The roles of exposure and speed in road safety analysis

Speed is a determining factor in road safety analysis. It is generally believed that an increase in speed harms road safety. However, it can also be argued that driving at high speed reduces the length of time exposure and thus the likelihood of a crash. It is therefore critical to clarify the roles that exposure and speed play in road safety analysis. This study evaluates the relationship between speed and crash risk with respect to distance and time exposure, using disaggregated crash and speed data collected from 112 road segments in Hong Kong. A joint probability model based on a full Bayesian method is applied simultaneously to model crash occurrence and crash severity. In addition, we consider the explanatory variables, including road design, weather conditions, and temporal distribution, in the proposed crash prediction model. The results indicate that average speed plays a significant role in crash risk, despite opposing correlations with respect to distance and time exposure; the correlation between speed and crash risk is positive when distance exposure is considered, but negative when time exposure is used. However, in both cases, speed is positively associated with the injury severity.     

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.     

An approach to accidents modeling based on compounds road environments

The most common approach to study the influence of certain road features on accidents has been the consideration of uniform road segments characterized by a unique feature. However, when an accident is related to the road infrastructure, its cause is usually not a single characteristic but rather a complex combination of several characteristics. The main objective of this paper is to describe a methodology developed in order to consider the road as a complete environment by using compound road environments, overcoming the limitations inherented in considering only uniform road segments. The methodology consists of: dividing a sample of roads into segments; grouping them into quite homogeneous road environments using cluster analysis; and identifying the influence of skid resistance and texture depth on road accidents in each environment by using generalized linear models. The application of this methodology is demonstrated for eight roads. Based on real data from accidents and road characteristics, three compound road environments were established where the pavement surface properties significantly influence the occurrence of accidents. Results have showed clearly that road environments where braking maneuvers are more common or those with small radii of curvature and high speeds require higher skid resistance and texture depth as an important contribution to the accident prevention.     

A fully Bayesian before–after analysis of permeable friction course (PFC) pavement wet weather safety

Permeable friction course (PFC), a porous hot-mix asphalt, is typically applied to improve wet weather safety on high-speed roadways in Texas. In order to warrant expensive PFC construction, a statistical evaluation of its safety benefits is essential. Generally, the literature on the effectiveness of porous mixes in reducing wet-weather crashes is limited and often inconclusive. In this study, the safety effectiveness of PFC was evaluated using a fully Bayesian before–after safety analysis. First, two groups of road segments overlaid with PFC and non-PFC material were identified across Texas; the non-PFC or reference road segments selected were similar to their PFC counterparts in terms of site specific features. Second, a negative binomial data generating process was assumed to model the underlying distribution of crash counts of PFC and reference road segments to perform Bayesian inference on the safety effectiveness. A data-augmentation based computationally efficient algorithm was employed for a fully Bayesian estimation. The statistical analysis shows that PFC is not effective in reducing wet weather crashes. It should be noted that the findings of this study are in agreement with the existing literature, although these studies were not based on a fully Bayesian statistical analysis. Our study suggests that the safety effectiveness of PFC road surfaces, or any other safety infrastructure, largely relies on its interrelationship with the road user. The results suggest that the safety infrastructure must be properly used to reap the benefits of the substantial investments.     

Use of speed profile as surrogate measure: Effect of traffic calming devices on crosstown road safety performance

Urban road safety management is usually characterized by the lack of sufficient, good quality crash data and low budgets to obtain it even though many traffic accidents occur there. For example, 54 percent of road crashes in Spain take place in urban areas, and 10 percent of urban fatal crashes occur on crosstown roads, which are rural roads that traverse small communities. Traffic calming measures (TCMs) are often implemented on these parts of rural roads that traverse small communities in order to reduce both the frequency and severity of crashes by lowering speeds, but evaluation of their effectiveness has been limited. The objective of this study was to develop a methodology using continuous speed profiles to evaluate the safety effectiveness of TCMs on crosstown roads as part of an integrated system in the absence of historical data. Given the strong relationship between speed and crash experience, safety performance can be related to speed. Consequently, speed can be used indirectly as a surrogate safety measure in the absence of crash and speed data.     

Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway

While rural freeways generally have lower crash rates, interactions between driver behavior, traffic and geometric characteristics, and adverse weather conditions may increase the crash risk along some freeway sections. This paper examines the safety effects of roadway geometrics on crash occurrence along a freeway section that features mountainous terrain and adverse weather. Starting from preliminary exploration using Poisson models, Bayesian hierarchical models with spatial and random effects were developed to efficiently model the crash frequencies on road segments on the 20-mile freeway section of study. Crash data for 6 years (2000–2005), roadway geometry, traffic characteristics and weather information in addition to the effect of steep slopes and adverse weather of snow and dry seasons, were used in the investigation. Estimation of the model coefficients indicates that roadway geometry is significantly associated with crash risk; segments with steep downgrades were found to drastically increase the crash risk. Moreover, this crash risk could be significantly increased during snow season compared to dry season as a confounding effect between grades and pavement condition. Moreover, sites with higher degree of curvature, wider medians and an increase of the number of lanes appear to be associated with lower crash rate. Finally, a Bayesian ranking technique was implemented to rank the hazard levels of the roadway segments; the results confirmed that segments with steep downgrades are more crash prone along the study section.     

Impact direction effect on serious-to-fatal injuries among drivers in near-side collisions according to impact location: Focus on thoracic injuries

Occupant injury in real world vehicle accidents can be significantly affected by a set of crash characteristics, of which impact direction and impact location (or damage location) in general scale interval (e.g., frontal impact is frequently defined as general damage to vehicle frontal end with impact angle range of 11–1 o’clock) have been identified to associate with injury outcome. The effects of crash configuration in more specific scale of interval on the injury characteristics have not been adequately investigated. This paper presents a statistical analysis to investigate the combined effects of specific impact directions and impact locations on the serious-to-fatal injuries of driver occupants involved in near-side collisions using crash data from National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) for the calendar years of 1995–2005.     

Computational and experimental crash analysis of the road safety barrier

The paper describes the computational analysis and experimental crash tests of a new road safety barrier. The purpose of this research was to develop and evaluate a full-scale computational model of the road safety barrier for use in crash simulations and to further compare the computational results with real crash test data. The impact severity and stiffness of the new design have been evaluated with the dynamic nonlinear elasto-plastic analysis of the three-dimensional road safety barrier within the framework of the finite element method with LS-DYNA code. Comparison of computational and experimental results proved the correctness of the computational model. The tests have also shown that the new safety barrier assures controllable crash energy absorption which in turn increases the safety of vehicle occupants.     

Motor vehicle-bicycle crashes in Beijing: irregular maneuvers, crash patterns, and injury severity

This research presents a comprehensive analysis of motor vehicle–bicycle crashes using 4 years of reported crash data (2004–2007) in Beijing. The interrelationship of irregular maneuvers, crash patterns and bicyclist injury severity are investigated by controlling for a variety of risk factors related to bicyclist demographics, roadway geometric design, road environment, etc.Results show that different irregular maneuvers are correlated with a number of risk factors at different roadway locations such as the bicyclist age and gender, weather and traffic condition. Furthermore, angle collisions are the leading pattern of motor vehicle–bicycle crashes, and different irregular maneuvers may lead to some specific crash patterns such as head-on or rear-end crashes. Orthokinetic scrape is more likely to result in running over bicyclists, which may lead to more severe injury. Moreover, bicyclist injury severity level could be elevated by specific crash patterns and risk factors including head-on and angle collisions, occurrence of running over bicyclists, night without streetlight, roads without median/division, higher speed limit, heavy vehicle involvement and older bicyclists.This study suggests installation of median, division between roadway and bikeway, and improvement of illumination on road segments. Reduced speed limit is also recommended at roadway locations with high bicycle traffic volume. Furthermore, it may be necessary to develop safety campaigns aimed at male, teenage and older bicyclists.     

Influence of pavement condition on horizontal curve safety

Crash statistics suggest that horizontal curves are the most vulnerable sites for crash occurrence. These crashes are often severe and many involve at least some level of injury due to the nature of the collisions. Ensuring the desired pavement surface condition is one potentially effective strategy to reduce the occurrence of severe accidents on horizontal curves. This study sought to develop crash injury severity models by integrating crash and pavement surface condition databases. It focuses on developing a causal relationship between pavement condition indices and severity level of crashes occurring on two-lane horizontal curves in Texas. In addition, it examines the suitability of the existing Skid Index for safety maintenance of two-lane curves. Significant correlation is evident between pavement condition and crash injury severity on two-lane undivided horizontal curves in Texas. Probability of a crash becoming fatal is appreciably sensitive to certain pavement indices. Data suggested that road facilities providing a smoother and more comfortable ride are vulnerable to severe crashes on horizontal curves. In addition, the study found that longitudinal skid measurement barely correlates with injury severity of crashes occurring on curved portions. The study recommends exploring the option of incorporating lateral friction measurement into Pavement Management System (PMS) databases specifically at curved road segments.     

Bayesian spatial joint modeling of traffic crashes on an urban road network

This study proposes a Bayesian spatial joint model of crash prediction including both road segments and intersections located in an urban road network, through which the spatial correlations between heterogeneous types of entities could be considered. A road network in Hillsborough, Florida, with crash, road, and traffic characteristics data for a three-year period was selected in order to compare the proposed joint model with three site-level crash prediction models, that is, the Poisson, negative binomial (NB), and conditional autoregressive (CAR) models. According to the results, the CAR and Joint models outperform the Poisson and NB models in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-entity spatial correlations. Although the goodness-of-fit and predictive performance of the CAR and Joint models are equivalent in this case study, spatial correlations between segments and the connected intersections are found to be more significant than those solely between segments or between intersections, which supports the employment of the Joint model as an alternative in road-network-level safety modeling.     

Use of age–period–cohort models to estimate effects of vehicle age,year of crash and year of vehicle manufacture on driver injury and fatality rates in single vehicle crashes in New South Wales, 2003–2010

A novel application of age–period–cohort methods are used to explain changes in vehicle based crash rates in New South Wales, Australia over the period 2003–2010. Models are developed using vehicle age, crash period and vehicle cohort to explain changes in the rate of single vehicle driver fatalities and injuries in vehicles less than 13 years of age. Large declines in risk are associated with vehicle cohorts built after about 1996. The decline in risk appears to have accelerated to 12 percent per vehicle cohort year for cohorts since 2004. Within each cohort, the risk of crashing appears to be a minimum at two years of age and increases as the vehicle ages beyond this. Period effects (i.e., other road safety measures) between 2003 and 2010 appear to have contributed to declines of up to about two percent per annum to the driver-fatality single vehicle crash rate, and possibly only negligible improvements to the driver-injury single vehicle crash rate. Vehicle improvements appear to have been responsible for a decline in per-vehicle crash risk of at least three percent per calendar year for both severity levels over the same period. Given the decline in risk associated with more recent vehicle cohorts and the dynamics of fleet turnover, continued declines in per-vehicle crash risk over coming years are almost certain.     

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

Hot spot identification (HSID) aims to identify potential sites—roadway segments, intersections, crosswalks, interchanges, ramps, etc.—with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset.     

An empirical analysis of farm vehicle crash injury severities on Iowa's public road system

Farm vehicle crashes are a major safety concern for farmers as well as all other users of the public road system in agricultural states. Using data on farm vehicle crashes that occurred on Iowa's public roads between 2004 and 2006, we estimate a multinomial logit model to identify crash-, farm vehicle-, and driver-specific factors that determine farm vehicle crash injury severity outcomes. Estimation findings indicate that there are crash patterns (rear-end manner of collision; single-vehicle crash; farm vehicle crossed the centerline or median) and conditions (obstructed vision and crash in rural area; dry road, dark lighting, speed limit 55 mph or higher, and harvesting season), as well as farm vehicle and driver-contributing characteristics (old farm vehicle, young farm vehicle driver), where targeted intervention can help reduce the severity of crash outcomes. Determining these contributing factors and their effect is the first step to identifying countermeasures and safety strategies in a bid to improve transportation safety for all users on the public road system in Iowa as well as other agricultural states.     

Road network safety evaluation using Bayesian hierarchical joint model

Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model.A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well.     

Traffic conflicts on bicycle paths: A systematic observation of behaviour from video

In The Netherlands, on bicycle paths, single-bicycle accidents, bicycle–bicycle and bicycle–moped accidents constitute a considerable share of all bicyclist injuries. Over three quarters of all hospitalised bicyclist victims in the Netherlands cannot be directly related to a crash with motorised traffic. As the usage of bicycle paths steadily increases, it is to be expected that safety on bicycle paths will become a major issue in the coming years in The Netherlands.     

Road safety risk evaluation and target setting using data envelopment analysis and its extensions

Currently, comparison between countries in terms of their road safety performance is widely conducted in order to better understand one's own safety situation and to learn from those best-performing countries by indicating practical targets and formulating action programmes. In this respect, crash data such as the number of road fatalities and casualties are mostly investigated. However, the absolute numbers are not directly comparable between countries. Therefore, the concept of risk, which is defined as the ratio of road safety outcomes and some measure of exposure (e.g., the population size, the number of registered vehicles, or distance travelled), is often used in the context of benchmarking. Nevertheless, these risk indicators are not consistent in most cases. In other words, countries may have different evaluation results or ranking positions using different exposure information. In this study, data envelopment analysis (DEA) as a performance measurement technique is investigated to provide an overall perspective on a country's road safety situation, and further assess whether the road safety outcomes registered in a country correspond to the numbers that can be expected based on the level of exposure. In doing so, three model extensions are considered, which are the DEA based road safety model (DEA-RS), the cross-efficiency method, and the categorical DEA model. Using the measures of exposure to risk as the model's input and the number of road fatalities as output, an overall road safety efficiency score is computed for the 27 European Union (EU) countries based on the DEA-RS model, and the ranking of countries in accordance with their cross-efficiency scores is evaluated. Furthermore, after applying clustering analysis to group countries with inherent similarity in their practices, the categorical DEA-RS model is adopted to identify best-performing and underperforming countries in each cluster, as well as the reference sets or benchmarks for those underperforming ones. More importantly, the extent to which each reference set could be learned from is specified, and practical yet challenging targets are given for each underperforming country, which enables policymakers to recognize the gap with those best-performing countries and further develop their own road safety policy.     

The role of the built environment in explaining relationships between perceived and actual pedestrian and bicyclist safety

While the conventional approach to safety planning has emphasized crash analysis with police-reported crash information, transportation professionals increasingly recognize the importance of proactively identifying potential crash risk and considering environmental characteristics. In a proactive approach, individuals’ perception of crash risk provides important information in identifying potential crash risk. As built environment characteristics influence the levels of pedestrian and bicycle safety, this study examined how perceived and actual crash risk are related with each other and with respect to built environmental characteristics. Our results showed that residents who live in low density-single residential neighborhoods are more likely to perceive their neighborhood as dangerous relative to residents of compact, mixed-use neighborhoods even though the latter exhibited higher actual crash rates. The results of path analyses confirmed that a simultaneous but opposite relationship exists between perceived and actual crash risks. Our results indicate that higher actual crash risk increases perceived crash risk, while higher perceived crash risk is negatively associated to actual crash rates. Consequently, low density and non-mixed land uses increase individuals perception of crash risk, and increased perception of risk and unfriendly environment for pedestrian and bikers reduces actual crash rates as a result of behavioral changes. From a policy standpoint, more attention and proactive interventions are desirable in suburban areas beyond the areas with high crash rates, as some of these areas have high-perceived risks.     

Controlling factors of the parental safety perception on children's travel mode choice

The travel mode of children changed significantly over the last 20 years, with a decrease of children travelling as pedestrians or cyclists. This study focuses on six to twelve year old children. Parents determine to a large extent the mode choice of children in this age category. Based on the analysis of an extensive survey, the research shows that traffic infrastructure has a significant impact on parental decision making concerning children's travel mode choice, by affecting both the real and the perceived traffic safety. Real traffic safety is quantified in terms of numbers of accidents and road infrastructure. For the perceived traffic safety a parental allowance probability is calculated per road type to show that infrastructure characteristics influence parental decision making on the children's mode choice. A binary logistic model shows that this allowance is determined by age, gender and traffic infrastructure near the child's home or near destinations frequently visited by children. Since both real and perceived traffic safety are influenced by infrastructure characteristics, a spatial analysis of parental perception and accident statistics can be used to indicate the locations where infrastructure improvements will be most effective to increase the number of children travelling – safely – as pedestrians or cyclists.     

Comparison of empirical Bayes and full Bayes approaches for before-after road safety evaluations

The empirical Bayes (EB) approach has now gained wide acceptance among researchers as the much preferred one for the before-after evaluation of road safety treatments. In this approach, the before period crash experience at treated sites is used in conjunction with a crash prediction model for untreated reference sites to estimate the expected number of crashes that would have occurred without treatment. This estimate is compared to the count of crashes observed after treatment to evaluate the effect of the treatment. This procedure accounts for regression-to-the-mean effects that result from the natural tendency to select for treatment those sites with high observed crash frequencies. Of late, a fully Bayesian (FB) approach has been suggested as a useful, though complex alternative to the empirical Bayes approach in that it is believed to require less data for untreated reference sites, it better accounts for uncertainty in data used, and it provides more detailed causal inferences and more flexibility in selecting crash count distributions. This paper adds to the literature on comparing the two Bayesian approaches through empirical applications. The main application is an evaluation of the conversion of road segments from a four-lane to a three-lane cross-section with two-way left-turn lanes (also known as road diets). For completeness, the paper also summarizes the results of an earlier application pertaining to the evaluation of conversion of rural intersections from unsignalized to signalized control. For both applications, the estimated safety effects from the two approaches are comparable.