Performance evaluation of an adaptive travel time prediction model

This paper presents a travel time prediction model and evaluates its performance and transferability. Advanced Travelers Information Systems (ATIS) are gaining more and more importance, increasing the need for accurate, timely and useful information to the travelers. Travel time information quantifies the traffic condition in an easy to understand way for the users. The proposed travel time prediction model is based on an efficient use of nearest neighbor search. The model is calibrated for optimal performance using Genetic Algorithms. Results indicate better performance by using the proposed model than the presently used naïve model.

Adherence to nutrition supplements among patients with a fall-related lower limb fracture

Background The purpose of this study was to provide a detailed evaluation of adherence to nutrition supplements by patients with a lower limb fracture. Methods These descriptive data are from 49 nutritionally“ at-risk” patients aged 70+ years admitted to the hospital after a fall-related lower limb fracture and allocated to receive supplementation as part of a randomized, controlled trial. Supplementation commenced on day 7 and continued for 42 days. Prescribed volumes aimed to meet 45% of individually estimated theoretical energy requirements to meet the shortfall between literature estimates of energy intake and requirements. The supplement was administered by nursing staff on medication rounds in the acute or residential care settings and supervised through thrice-weekly home visits postdischarge. Results Median daily percent of the prescribed volume of nutrition supplement consumed averaged over the 42 days was 67% (interquartile range [IQR], 31–89, n = 49). There was no difference in adherence for gender, accommodation, cognition, or whether the supplement was self-administered or supervised. Twenty-three participants took some supplement every day, and a further 12 missed <5 days. For these 35 “nonrefusers,” adherence was 82% (IQR, 65–93), and they lost on average 0.7% (SD, 4.0%) of baseline weight over the 6 weeks of supplementation compared with a loss of 5.5% (SD, 5.4%) in the “refusers” (n = 14, 29%), p = .003. Conclusions We achieved better volume and energy consumption than previous studies of hip fracture patients but still failed to meet target supplement volumes prescribed to meet 45% of theoretical energy requirements. Clinicians should consider alternative methods of feeding such as a nasogastric tube, particularly in those patients where adherence to oral nutrition supplements is poor and dietary intake alone is insufficient to meet estimated energy requirements.

An epidemiological study of the relationship between time to surgery and health status for elderly patients with a hip fracture

A hip fracture causes permanent changes to life style for older people. Further, two important mortality indicators found post operatively for this group include, the time until surgery after fracture, and pre-operative health status prior to surgery, yet no research is available investigating relationships between time to surgery and health status. The researchers aimed to establish the health status risks for patients aged over 65 years with a non-pathological hip fracture to guide nursing care interventions. A prospective cohort design was used to investigate relationships between time to surgery and measures on pre-operative health status indicators including, skin integrity risk, vigor, mental state, bowel function and continence. Twenty-nine patients with a mean age in years of 81.93 (SD,9.49), were recruited. The mean number of hours from time 1 assessment to surgery was 52.72 (SD,58.35) and the range was 1 hour to 219 hours. At Time 2, the mean scores of vigor and skin integrity risk were significantly higher, indicating poorer health status. A change in health status occurred but possibly due to the small sample size it was difficult to relate this result to time. However the results informed preoperative care prior to surgery, for this group.

Empirical investigation of interactive highway safety design model accident prediction algorithm : Rural intersections

One major gap in transportation system safety management is the ability to assess the safety ramifications of design changes for both new road projects and modifications to existing roads. To fulfill this need, FHWA and its many partners are developing a safety forecasting tool, the Interactive Highway Safety Design Model (IHSDM). The tool will be used by roadway design engineers, safety analysts, and planners throughout the United States. As such, the statistical models embedded in IHSDM will need to be able to forecast safety impacts under a wide range of roadway configurations and environmental conditions for a wide range of driver populations and will need to be able to capture elements of driving risk across states. One of the IHSDM algorithms developed by FHWA and its contractors is for forecasting accidents on rural road segments and rural intersections. The methodological approach is to use predictive models for specific base conditions, with traffic volume information as the sole explanatory variable for crashes, and then to apply regional or state calibration factors and accident modification factors (AMFs) to estimate the impact on accidents of geometric characteristics that differ from the base model conditions. In the majority of past approaches, AMFs are derived from parameter estimates associated with the explanatory variables. A recent study for FHWA used a multistate database to examine in detail the use of the algorithm with the base model-AMF approach and explored alternative base model forms as well as the use of full models that included nontraffic-related variables and other approaches to estimate AMFs. That research effort is reported. The results support the IHSDM methodology.

Best practices in prediction for decision-making : lessons from the atmospheric and earth sciences

Predictions that result from scientific research hold great appeal for decision-makers who are grappling with complex and controversial environmental issues, by promising to enhance their ability to determine a need for and outcomes of alternative decisions. A problem exists in that decision-makers and scientists in the public and private sectors solicit, produce, and use such predictions with little understanding of their accuracy or utility, and often without systematic evaluation or mechanisms of accountability. In order to contribute to a more effective role for ecological science in support of decision-making, this paper discusses three ``best practices'' for quantitative ecosystem modeling and prediction gleaned from research on modeling, prediction, and decision-making in the atmospheric and earth sciences. The lessons are distilled from a series of case studies and placed into the specific context of examples from ecological science.

Influences of age and mechanical stability on volume, microstructure, and mineralization of the fracture callus during bone healing : Is osteoclast activity the key to age-related impaired healing?

Earlier studies have shown that the influence of fixation stability on bone healing diminishes with advanced age. The goal of this study was to unravel the relationship between mechanical stimulus and age on callus competence at a tissue level. Using 3D in vitro micro-computed tomography derived metrics, 2D in vivo radiography, and histology, we investigated the influences of age and varying fixation stability on callus size, geometry, microstructure, composition, remodeling, and vascularity. Compared were four groups with a 1.5-mm osteotomy gap in the femora of Sprague–Dawley rats: Young rigid (YR), Young semirigid (YSR), Old rigid (OR), Old semirigid (OSR). Hypothesis was that calcified callus microstructure and composition is impaired due to the influence of advanced age, and these individuals would show a reduced response to fixation stabilities. Semirigid fixations resulted in a larger ΔCSA (Callus cross-sectional area) compared to rigid groups. In vitro μCT analysis at 6 weeks postmortem showed callus bridging scores in younger animals to be superior than their older counterparts (pb0.01). Younger animals showed (i) larger callus strut thickness (pb0.001), (ii) lower perforation in struts (pb0.01), and (iii) higher mineralization of callus struts (pb0.001). Callus mineralization was reduced in young animals with semirigid fracture fixation but remained unaffected in the aged group. While stability had an influence, age showed none on callus size and geometry of callus. With no differences observed in relative osteoid areas in the callus ROI, old as well as semirigid fixated animals showed a higher osteoclast count (pb0.05). Blood vessel density was reduced in animals with semirigid fixation (pb0.05). In conclusion, in vivo monitoring indicated delayed callus maturation in aged individuals. Callus bridging and callus competence (microstructure and mineralization) were impaired in individuals with an advanced age. This matched with increased bone resorption due to higher osteoclast numbers. Varying fixator configurations in older individuals did not alter the dominant effect of advanced age on callus tissue mineralization, unlike in their younger counterparts. Age-associated influences appeared independent from stability. This study illustrates the dominating role of osteoclastic activity in age-related impaired healing, while demonstrating the optimization of fixation parameters such as stiffness appeared to be less effective in influencing healing in aged individuals.

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