Bayesian estimation of hourly exposure functions by crash type and time of day

The study describes an investigation of the relationship between crash occurrence and hourly volume counts for small samples of highway segments from two states: Michigan and Connecticut. We used a hierarchical Bayesian framework to fit binary regression models for predicting crash occurrence for each of four crash types: (1) single-vehicle, (2) multi-vehicle same direction, (3) multi-vehicle opposite direction, and (4) multi-vehicle intersecting direction, as a function of the hourly volume, segment length, speed limit and pavement width. The results reveal how the relationship between crashes and hourly volume varies by time of day, thus improving the accuracy of crash occurrence predictions. The results show that even accounting for time of day, the disaggregate exposure measure – hourly volume – is indeed non-linear for each of the four crash types. This implies that at any time of day, the crash occurrence is not proportional to the hourly volume. These findings help us to further understand the relationship between crash occurrence and hourly volume, segment length and other risk factors, and facilitate more meaningful comparisons of the safety record of seemingly similar highway locations.     

Explaining two-lane highway crash rates using land use and hourly exposure

This paper describes the estimation of Poisson regression models for predicting both single and multi-vehicle highway crash rates as a function of traffic density and land use, as well as ambient light conditions and time of day. The study focuses on seventeen rural, two-lane highway segments, each one-half mile in length with varying land use patterns and where actual hourly exposure values are available in the form of observed traffic counts. Land-use effects are represented by the number of driveways of various types on each segment. Hourly exposure is represented for single-vehicle crashes as the total vehicle miles traveled and volume/capacity ratio; for multi-vehicle crashes it is the product of the hourly volumes on the main highway and the roads intersecting it along the study segment. For single-vehicle crashes, the following variables were found to be significant, with a positive or negative effect as noted: daytime (06:00–19:00 h, negative effect), the natural log of the segment volume/capacity ratio (negative), percent of the segment with no passing zones (positive), shoulder width (positive), number of intersections (negative), and driveways (mixed effects by type). Good multi-vehicle crash prediction models had quite different variables: daylight conditions from 10:00–15:00 and 15:00–19:00 h (positive), number of intersections (negative), and driveways (positive for all types). The results show that traffic intensity explains differences in crash rates even when controlling for time of day and light conditions, and that these effects are quite different for single and multi-vehicle crashes. Suggestions for future research are also given.     

“Crashing the gates” – selection criteria for television news reporting of traffic crashes

This study investigates which crash characteristics influence the probability that the crash is reported in the television news. To this purpose, all news items from the period 2006–2012 about traffic crashes from the prime time news of two Belgian television channels are linked to the official injury crash database. Logistic regression models are built for the database of all injury crashes and for the subset of fatal crashes to identify crash characteristics that correlate with a lower or higher probability of being reported in the news.     

Effects of work zone presence on injury and non-injury crashes.

Work zones in the United States have approximately 700 traffic-related fatalities, 24,000 injury crashes, and 52,000 non-injury crashes every year. Due to future highway reconstruction needs, work zones are likely to increase in number, duration, and length. This study focuses on analyzing the effect of work zone duration mainly due to its policy-sensitivity. To do so, we created a unique dataset of California freeway work zones that included crash data (crash frequency and injury severity), road inventory data (average daily traffic (ADT) and urban/rural character), and work zone related data (duration, length, and location). Then, we investigated crash rates and crash frequencies in the pre-work zone and during-work zone periods. For the freeway work zones investigated in this study, the total crash rate in the during-work zone period was 21.5% higher (0.79 crashes per million vehicle kilometer (MVKM)) than the pre-work zone period (0.65 crashes per MVKM). Compared with the pre-work zone period, the increase in non-injury and injury crash rates in the during-work zone period was 23.8% and 17.3%, respectively. Next, crash frequencies were investigated using negative binomial models, which showed that frequencies increased with increasing work zone duration, length, and average daily traffic. The important finding is that after controlling for various factors, longer work zone duration significantly increases both injury and non-injury crash frequencies. The implications of the study findings are discussed in the paper.     

Identify sequence of events likely to result in severe crash outcomes

The current practice of crash characterization in highway engineering reduces multiple dimensions of crash contributing factors and their relative sequential connections, crash sequences, into broad definitions, resulting in crash categories such as head-on, sideswipe, rear-end, angle, and fixed-object. As a result, crashes that are classified in the same category may contain many different crash sequences. This makes it difficult to develop effective countermeasures because these crash categorizations are based on the outcomes rather than the preceding events. Consequently, the efficacy of a countermeasure designed for a specific type of crash may not be appropriate due to different pre-crash sequences. This research seeks to explore the use of event sequence to characterize crashes. Additionally, this research seeks to identify crash sequences that are likely to result in severe crash outcomes so that researchers can develop effective countermeasures to reduce severe crashes. This study utilizes the sequence of events from roadway departure crashes in the Fatality Analysis Reporting System (FARS), and converts the information to form a new categorization called “crash sequences.” The similarity distance between each pair of crash sequences were calculated using the Optimal Matching approach. Cluster analysis was applied to group crash sequences that are etiologically similar in terms of the similarity distance. A hybrid model was constructed to mitigate the potential sample selection bias of FARS data, which is biased toward more severe crashes. The major findings include: (1) in terms of a roadway departure crash, the crash sequences that are most likely to result in high crash severity include a vehicle that first crosses the median or centerline, runs-off-road on the left, and then collides with a roadside fixed-object; (2) seat-belt and airbag usage reduces the probability of dying in a roadway departure crash by 90%; and (3) occupants who are seated on the side of the vehicle that experience a direct impact are 2.6 times more likely to die in a roadway departure crash than those not seated on the same side of the vehicle where the impact occurs.     

Model crash frequency at highway–railroad grade crossings using negative binomial regression

Despite the fact that traffic collisions at highway–railroad grade crossings (HRGXs) are rare events, the impact of HRGX crashes is nevertheless more severe than highway crashes. Empirical studies show that traffic collisions at HRGXs are mainly attributed to railway-related and/or highway-related characteristics, particularly drivers’ abnormal behavior, driving around, or through an HRGX. These factors have different effects on crash likelihood (i.e., the number of traffic collisions or crash frequency) at an HRGX. To explore the causal relationship between crash frequency and the factors related to railroad and highway systems, we used a negative binomial regression model to identify the factors that are statistically significantly associated with traffic collisions at HRGXs, and conducted relevant sensitivity analyses to investigate the marginal effect of daily highway traffic on changes in crash frequency. The empirical study shows that the number of daily trains, the number of tracks, highway separation, annual averaged daily traffic (AADT), and crossing length had statistically significant effects on the mean number of traffic collisions (all p-values?≤?0.0487). Further, the marginal effect of the AADT on the change of crash frequency indicates that crash likelihood monotonically increases with the increase of AADT.     

Selecting exposure measures in crash rate prediction for two-lane highway segments

A critical part of any risk assessment is identifying how to represent exposure to the risk involved. Recent research shows that the relationship between crash count and traffic volume is non-linear; consequently, a simple crash rate computed as the ratio of crash count to volume is not proper for comparing the safety of sites with different traffic volumes. To solve this problem, we describe a new approach for relating traffic volume and crash incidence. Specifically, we disaggregate crashes into four types: (1) single-vehicle, (2) multi-vehicle same direction, (3) multi-vehicle opposite direction, and (4) multi-vehicle intersecting, and define candidate exposure measures for each that we hypothesize will be linear with respect to each crash type. This paper describes initial investigation using crash and physical characteristics data for highway segments in Michigan from the Highway Safety Information System (HSIS). We use zero-inflated-Poisson (ZIP) modeling to estimate models for predicting counts for each of the above crash types as a function of the daily volume, segment length, speed limit and roadway width. We found that the relationship between crashes and the daily volume (AADT) is non-linear and varies by crash type, and is significantly different from the relationship between crashes and segment length for all crash types. Our research will provide information to improve accuracy of crash predictions and, thus, facilitate more meaningful comparison of the safety record of seemingly similar highway locations.     

Propensity scores-potential outcomes framework to incorporate severity probabilities in the Highway Safety Manual crash prediction algorithm

Accurate estimation of the expected number of crashes at different severity levels for entities with and without countermeasures plays a vital role in selecting countermeasures in the framework of the safety management process. The current practice is to use the American Association of State Highway and Transportation Officials’ Highway Safety Manual crash prediction algorithms, which combine safety performance functions and crash modification factors, to estimate the effects of safety countermeasures on different highway and street facility types. Many of these crash prediction algorithms are based solely on crash frequency, or assume that severity outcomes are unchanged when planning for, or implementing, safety countermeasures. Failing to account for the uncertainty associated with crash severity outcomes, and assuming crash severity distributions remain unchanged in safety performance evaluations, limits the utility of the Highway Safety Manual crash prediction algorithms in assessing the effect of safety countermeasures on crash severity. This study demonstrates the application of a propensity scores-potential outcomes framework to estimate the probability distribution for the occurrence of different crash severity levels by accounting for the uncertainties associated with them. The probability of fatal and severe injury crash occurrence at lighted and unlighted intersections is estimated in this paper using data from Minnesota. The results show that the expected probability of occurrence of fatal and severe injury crashes at a lighted intersection was 1 in 35 crashes and the estimated risk ratio indicates that the respective probabilities at an unlighted intersection was 1.14 times higher compared to lighted intersections. The results from the potential outcomes-propensity scores framework are compared to results obtained from traditional binary logit models, without application of propensity scores matching. Traditional binary logit analysis suggests that the probability of occurrence of severe injury crashes is higher at lighted intersections compared to unlighted intersections, which contradicts the findings obtained from the propensity scores-potential outcomes framework. This finding underscores the importance of having comparable treated and untreated entities in traffic safety countermeasure evaluations.     

Analysis of crash severities using nested logit model—Accounting for the underreporting of crashes

Recent studies in the area of highway safety have demonstrated the usefulness of logit models for modeling crash injury severities. Use of these models enables one to identify and quantify the effects of factors that contribute to certain levels of severity. Most often, these models are estimated assuming equal probability of the occurrence for each injury severity level in the data. However, traffic crash data are generally characterized by underreporting, especially when crashes result in lower injury severity. Thus, the sample used for an analysis is often outcome-based, which can result in a biased estimation of model parameters. This is more of a problem when a nested logit model specification is used instead of a multinomial logit model and when true shares of the outcomes-injury severity levels in the population are not known (which is almost always the case). This study demonstrates an application of a recently proposed weighted conditional maximum likelihood estimator in tackling the problem of underreporting of crashes when using a nested logit model for crash severity analyses.     

Effect of repeal of the national maximum speed limit law on occurrence of crashes, injury crashes, and fatal crashes on Utah highways

Speed limits were increased in Utah and other States after repeal of the national maximum speed limit law (NMSL) in 1995. This study analyzed effects of the increased speed limit on Utah highways on crash rates, fatality crash rates, and injury crash rates. Annual (1992-1999) rates of crashes, fatality crashes, and injury crashes for the following highway categories were calculated: urban Interstate segments (current speed limit 60-65 miles per hour (mph)); rural Interstate segments (current speed limit 70-75 mph); 55 mph rural non-Interstate highway segments; and high-speed non-Interstate highways (current speed limit 60-65 mph). Data were analyzed using autoregressive integrative moving average intervention time series analysis techniques. There were significant increases in total crash rates on urban (60-65 mph) Interstate segments (confounded by extensive ongoing highway construction on these highways), and in fatal crash rates on high-speed (60-65 mph) rural non-Interstate segments. The following variables were unaffected: total, fatality, and injury crash rates on rural Interstate segments; fatality and injury crash rates on urban Interstate segments; total and injury crash rates on high-speed non-Interstate segments. These results show an adverse effect on crash occurrence for subsets of crash types and highways, but do not show a major overall effect of NMSL repeal and increased speed limit on crash occurrence on Utah highways.     

Risk and type of crash among young drivers by rurality of residence: Findings from the DRIVE Study

Background

Most previous literature on urban/rural differences in road crashes has a primary focus on severe injuries or deaths, which may be largely explained by variations of medical resources. Little has been reported on police-reported crashes by geographical location, or crash type and severity, especially among young drivers.

Methods

DRIVE is a prospective cohort study of 20,822 drivers aged 17-24 in NSW, Australia. Information on risk factors was collected via online questionnaire and subsequently linked to police-reported crashes. Poisson regression was used to analyse risk of various crash types by three levels of rurality of residence: urban, regional (country towns and surrounds) and rural.

Results

Compared to urban drivers, risk of crash decreased with increasing rurality (regional adjusted RR: 0.7, 95% CI 0.6-0.9; rural adjusted RR: 0.5, 95% CI 0.3-0.7). Among those who crashed, risk of injurious crash did not differ by geographic location; however, regional and rural drivers had significantly higher risk of a single versus multiple vehicle crash (regional adjusted RR 1.8, 95% CI 1.3-2.5; rural adjusted RR: 2.0, 95% CI 1.1-3.6), which was explained by speeding involvement and road alignment at the time or site of crash.

Conclusions

Although young urban drivers have a higher crash risk overall, rural and regional residents have increased risk of a single vehicle crash. Interventions to reduce single vehicle crashes should aim to address key issues affecting such crashes, including speeding and specific aspects of road geometry.     

A spatial generalized ordered response model to examine highway crash injury severity

This paper proposes a flexible econometric structure for injury severity analysis at the level of individual crashes that recognizes the ordinal nature of injury severity categories, allows unobserved heterogeneity in the effects of contributing factors, as well as accommodates spatial dependencies in the injury severity levels experienced in crashes that occur close to one another in space. The modeling framework is applied to analyze the injury severity sustained in crashes occurring on highway road segments in Austin, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files from 2009 and includes a variety of crash characteristics, highway design attributes, driver and vehicle characteristics, and environmental factors. The results from our analysis underscore the value of our proposed model for data fit purposes as well as to accurately estimate variable effects. The most important determinants of injury severity on highways, according to our results, are (1) whether any vehicle occupant is ejected, (2) whether collision type is head-on, (3) whether any vehicle involved in the crash overturned, (4) whether any vehicle occupant is unrestrained by a seat-belt, and (5) whether a commercial truck is involved.     

Acute healthcare utilization by children after motor vehicle crashes

This study, describing the overall patterns of acute healthcare resource utilization by child crash victims (age 15 years and younger), was conducted between 28 July 1999 and 30 November 2000 as part of an on-going large-scale, child-specific crash surveillance system, Partners for Child Passenger Safety: insurance claims from 15 states and the District of Columbia function as the source of subjects, with telephone survey and on-site crash investigations serving as the primary sources of data. A probability sample of 4862 eligible crashes with 7368 child occupants formed the study sample. Our results suggest that for every 1000 children involved in crashes, 3 are hospitalized; 108 are treated and released from an emergency department (ED); 48 are evaluated in a physician's office, urgent care center, or other facility; and 841 receive no care at all. Comprehensive surveillance systems for motor vehicle crashes must capture children treated in physicians' offices, emergency departments, and other healthcare facilities in order to provide accurate estimates of the impact on the health care system related to motor vehicle trauma.     

Appropriate analysis of CIREN data: Using NASS-CDS to reduce bias in estimation of injury risk factors in passenger vehicle crashes

The Crash Injury Research Engineering Network (CIREN) database contains detailed medical and crash information on a large number of severely injured occupants in motor vehicle crashes. CIREN's major limitation for stand-alone analyses to explore injury risk factors is that control subjects without a given injury type must have another severe injury to be included in the database. This leads to bias toward the null in the estimation of risk associations. One method to cope with this limitation is to obtain information about occupants without a given injury type from the National Automotive Sampling System's Crashworthiness Data System (NASS-CDS), which is a probability sample of towaway crashes, containing similar crash information, but less medical detail. Combining CIREN and NASS-CDS in this manner takes advantage of the increased sample size when outcomes are available in both datasets; otherwise NASS-CDS can serve as a sample of controls to be combined with CIREN cases, possibly under a sensitivity analysis that includes and excludes NASS-CDS subjects whose status as a control is uncertain. Because CIREN is not a probability sample of crashes that meet its inclusion criteria, we develop a method to estimate the probability of selection for the CIREN cases using data from NASS-CDS. These estimated probabilities are then used to compute “pseudo-weights” for the CIREN cases. These pseudo-weights not only allow for reduced bias in the estimation of risk associations, they allow direct prevalence estimates to be made using medical outcome data available only in CIREN. We illustrate the use of these methods with both simulation studies and application to estimation of prevalence and predictors of AIS 3+ injury risk to head, thorax, and lower extremity regions, as well as prevalence and predictors of acetabular pelvic fractures. Results of these analyses demonstrate combining NASS and CIREN data can yield improvements in mean square error and nominal confidence interval coverage over analyses that use either the NASS-CDS or the CIREN sample alone.     

Landscaping of highway medians and roadway safety at unsignalized intersections

Well-planted and maintained landscaping can help reduce driving stress, provide better visual quality, and decrease over speeding, thus improving roadway safety. Florida Department of Transportation (FDOT) Standard Index (SI-546) is one of the more demanding standards in the U.S. for landscaping design criteria at highway medians near intersections. The purposes of this study were to (1) empirically evaluate the safety results of SI-546 at unsignalized intersections and (2) quantify the impacts of geometrics, traffic, and landscaping design features on total crashes and injury plus fatal crashes. The studied unsignalized intersections were divided into (1) those without median trees near intersections, (2) those with median trees near intersections that were compliant with SI-546, and (3) those with median trees near intersections that were non-compliant with SI-546. A total of 72 intersections were selected, for which five-year crash data from 2006–2010 were collected.The sites that were compliant with SI-546 showed the best safety performance in terms of the lowest crash counts and crash rates. Four crash predictive models—two for total crashes and two for injury crashes—were developed. The results indicated that improperly planted and maintained median trees near highway intersections can increase the total number of crashes and injury plus fatal crashes at a 90% confidence level; no significant difference could be found in crash rates between sites that were compliant with SI-546 and sites without trees. All other conditions remaining the same, an intersection with trees that was not compliant with SI-546 had 63% more crashes and almost doubled injury plus fatal crashes than those at intersections without trees. The study indicates that appropriate landscaping in highway medians near intersections can be an engineering technology that not only improves roadway environmental quality but also maintains intersection safety.     

Chain Sampling Scheme under Constant Inspection Errors

The primary aim of this paper is to extend the inspection error consideration to chain sampling schemes, an area that has not been dealt with in the literature. A mathematical model is developed to investigate the performance of chain sampling schemes under constant inspection errors. Expressions of performance measures, such as operating characteristic function, average total inspection and average outgoing quality, are derived to aid the analysis of a general chain sampling scheme, ChSP‐4A ( ) r, developed by Frishman. This study reveals that as Type I inspection error increases the probability of acceptance will decrease and as Type II inspection error increases the acceptance probability will increase. The effect of Type II error on the probability of acceptance is very marginal compared with that of Type I error, especially when the true fraction non‐conforming is small. In addition, the effects of inspection errors can be ‘eliminated’ by transforming to its equivalent perfect inspection counterpart, hence greatly reducing the complexity of the analysis. The effects of other sampling parameters are also studied to serve as a foundation for future plan designing purposes. Copyright © 2006 John Wiley & Sons, Ltd.     

A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods

Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models crash counts by injury severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses several questions that are difficult to answer when estimating crash counts separately. Thanks to recent advances in crash modeling and Bayesian statistics, parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis–Hastings (M–H) algorithms for crashes on Washington State rural two-lane highways. Estimation results from the MVPLN approach show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggest overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves.     

Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention.     

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.