Evaluation and enhancement of accident prediction models and accident modification factors of rural intersections

Ph.D.Simon Washingto

Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: Example applied to at grade railroad crossings in Korea

This paper describes the formalization and application of a methodology to evaluate the safety benefit of countermeasures in the face of uncertainty. To illustrate the methodology, 18 countermeasures for improving safety of at grade railroad crossings (AGRXs) in the Republic of Korea are considered. Akin to “stated preference” methods in travel survey research, the methodology applies random selection and laws of large numbers to derive accident modification factor (AMF) densities from expert opinions. In a full Bayesian analysis framework, the collective opinions in the form of AMF densities (data likelihood) are combined with prior knowledge (AMF density priors) for the 18 countermeasures to obtain ‘best’ estimates of AMFs (AMF posterior credible intervals). The countermeasures are then compared and recommended based on the largest safety returns with minimum risk (uncertainty). To the author's knowledge the complete methodology is new and has not previously been applied or reported in the literature. The results demonstrate that the methodology is able to discern anticipated safety benefit differences across candidate countermeasures. For the 18 at grade railroad crossings considered in this analysis, it was found that the top three performing countermeasures for reducing crashes are in-vehicle warning systems, obstacle detection systems, and constant warning time systems

Accident prediction model for railway-highway interfaces

Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes. Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes

Expected safety performance of rural signalized intersections in South Korea

Understanding the expected safety performance of rural signalized intersections is critical for (a) identifying high-risk sites where the observed safety performance is substantially worse than the expected safety performance, (b) understanding influential factors associated with crashes, and (c) predicting the future performance of sites and helping plan safety-enhancing activities. These three critical activities are routinely conducted for safety management and planning purposes in jurisdictions throughout the United States and around the world. This paper aims to develop baseline expected safety performance functions of rural signalized intersections in South Korea, which to date have not yet been established or reported in the literature. Data are examined from numerous locations within South Korea for both three-legged and four-legged configurations. The safety effects of a host of operational and geometric variables on the safety performance of these sites are also examined. In addition, supplementary tables and graphs are developed for comparing the baseline safety performance of sites with various geometric and operational features. These graphs identify how various factors are associated with safety. The expected safety prediction tables offer advantages over regression prediction equations by allowing the safety manager to isolate specific features of the intersections and examine their impact on expected safety. The examination of the expected safety performance tables through illustrated examples highlights the need to correct for regression-to-the-mean effects, emphasizes the negative impacts of multicollinearity, shows why multivariate models do not translate well to accident modification factors, and illuminates the need to examine road safety carefully and methodically. Caveats are provided on the use of the safety performance prediction graphs developed in this paper

Development of accident prediction models for rural highway intersections

A study was done to develop macrolevel crash prediction models that can be used to understand and identify effective countermeasures for improving signalized highway intersections and multilane stop-controlled highway intersections in rural areas. Poisson and negative binomial regression models were fit to intersection crash data from Georgia, California, and Michigan. To assess the suitability of the models, several goodness-of-fit measures were computed. The statistical models were then used to shed light on the relationships between crash occurrence and traffic and geometric features of the rural signalized intersections. The results revealed that traffic flow variables significantly affected the overall safety performance of the intersections regardless of intersection type and that the geometric features of intersections varied across intersection type and also influenced crash type

Property Damage Crash Equivalency Factors for Solving the Crash Frequency-Severity Dilemma: Case Study on South Korean Rural Roads

Safety interventions (e.g. median barriers, photo enforcement) and road features (e.g. median type and width) can influence crash severities, crash frequencies, or both. Both dimensions—crash frequency and crash severity—are needed to obtain a full accounting of road safety. Extensive literature and common sense both dictate that all crashes are not ‘created’ equal—with fatalities costing society more than 1000 times the cost of property damage only crashes. Despite this glowing disparity, the profession has not unanimously embraced or successfully defended a non-arbitrary severity weighting approach for analyzing safety data and conducting safety analyses.This paper argues that both the crash frequency and severity dimensions are made available by intelligently and reliably weighting crash frequencies and converting all crashes to property damage only crash equivalents (PDOEs) using comprehensive societal unit crash injury costs. This approach is analogous to calculating axle load equivalents when predicting pavement damage—for instance a 40,000 lb truck causes 4025 times more stress than does a 4000 lb car and so simply counting axles is not sufficient. Calculating PDOEs using unit crash costs is the most defensible and non-arbitrary weighting scheme, allows for the simple incorporation of severity and frequency, and leads to crash models that are sensitive to factors that affect crash severity. Moreover, we show that using PDOEs diminishes the errors introduced by under-reporting of less severe crashes—an added benefit of the PDO equivalent analysis approach. The method is illustrated using rural road segment data from South Korea (which in practice should apply Korean crash cost data). Finally, the results and remaining issues with PDOEs and their adoption in safety analysis are discussed

Property Damage Crash Equivalency Factors for Solving the Crash Frequency-Severity Dilemma: Case Study on South Korean Rural Roads

Safety interventions (e.g. median barriers, photo enforcement) and road features (e.g. median type and width) can influence crash severities, crash frequencies, or both. Both dimensions—crash frequency and crash severity—are needed to obtain a full accounting of road safety. Extensive literature and common sense both dictate that all crashes are not ‘created’ equal—with fatalities costing society more than 1000 times the cost of property damage only crashes. Despite this glowing disparity, the profession has not unanimously embraced or successfully defended a non-arbitrary severity weighting approach for analyzing safety data and conducting safety analyses.This paper argues that both the crash frequency and severity dimensions are made available by intelligently and reliably weighting crash frequencies and converting all crashes to property damage only crash equivalents (PDOEs) using comprehensive societal unit crash injury costs. This approach is analogous to calculating axle load equivalents when predicting pavement damage—for instance a 40,000 lb truck causes 4025 times more stress than does a 4000 lb car and so simply counting axles is not sufficient. Calculating PDOEs using unit crash costs is the most defensible and non-arbitrary weighting scheme, allows for the simple incorporation of severity and frequency, and leads to crash models that are sensitive to factors that affect crash severity. Moreover, we show that using PDOEs diminishes the errors introduced by under-reporting of less severe crashes—an added benefit of the PDO equivalent analysis approach. The method is illustrated using rural road segment data from South Korea (which in practice should apply Korean crash cost data). Finally, the results and remaining issues with PDOEs and their adoption in safety analysis are discussed

Modeling crash types: New insights into the effects of covariates on crashes at rural intersections

Many studies focused on the development of crash prediction models have resulted in aggregate crash prediction models to quantify the safety effects of geometric, traffic, and environmental factors on the expected number of total, fatal, injury, and/or property damage crashes at specific locations. Crash prediction models focused on predicting different crash types, however, have rarely been developed. Crash type models are useful for at least three reasons. The first is motivated by the need to identify sites that are high risk with respect to specific crash types but that may not be revealed through crash totals. Second, countermeasures are likely to affect only a subset of all crashes—usually called target crashes—and so examination of crash types will lead to improved ability to identify effective countermeasures. Finally, there is a priori reason to believe that different crash types (e.g., rear-end, angle, etc.) are associated with road geometry, the environment, and traffic variables in different ways and as a result justify the estimation of individual predictive models. The objectives of this paper are to (1) demonstrate that different crash types are associated to predictor variables in different ways (as theorized) and (2) show that estimation of crash type models may lead to greater insights regarding crash occurrence and countermeasure effectiveness. This paper first describes the estimation results of crash prediction models for angle, head-on, rear-end, sideswipe (same direction and opposite direction), and pedestrian-involved crash types. Serving as a basis for comparison, a crash prediction model is estimated for total crashes. Based on 837 motor vehicle crashes collected on two-lane rural intersections in the state of Georgia, six prediction models are estimated resulting in two Poisson (P) models and four NB (NB) models. The analysis reveals that factors such as the annual average daily traffic, the presence of turning lanes, and the number of driveways have a positive association with each type of crash, whereas median widths and the presence of lighting are negatively associated. For the best fitting models covariates are related to crash types in different ways, suggesting that crash types are associated with different precrash conditions and that modeling total crash frequency may not be helpful for identifying specific countermeasures

Development of accident modification factors on intersection crash maneuvers

In order to estimate the safety impact of roadway interventions engineers need to collect, analyze, and interpret the results of carefully implemented data collection efforts. The intent of these studies is to develop Accident Modification Factors (AMF's), which are used to predict the safety impact of various road safety features at other locations or in upon future enhancements. Models are typically estimated to estimate AMF's for total crashes, but can and should be estimated for crash outcomes as well. This paper first describes data collected with the intent estimate AMF's for rural intersections in the state of Georgia within the United Sates. Modeling results of crash prediction models for the crash outcomes: angle, head-on, rear-end, sideswipe (same direction and opposite direction) and pedestrian-involved crashes are then presented and discussed. The analysis reveals that factors such as the Annual Average Daily Traffic (AADT), the presence of turning lanes, and the number of driveways have a positive association with each type of crash, while the median width and the presence of lighting are negatively associated with crashes. The model covariates are related to crash outcome in different ways, suggesting that crash outcomes are associated with different pre-crash conditions

Identifying Black Spots Using Property Damage Only Equivalency (PDOE) Factors

Methodologies for identifying ‘sites with promise’ have received considerable attention in the transportation safety literature. Robust hot spot identification (HSID) methodologies are vital as errors result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative) misuse of public funds and lead to poor investment decisions and inefficient risk management. The historical pursuit of HSID methods development has lead to important insights; however, there remain at least several critical impediments to further progress: 1) a significant proportion of property damage only (PDO) and minor injury crashes are underreported (approximately 40%), affecting the reliability of count based models; 2) most methods ignore crash severity and costs; and 3) expected safety performance functions are heavily skewed by a preponderance of zeroes. This paper argues that it is possible and indeed desirable to incorporate crash costs into HSID. Moreover, a straightforward method is proposed whereby crashes are intelligently weighted using property damage only equivalent (PDOE) crashes. The use of PDOEs enables identification of a set of high-risk sites that reflect the true safety costs to society and simultaneously reduces the influence of under-reported PDO crashes, thereby addressing impediments 1 and 2. Non-parametric Quantile regression is used to overcome the preponderance of zeroes problem (impediment 3). The proposed procedure is illustrated using rural road segment data from Korea