Validation of FHWA crash models for rural intersections - Lessons learned

A national-level safety analysis tool is needed to complement existing analytical tools for assessment of the safety impacts of roadway design alternatives. FHWA has sponsored the development of the Interactive Highway Safety Design Model (IHSDM), which is roadway design and redesign software that estimates the safety effects of alternative designs. Considering the importance of IHSDM in shaping the future of safety-related transportation investment decisions, FHWA justifiably sponsored research with the sole intent of independently validating some of the statistical models and algorithms in IHSDM. Statistical model validation aims to accomplish many important tasks, including (a) assessment of the logical defensibility of proposed models, (b) assessment of the transferability of models over future time periods and across different geographic locations, and (c) identification of areas in which future model improvements should be made. These three activities are reported for five proposed types of rural intersection crash prediction models. The internal validation of the model revealed that the crash models potentially suffer from omitted variables that affect safety, site selection and countermeasure selection bias, poorly measured and surrogate variables, and misspecification of model functional forms. The external validation indicated the inability of models to perform on par with model estimation performance. Recommendations for improving the state of the practice from this research include the systematic conduct of carefully designed before-and-after studies, improvements in data standardization and collection practices, and the development of analytical methods to combine the results of before-and-after studies with cross-sectional studies in a meaningful and useful way.

Soil carbon saturation: Linking concept and measurable carbon pools

The soil C saturation concept suggests a limit to whole soil organic carbon (SOC) accumulation determined by inherent physicochemical characteristics of four soil C pools: unprotected, physically protected, chemically protected, and biochemically protected. Previous attempts to quantify soil C sequestration capacity have focused primarily on silt and clay protection and largely ignored the effects of soil structural protection and biochemical protection. We assessed two contrasting models of SOC accumulation, one with no saturation limit (i.e., linear first-order model) and one with an explicit soil C saturation limit (i.e., C saturation model). We isolated soil fractions corresponding to the C pools (i.e., free particulate organic matter POM], microaggregate-associated C, silt- and clay-associated C, and non-hydrolyzable C) from eight long-term agroecosystern experiments across the United States and Canada. Due to the composite nature of the physically protected C pool, we firactioned it into mineral- vs. POM-associated C. Within each site, the number of fractions fitting the C saturation model was directly related to maximum SOC content, suggesting that a broad range in SOC content is necessary to evaluate fraction C saturation. The two sites with the greatest SOC range showed C saturation behavior in the chemically, biochemically, and some mineral-associated fractions of the physically protected pool. The unprotected pool and the aggregate-protected POM showed linear, nonsaturating behavior. Evidence of C saturation of chemically and biochemically protected SOC pools was observed at sites far from their theoretical C saturation level, while saturation of aggregate-protected fractions occurred in soils closer to their C saturation level.

Repair and regeneration of osteochondral defects in the articular joints

People suffering from pain due to osteoarthritic or rheumatoidal changes in the joints are still waiting for a better treatment. Although some studies have achieved success in repairing small cartilage defects, there is no widely accepted method for complete repair of osteochondral defects. Also joint replacements have not yet succeeded in replacing of natural cartilage without complications. Therefore, there is room for a new medical approach, which outperforms currently used methods. The aim of this study is to show potential of using a tissue engineering approach for regeneration of osteochondral defects. The critical review of currently used methods for treatment of osteochondral defects is also provided. In this study, two kinds of hybrid scaffolds developed in Hutmacher's group have been analysed. The first biphasic scaffold consists of fibrin and PCL. The fibrin serves as a cartilage phase while the porous PCL scaffold acts as the subchondral phase. The second system comprises of PCL and PCL-TCP. The scaffolds were fabricated via fused deposition modeling which is a rapid prototyping system. Bone marrow-derived mesenchymal cells were isolated from New Zealand White rabbits, cultured in vitro and seeded into the scaffolds. Bone regenerations of the subchondral phases were quantified via micro CT analysis and the results demonstrated the potential of the porous PCL and PCL-TCP scaffolds in promoting bone healing. Fibrin was found to be lacking in this aspect as it degrades rapidly. On the other hand, the porous PCL scaffold degrades slowly hence it provides an effective mechanical support. This study shows that in the field of cartilage repair or replacement, tissue engineering may have big impact in the future. In vivo bone and cartilage engineering via combining a novel composite, biphasic scaffold technology with a MSC has been shown a high potential in the knee defect regeneration in the animal models. However, the clinical application of tissue engineering requires the future research work due to several problems, such as scaffold design, cellular delivery and implantation strategies.

Comparison of human alveolar osteoblasts cultured on polymer-ceramic composite scaffolds and tissue culture plates

The effects of medical grade polycaprolactone–tricalcium phosphate (mPCL–TCP) (80:20) scaffolds on primary human alveolar osteoblasts (AOs) were compared with standard tissue-culture plates. Of the seeded AOs, 70% adhered to and proliferated on the scaffold surface and within open and interconnected pores; they formed multi-layered sheets and collagen fibers with uniform distribution within 28 days. Elevation of alkaline phosphatase activity occurred in scaffold–cell constructs independent of osteogenic induction. AO proliferation rate increased and significant decrease in calcium concentration of the medium for both scaffolds and plates under induction conditions were seen. mPCL–TCP scaffolds significantly influenced the AO expression pattern of osterix and osteocalcin (OCN). Osteogenic induction down-regulated OCN at both RNA and protein level on scaffolds (3D) by day 7, and up-regulated OCN in cell-culture plates (2D) by day 14, but OCN levels on scaffolds were higher than on cell-culture plates. Immunocytochemical signals for type I collagen, osteopontin and osteocalcin were detected at the outer parts of scaffold–cell constructs. More mineral nodules were found in induced than in non-induced constructs. Only induced 2D cultures showed nodule formation. mPCL–TCP scaffolds appear to stimulate osteogenesis in vitro by activating a cellular response in AO's to form mineralized tissue. There is a fundamental difference between culturing AOs on 2D and 3D environments that should be considered when studying osteogenesis in vitro.

Towards airport passenger experience models

This paper reports on the study of passenger experiences and how passengers interact with services, technology and processes at an airport. As part of our research, we have followed people through the airport from check-in to security and from security to boarding. Data was collected by approaching passengers in the departures concourse of the airport and asking for their consent to be videotaped. Data was collected and coded and the analysis focused on both discretionary and process related passenger activities. Our findings show the interdependence between activities and passenger experiences. Within all activities, passengers interact with processes, domain dependent technology, services, personnel and artifacts. These levels of interaction impact on passenger experiences and are interdependent. The emerging taxonomy of activities consists of (i) ownership related activities, (ii) group activities, (iii) individual activities (such as activities at the domain interfaces) and (iv) concurrent activities. This classification is contributing to the development of descriptive models of passenger experiences and how these activities affect the facilitation and design of future airports.

Important omitted spatial variables in safety models: Understanding contributing crash causes at intersections

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causation—rests upon the pursuit of numerous lines of inquiry. The research community has focused on analytical methods development (negative binomial specifications, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might think of different lines of inquiry in terms of ‘low lying fruit’—areas of inquiry that might provide significant improvements in understanding crash causation. It is the contention of this research that omitted variable bias caused by the exclusion of important variables is an important line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant ability to better understand contributing factors to crashes. This study—believed to represent a unique contribution to the safety literature—develops and examines the role of a sizeable set of spatial variables in intersection crash occurrence. In addition to commonly considered traffic and geometric variables, examined spatial factors include local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools. The results indicate that inclusion of these factors results in significant improvement in model explanatory power, and the results also generally agree with expectation. The research illuminates the importance of spatial variables in safety research and also the negative consequences of their omissions.

The significance of endogeneity problems in crash models: An examination of left-turn lanes in intersection crash models

Crash prediction models are used for a variety of purposes including forecasting the expected future performance of various transportation system segments with similar traits. The influence of intersection features on safety have been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes compared to other segments in the transportation system. The effects of left-turn lanes at intersections in particular have seen mixed results in the literature. Some researchers have found that left-turn lanes are beneficial to safety while others have reported detrimental effects on safety. This inconsistency is not surprising given that the installation of left-turn lanes is often endogenous, that is, influenced by crash counts and/or traffic volumes. Endogeneity creates problems in econometric and statistical models and is likely to account for the inconsistencies reported in the literature. This paper reports on a limited-information maximum likelihood (LIML) estimation approach to compensate for endogeneity between left-turn lane presence and angle crashes. The effects of endogeneity are mitigated using the approach, revealing the unbiased effect of left-turn lanes on crash frequency for a dataset of Georgia intersections. The research shows that without accounting for endogeneity, left-turn lanes ‘appear’ to contribute to crashes; however, when endogeneity is accounted for in the model, left-turn lanes reduce angle crash frequencies as expected by engineering judgment. Other endogenous variables may lurk in crash models as well, suggesting that the method may be used to correct simultaneity problems with other variables and in other transportation modeling contexts.

On the nature of over-dispersion in motor vehicle crash prediction models

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts—variation over and above that accounted for by the Poisson density. The extra-variation – or dispersion – is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models—tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31–40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites

Incorporating weather into regionwide safety planning prediction models

Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.