Property Damage Only Research Articles

A full Bayes multivariate intervention model with random parameters among matched pairs for before–after safety evaluation

The objective of this study is to evaluate the safety performance of a sample of intersections that have been improved with the implementation of certain safety countermeasures in the Greater Vancouver area. A full Bayes approach is utilized to determine the effectiveness of the improvements using a before–after design with matched (yoked) comparison groups. A multivariate Poisson-lognormal intervention model is used for the analysis of crash counts by severity levels. The model is extended to incorporate random parameters to account for the correlation between sites within comparison-treatment pairs. The full Bayes analysis revealed that incorporating such design features as matched comparison groups in the specification of safety performance functions can significantly improve the fit, while reducing the estimates of the extra-Poisson variation. As well, such extended models can be used to account for heterogeneity due to unobserved road geometrics, traffic characteristics, environmental factors and driver behavior. The results showed that the overall odds ratios for injuries and fatalities ( I + F) and property damage only ( PDO) imply significant reductions in predicted crash counts of 23% and 15%, respectively. The corresponding credible intervals were (12%, 33%) and (6%, 24%) at the 0.95 confidence level. The majority of the site-level odds ratio exhibited reductions in both I + F and PDO predicted crash counts. However, only some of these reductions were significant. As well, the effectiveness of the treatment seems to vary by severity level from one location to another. For I + F, the crash reduction factors were 29%, 15% and 21% for improving signal visibility, left turn phase improvement and left turn lane installation, respectively. The corresponding crash reduction factors for PDO were 21%, 4% and 20%, respectively.

The effectiveness and cost-savings of first eye cataract surgery in reducing crash risk: a population based study

The present study examined the effectiveness of cataract surgery in terms of crash risk and associated costs on drivers aged 60 and older at a population level. A retrospective before...

Collision prediction models using multivariate Poisson-lognormal regression

This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only ( PDO) and injuries plus fatalities ( I + F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I + F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models.

Validity of surrogate measures of alcohol involvement when applied to nonfatal crashes

Since the 1970s, nighttime fatal crashes have been used as a surrogate measure for alcohol-related fatalities for crashes for which more direct measures were absent. The validity of this approach was confirmed in 1985 but has not been re-evaluated since. Although this measure has also been applied to identify alcohol involvement in nonfatal crashes, its validity when applied to nonfatal cases has never been determined. The objective of this study was to evaluate the appropriateness of using nighttime crashes as surrogate measures for alcohol impairment when applied to fatal and nonfatal injury and property damage only (PDO) crashes. To do so, we used data from a crash–control design study collected at the roadside in two U.S. states between 1997 and 1999, as well as from the 1997–1999 and 2004–2006 Fatality Analysis Reporting System. The outcome of this study confirms the validity of using nighttime crashes as a surrogate measure for alcohol-related fatalities and supports the use of after-midnight crashes for measuring alcohol involvement in nonfatal and PDO crashes when the number of late-night crashes permits.

Spatial Autocorrelation and Bayesian Spatial Statistical Method for Analyzing Intersections Prone to Injury Crashes

Identification of hazardous road locations (i.e., crash hot spots or black spots) is a standard practice in departments of transportation (DOTs) throughout the United States. Often, DOTs use relatively straightforward methods to detect high crash sites; these methods, however, may not be accurate. The objectives of this study are to develop two different methodologies: ( a) a geographic information system (GIS)-based method to detect crash hot spots and ( b) a spatial regression method with structured and unstructured random effects in continuous space to investigate the intersection-level factors that influence the concentration of fatal injury crashes. Using injury and property damage only (PDO) crash data from Tucson, Arizona, this study shows that spatial dependence plays a strong role during the analyses of road-traffic crashes. These spatial dependencies, accounted through spatial autocorrelation, help to detect statistically significant clusters of crashes involving fatalities or severe or minor injuries, in a GIS framework. This study also develops statistical models of severe- and minor-injury crashes, using classical negative binomial (NB) and Bayesian spatial statistical methods incorporating spatially structured and unstructured random effects. The coefficient estimates from a Bayesian framework are similar to those from NB estimation, but with better precision. The model that includes spatial correlations also indicates the potential to reduce the bias associated with model mis-specification by changing the estimate of the annual average daily traffic coefficients. This study's results also indicate that spatially structured correlation is quite significant in cases of crashes involving minor injury or PDO and that unstructured effects are somewhat significant at the intersection-level for cases of severe-injury crashes.

Evaluating Impact on Safety of Improved Signal Visibility at Urban Signalized Intersections

A study evaluated the safety impacts associated with improved signal visibility at urban signalized intersections. The improvements included one or a combination of the following upgrades: signal lens size, new backboards, reflective tapes added to existing backboards, and additional signal heads. Intersection collision data based on insurance claim records from the Insurance Corporation of British Columbia were used in the study to evaluate the effectiveness of the signal visibility improvement. These automobile insurance claim data are current, comprehensive, and considered quite reliable for intersection locations. Traffic volume and collision data were collected for treatment and comparison groups. The treatment group included 139 intersections and the comparison group included 85 intersections. The data for the comparison group were used to account for history and maturation confounding factors. An empirical Bayes analysis was used to ensure that the evaluation results were reliable and to account for the regression-to-the-mean confounding factor. The analysis was undertaken for both severe (injury + fatal) and property-damage-only (PDO) collisions and also for daytime and nighttime collisions. The evaluation results indicate statistically significant reductions of 8.5%, 5.9%, 6.6%, and 7.3% for PDO, daytime, nighttime, and total collisions, respectively. Severe collisions showed a nonsignificant reduction of 2.6%.

Analysis of traffic accidents at urban intersections in Riyadh

Previous studies have shown that intersection-related accidents account for about 50% of all accidents registered annually in Riyadh, the capital of the Kingdom of Saudi Arabia (KSA). More than half of these accidents are classified as severe. In this study, an attempt was made to investigate traffic accidents that occurred at both intersections and non-intersection sites. The goal was to analyze the nature of such accidents to determine their characteristics so that remedies could be sought or at least future research could be suggested. For this purpose, a sample of 1774 reported accidents was collected in a systematic random manner for the period 1996–1998 (651 severe accidents (accidents resulting in at least one personal injury or fatality) and 1123 property-damage-only (PDO) accidents). Conditional probability and contingency table analyses were used to make inferences from the data. The study found that improper driving behavior is the primary cause of accidents at signalized urban intersections in Riyadh; running a red light and failing to yield are the primary contributing causes. The analysis indicates that there is an urgent need to review existing intersection geometry along with the traffic control devices installed at these sites. In addition, public education campaigns and law enforcement strategies are urgently needed.

Testing for Instrumentation in Transportation Time Series Data: A Case Study

Instrumentation is one of the threats to the validity of experiments. Four possible cases of instrumentation in a time series of traffic accident statistics in Illinois since the mid-1970s were tested, primarily by using autoregressive integrated moving average methods. Two of these cases, a 1977 change in the reporting threshold for property-damage-only (PDO) accidents and a 1989 change in the definition of a fatality, were not found to be significant. A 1989 change in the method of tabulating monthly data and a 1992 change in the reporting threshold for PDO accidents were statistically significant. These two cases combined could account for a more than 15 percent decline in PDO accidents.

Harmful events in crashes∗

This study examined objects struck in motor vehicle crashes which cause injury or property damage—called harmful events on national accident data files. The percentage of loss due to fatalities was found to differ greatly by harmful event type. Comprehensive costs were used to combine data on fatalities, injuries, and property damage only (PDO) vehicles into a single economic measure of crash loss. Use of comprehensive costs can reduce distortions that may occur in analyses limited to fatal events. Large differences were identified in average injury costs by harmful event type, indicating the need to use specific harmful event injury costs in analyses whenever possible. Major differences found between first and most harmful events in ran-off-road crashes indicate a need for states to collect both data elements on their police accident report forms.

A Statistical Comparison Between Severe Accidents And PDO Accidents In Riyadh

Riyadh, capital of the Kingdom of Saudi Arabia, is a fast developing and modernized city. With a population of three million inhabitants and a motorized rate (population per vehicle) of 3.8, Riyadh has been experiencing increasing traffic accident problems. Official statistics have shown that more than 400 fatalities and 3000 injuries occur annually due to traffic accidents. The severity index of traffic accidents over the past four years is higher than the corresponding nationwide index. This study aims at investigating the severity of traffic accidents in Riyadh. An attempt was made to compare property damage only (PDO) accidents with severe accidents in order to analyze the nature of severe accidents and determine their characteristics so that some remedies can be recommended. For achieving this purpose, a total of 1774 police-reported accidents was collected in a systematic random manner for the period of 1996 through 1997. Analysis using various test statistics was conducted to compare the two categories of accidents. The study found that improper driving behavior is the primary cause of accidents in Riyadh and the young drivers are most often the victims in such accidents. In addition, the taxis and pick-up trucks are in high risk of being involved in accidents. The analysis indicates that there is an urgent need for reviewing existing public awareness programs and law enforcement strategies.