevent injury of these commercial drivers.     

Prioritizing Highway Safety Manual’s crash prediction variables using boosted regression trees

The Highway Safety Manual (HSM) recommends using the empirical Bayes (EB) method with locally derived calibration factors to predict an agency’s safety performance. However, the data needs for deriving these local calibration factors are significant, requiring very detailed roadway characteristics information. Many of the data variables identified in the HSM are currently unavailable in the states’ databases. Moreover, the process of collecting and maintaining all the HSM data variables is cost-prohibitive. Prioritization of the variables based on their impact on crash predictions would, therefore, help to identify influential variables for which data could be collected and maintained for continued updates. This study aims to determine the impact of each independent variable identified in the HSM on crash predictions. A relatively recent data mining approach called boosted regression trees (BRT) is used to investigate the association between the variables and crash predictions. The BRT method can effectively handle different types of predictor variables, identify very complex and non-linear association among variables, and compute variable importance. Five years of crash data from 2008 to 2012 on two urban and suburban facility types, two-lane undivided arterials and four-lane divided arterials, were analyzed for estimating the influence of variables on crash predictions. Variables were found to exhibit non-linear and sometimes complex relationship to predicted crash counts. In addition, only a few variables were found to explain most of the variation in the crash data.     

Assessment of safety effects for widening urban roadways in developing crash modification functions using nonlinearizing link functions

Since a crash modification factor (CMF) represents the overall safety performance of specific treatments in a single fixed value, there is a need to explore the variation of CMFs with different roadway characteristics among treated sites over time. Therefore, in this study, we (1) evaluate the safety performance of a sample of urban four-lane roadway segments that have been widened with one through lane in each direction and (2) determine the relationship between the safety effects and different roadway characteristics over time. Observational before–after analysis with the empirical Bayes (EB) method was assessed in this study to evaluate the safety effects of widening urban four-lane roadways to six-lanes. Moreover, the nonlinearizing link functions were utilized to achieve better performance of crash modification functions (CMFunctions). The CMFunctions were developed using a Bayesian regression method including the estimated nonlinearizing link function to incorporate the changes in safety effects of the treatment over time. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed and used for EB estimation.     

Development of crash modification factors for changing lane width on roadway segments using generalized nonlinear models

This study evaluates the effectiveness of changing lane width in reducing crashes on roadway segments. To consider nonlinear relationships between crash rate and lane width, the study develops generalized nonlinear models (GNMs) using 3-years crash records and road geometry data collected for all roadway segments in Florida. The study also estimates various crash modification factors (CMFs) for different ranges of lane width based on the results of the GNMs. It was found that the crash rate was highest for 12-ft lane and lower for the lane width less than or greater than 12 ft. GNMs can extrapolate this nonlinear continuous effect of lane width and estimate the CMFs for any lane width, not only selected lane widths, unlike generalized linear models (GLMs) with categorical variables. The CMFs estimated using GNMs reflect that crashes are less likely to occur for narrower lanes if the lane width is less than 12 ft whereas crashes are less likely to occur for wider lanes if the lane width is greater than 12 ft. However, these effects varied with the posted speed limits as the effect of interaction between lane width and speed limit was significant. The estimated CMFs show that crashes are less likely to occur for lane widths less than 12 ft than the lane widths greater than 12 ft if the speed limit is higher than or equal to 40 mph. It was also found from the CMFs that crashes at higher severity levels (KABC and KAB) are less likely to occur for lane widths greater or less than 12 ft compared to 12-ft lane. The study demonstrates that the CMFs estimated using GNMs clearly reflect variations in crashes with lane width, which cannot be captured by the CMFs estimated using GLMs. Thus, it is recommended that if the relationship between crash rate and lane width is nonlinear, the CMFs are estimated using GNMs.     

A review of risk factors and patterns of motorcycle injuries

Per vehicle mile traveled, motorcycle riders have a 34-fold higher risk of death in a crash than people driving other types of motor vehicles. While lower-extremity injuries most commonly occur in all motorcycle crashes, head injuries are most frequent in fatal crashes. Helmets and helmet use laws have been shown to be effective in reducing head injuries and deaths from motorcycle crashes. Alcohol is the major contributing factor to fatal crashes. Enforcement of legal limits on the blood alcohol concentration is effective in reducing motorcycle deaths, while some alcohol-related interventions such as a minimal legal drinking age, increased alcohol excise taxes, and responsible beverage service specifically for motorcycle riders have not been examined. Other modifiable protective or risk factors comprise inexperience and driver training, conspicuity and daytime headlight laws, motorcycle licensure and ownership, riding speed, and risk-taking behaviors. Features of motorcycle use and potentially effective prevention programs for motorcycle crash injuries in developing countries are discussed. Finally, recommendations for future motorcycle-injury research are made.     

Using multivariate adaptive regression splines (MARS) to develop crash modification factors for urban freeway interchange influence areas

Crash modification factors (CMFs) are used to measure the safety impacts of changes in specific geometric characteristics. Their development has gained much interest following the adoption of CMFs by the recently released Highway Safety Manual (HSM) and SafetyAnalyst tool in the United States. This paper describes a study to develop CMFs for interchange influence areas on urban freeways in the state of Florida. Despite the very different traffic and geometric conditions that exist in interchange influence areas, most previous studies have not separated them from the rest of the freeway system in their analyses. In this study, a promising data mining method known as multivariate adaptive regression splines (MARS) was applied to develop CMFs for median width and inside and outside shoulder widths for “total” and “fatal and injury” (FI) crashes. In addition, CMFs were also developed for the two most frequent crash types, i.e., rear-end and sideswipe. MARS is characterized by its ability to accommodate the nonlinearity in crash predictors and to allow the impact of more than one geometric variable to be simultaneously considered. The methodology further implements crash predictions from the model to identify changes in geometric design features. Four years of crashes from 2007 to 2010 were used in the analysis and the results showed that MARS's prediction capability and goodness-of-fit statistics outperformed those of the negative binomial model. The influential variables identified included the outside and inside shoulder widths, median width, lane width, traffic volume, and shoulder type. It was deduced that a 2-ft increase in the outside and inside shoulders (from 10 ft to 12 ft) reduces FI crashes by 10% and 33%, respectively. Further, a 42-ft reduction in the median width (from 64 ft to 22 ft) increases the rear-end, total, and FI crashes by 473%, 263%, and 223%, respectively.     

Identifying factors that predict persistent driving after drinking,unsafe driving after drinking,and driving after using cannabis among young adults

The main aim of this study was to identify adolescent/young adulthood factors that predicted persistent driving after drinking, persistent unsafe driving after drinking, and persistent cannabis use and driving among young adults. It was a longitudinal study of a birth cohort (n=933, 474 males and 459 females) and was based on data collected at ages 15, 18, 21 and 26 years. At each of these ages members of the cohort attended the research unit for a personal interview by a trained interviewer, using a standardised questionnaire. For this study, the data for the outcome measures (persistent driving after drinking, persistent unsafe driving after drinking, and persistent driving after using cannabis) were obtained at ages 21 and 26 years. The main explanatory measures were collected at ages 15, 18, 21 years and included demographic factors (academic qualifications, employment, parenting); personality measures; mental health measures (substance use, cannabis dependence, alcohol dependence, depression); anti-social behaviour (juvenile arrest, aggressive behaviour, court convictions); early driving behaviour and experiences (car and motorcycle licences, traffic crashes).The analyses were conducted by gender. The results showed that females who persisted in driving after drinking (13%, n=61) were more likely than the others to have a motorcycle licence at 18. The males who persisted in driving after drinking (28%, n=135) were more likely than the other males to have some school academic qualifications and to be employed at age 26. Compared to the other males, those who persisted in unsafe driving after drinking (4%, n=17) were more likely to be aggressive at 18 and alcohol dependent at 21. Only six (1%) females persisted in unsafe driving after drinking so regression analyses were not conducted for this group. For persistent driving after using cannabis, the univariate analyses showed that females who persisted with this behaviour tended to have high substance use at 18, cannabis dependence at 21, police contact as a juvenile, and to be a parent at 21. For this group, because of the small numbers (3%, n=13) multivariate analyses were not appropriate. For the males who persisted in driving after using cannabis (14%, n=68) a wide range of variables were significant at the univariate stage. The multivariate analysis showed that the most important factors were dependence on cannabis at 21, at least one traffic conviction before 21, a non traffic conviction before 18, and low constraint at 18. CONCLUSION: These results show different characteristics were associated with persistence in each of these outcome behaviours. This indicates that different approaches would be required if intervention programmes were to be developed to target these behaviours.     

Assessing the safety effects of multiple roadside treatments using parametric and nonparametric approaches

This study evaluates the safety effectiveness of multiple roadside elements on roadway segments by estimating crash modification factors (CMFs) using the cross-sectional method. To consider the nonlinearity in crash predictors, the study develops generalized nonlinear models (GNMs) and multivariate adaptive regression splines (MARS) models. The MARS is one of the promising data mining techniques due to its ability to consider the interaction impact of more than one variables and nonlinearity of predictors simultaneously. The CMFs were developed for four roadside elements (driveway density, poles density, distance to poles, and distance to trees) and combined safety effects of multiple treatments were interpreted by the interaction terms from the MARS models. Five years of crash data from 2008 to 2012 were collected for rural undivided four-lane roadways in Florida for different crash types and severity levels. The results show that the safety effects decrease as density of driveways and roadside poles increase. The estimated CMFs also indicate that increasing distance to roadside poles and trees reduces crashes. The study demonstrates that the GNMs show slightly better model fitness than negative binomial (NB) models. Moreover, the MARS models outperformed NB and GNM models due to its strength to reflect the nonlinearity of crash predictors and interaction impacts among variables under different ranges. Therefore, it can be recommended that the CMFs are estimated using MARS when there are nonlinear relationships between crash rate and roadway characteristics, and interaction impacts among multiple treatments.