RESUMEN
The Highway Safety Manual provides multiple methods that can be used to identify sites with promise (SWiPs) for safety improvement. However, most of these methods cannot be used to identify sites with specific problems. Furthermore, given that infrastructure funding is often specified for use related to specific problems/programs, a method for identifying SWiPs related to those programs would be very useful. This research establishes a method for Identifying SWiPs with specific issues. This is accomplished using two safety performance functions (SPFs). This method is applied to identifying SWiPs with geometric design consistency issues. Mixed effects negative binomial regression was used to develop two SPFs using 5 years of crash data and over 8754km of two-lane rural roadway. The first SPF contained typical roadway elements while the second contained additional geometric design consistency parameters. After empirical Bayes adjustments, sites with promise (SWiPs) were identified. The disparity between SWiPs identified by the two SPFs was evident; 40 unique sites were identified by each model out of the top 220 segments. By comparing sites across the two models, candidate road segments can be identified where a lack design consistency may be contributing to an increase in expected crashes. Practitioners can use this method to more effectively identify roadway segments suffering from reduced safety performance due to geometric design inconsistency, with detailed engineering studies of identified sites required to confirm the initial assessment.
Asunto(s)
Accidentes de Tránsito/prevención & control , Conducción de Automóvil/estadística & datos numéricos , Planificación Ambiental/estadística & datos numéricos , Seguridad/estadística & datos numéricos , Teorema de Bayes , Humanos , Modelos Estadísticos , Análisis EspacialRESUMEN
This study integrates a causal inference framework to the Empirical Bayes (EB) before-after method to develop generalizable safety effect estimates (i.e., crash modification factor (CMF)). The method considers approaches to estimate the average treatment effect for the treated (ATT), average treatment effect for the untreated (ATU), and average treatment effect (ATE). The current EB method is shown to estimate ATT while ATE is what is typically desired in traffic safety research. Modifications to the current EB method to estimate ATU and ATE are provided. The method is then applied to a dataset with a "no-treatment" scenario where the treatments were: 1) randomly selected and 2) selected based on crash history. Given the "no-treatment" outcome, it is known that the CMFs should have a value of 1 in order to be considered accurate. The standard negative binomial and mixed effects negative binomial regression models were applied in the analysis. It was found that, of the two regression methods, the ATE CMFs developed using the standard negative binomial were the most accurate. Finally, potential sources of bias in the EB method are discussed.
Asunto(s)
Accidentes de Tránsito/estadística & datos numéricos , Accidentes de Tránsito/tendencias , Teorema de Bayes , Estudios Controlados Antes y Después , Humanos , Modelos Estadísticos , Análisis de Regresión , Medición de Riesgo , SeguridadRESUMEN
Underreporting is a well-known issue in crash frequency research. However, statistical methods that can account for underreporting have received little attention in the published literature. This paper compares results from underreporting models to models that account for unobserved heterogeneity. The difference in the elasticities between the negative binomial underreporting model and random parameters negative binomial models, which accounts for unobserved heterogeneity in crash frequency models, are used as the basis for comparison. The paper also includes a comparison of the predicted number of unreported PDO crashes based on the negative binomial underreporting model with crashes that were reported to police but were not considered reportable to PennDOT to assess the ability of the underreporting models to predict non-reportable crashes. The data used in this study included 21,340 segments of two-lane rural highways that are owned and maintained by PennDOT. Reported accident frequencies over an eight year period (2005-2012) were included in the sample, producing a total of 170,468 segment-years of data. The results indicate that if a variable impacts both the true accident frequency and the probability of accidents being reported, statistical modeling methods that ignore underreporting produce biased regression coefficients. The magnitude of the bias in the present study (based on elasticities) ranged from 0.00-16.79%. If the variable affects the true accident frequency, but not the probability of accidents being reported, the results from the negative binomial underreporting models are consistent with analysis methods that do not account for underreporting.
Asunto(s)
Accidentes de Tránsito/estadística & datos numéricos , Sesgo , Planificación Ambiental , Proyectos de Investigación/estadística & datos numéricos , Investigación/estadística & datos numéricos , Seguridad/estadística & datos numéricos , Estudios Transversales , Humanos , Modelos Estadísticos , Modelos Teóricos , Población Rural/estadística & datos numéricos , Estadística como Asunto , Heridas y Lesiones/epidemiologíaRESUMEN
A sufficient understanding of the safety impact of lane widths in urban areas is necessary to produce geometric designs that optimize safety performance for all users. The overarching trend found in the research literature is that as lane widths narrow, crash frequency increases. However, this trend is inconsistent and is the result of multiple cross-sectional studies that have issues related to lack of control for potential confounding variables, unobserved heterogeneity or omitted variable bias, or endogeneity among independent variables, among others. Using ten years of mid-block crash data on urban arterials and collectors from four cities in Nebraska, crash modification factors (CMFs) were estimated for various lane widths and crash types. These CMFs were developed using the propensity scores-potential outcomes methodology. This method reduces many of the issues associated with cross-sectional regression models when estimating the safety effects of infrastructure-related design features. Generalized boosting, a non-parametric modeling technique, was used to estimate the propensity scores. Matching was performed using both Nearest Neighbor and Mahalanobis matching techniques. CMF estimation was done using mixed-effects negative binomial or Poisson regression with the matched data. Lane widths included in the analysis included 9ft, 10ft, 11ft, and 12ft. Some of the estimated CMFs were point estimates while others were functions of traffic volume (i.e., the CMF changed depending on the traffic volume). Roadways with 10ft travel lanes were found to experience the highest crash frequency relative to other lane widths. Meanwhile, roads with 9ft travel lanes were found to experience the lowest relative crash frequency. While this may be due to increased driver caution when traveling on narrow lanes, it is possible that unobserved factors influenced this result. CMFs for target crash types (sideswipe same-direction and sideswipe opposite-direction) were consistent with the values currently used in the Highway Safety Manual (HSM).
Asunto(s)
Accidentes de Tránsito/prevención & control , Planificación Ambiental , Ciudades , Estudios Transversales , Humanos , Nebraska , Puntaje de Propensión , Análisis de Regresión , SeguridadRESUMEN
A variety of different study designs and analysis methods have been used to evaluate the performance of traffic safety countermeasures. The most common study designs and methods include observational before-after studies using the empirical Bayes method and cross-sectional studies using regression models. The propensity scores-potential outcomes framework has recently been proposed as an alternative traffic safety countermeasure evaluation method to address the challenges associated with selection biases that can be part of cross-sectional studies. Crash modification factors derived from the application of all three methods have not yet been compared. This paper compares the results of retrospective, observational evaluations of a traffic safety countermeasure using both before-after and cross-sectional study designs. The paper describes the strengths and limitations of each method, focusing primarily on how each addresses site selection bias, which is a common issue in observational safety studies. The Safety Edge paving technique, which seeks to mitigate crashes related to roadway departure events, is the countermeasure used in the present study to compare the alternative evaluation methods. The results indicated that all three methods yielded results that were consistent with each other and with previous research. The empirical Bayes results had the smallest standard errors. It is concluded that the propensity scores with potential outcomes framework is a viable alternative analysis method to the empirical Bayes before-after study. It should be considered whenever a before-after study is not possible or practical.