Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling

Driving along any rural road within Western Australia involves some level of uncertainty about encountering an animal whether it is wildlife, farm stock or domestic. This level of uncertainty can vary depending on factors such as the surrounding land use, water source, geometry of the road, speed limits and signage. This paper aims to model the risk of animal–vehicle crashes (AVCs) on a segmented highway. A hierarchical Bayesian model involving multivariate Poisson lognormal regression is used in establishing the relationship between AVCs and the contributing factors. Findings of this study show that farming on both sides of a road, a mixture of farming and forest roadside vegetation and roadside vegetation have significant positive effect on AVCs, while speed limits and horizontal curves indicate a negative effect. AVCs consist of both spatial- and segment-specific contributions, even though the spatial random error does not dominate model variability. Segment 15 is identified as the highest risk segment and its nearby segments also exhibit high risk

Hot-spot Identification: a Categorical Binary Model Approach

This paper presents an alternative methodology for hot-spot identification based on a probabilistic model. In this methodology, the ranking criterion for hot-spot identification conveys the probability of a site being a hot-spot or a non-hot spot. A binary choice model was used to link the outcome to a set of factors that characterize the risk of the sites under analysis based on our use of two categories (0/1) for the dependent variable. The proposed methodology consists of two main steps. First, a threshold value for the number of accidents is set to distinguish hot spots from safe sites (category 1 or 0, respectively). Based on this classification, a binary model is applied that allows the construction of an ordered site list using the probability of a site being a hot-spot. The second step involves the choice of a selection strategy. The selection strategy can target a fixed number of sites with the greatest probability or, alternatively, all sites exceeding a specific probability, such as 0.5. A demonstration of the proposed methodology is provided using simulated data. For the simulation design, urban intersection data from Porto, Portugal, covering a five-year period were used. The results of the binary model showed a good fit. To evaluate and compare the probabilistic method with other commonly used methods, measures were used to test the performance of each method in terms of its power to detect the "true" hot spots. The test results indicate that the proposed method is superior to two commonly used methods. The gains of using this method are related to the simplicity of its application, while critical issues such as prior distribution effect assumptions and the regression-to-the-mean phenomenon are overcome. Further, the proposed model provides a realistic and intuitive perspective and supports easy practical application

A geographical population analysis of dental trauma in school-children aged 12 and 15 in the city of Curitiba-Brazil

<p>Abstract</p> <p>Background</p> <p>The study presents a geographical analysis of dental trauma in a population of 12 and 15 year-old school-children, in the city of Curitiba, Brazil (n = 1581), using a database obtained in the period 2005-2006. The main focus is to analyze dental trauma using a geographic information system as a tool for integrating social, environmental and epidemiological data.</p> <p>Methods</p> <p>Geostatistical analysis of the database and thematic maps were generated showing the distribution of dental trauma cases according to Curitiba's Health Districts and other variables of interest. Dental trauma spatial variation was assessed using a generalized additive model in order to identify and control the individual risk-factors and thus determine whether spatial variation is constant or not throughout the Health Districts and the place of residence of individuals. In addition, an analysis was made of the coverage of dental trauma cases taking the spatial distribution of Curitiba's primary healthcare centres.</p> <p>Results</p> <p>The overall prevalence of dental trauma was 37.1%, with 53.1% in males and 46.7% in females. The spatial analysis confirms the hypothesis that there is significant variation in the occurrence of dental trauma, considering the place of residence in the population studied (Monte Carlo test, p = 0,006). Furthermore, 28.7% of cases had no coverage by the primary healthcare centres.</p> <p>Conclusions</p> <p>The effect of the place of residence was highly significant in relation to the response variable. The delimitation of areas, as a basis for case density, enables the qualification of geographical territories where actions can be planned based on priority criteria. Promotion, control and rehabilitation actions, applied in regions of higher prevalence of dental trauma, can be more effective and efficient, thus providing healthcare refinement.</p

A MODEL SUGGESTION FOR THE DETERMINATION OF THE TRAFFIC ACCIDENT HOTSPOTS ON THE TURKISH HIGHWAY ROAD NETWORK: A PILOT STUDY

Sugestão de modelo para a determinação de pontos críticos de acidentes de tráfego na rede de estradas de rodagem da Turquia: um estudo pilot

Estimating vehicle roadside encroachment frequency using accident prediction models

The existing data to support the development of roadside encroachment- based accident models are extremely limited and largely outdated. Under the sponsorship of the Federal Highway Administration and Transportation Research Board, several roadside safety projects have attempted to address this issue by providing rather comprehensive data collection plans and conducting pilot data collection efforts. It is clear from the results of these studies that the required field data collection efforts will be expensive. Furthermore, the validity of any field collected encroachment data may be questionable because of the technical difficulty to distinguish intentional from unintentional encroachments. This paper proposes an alternative method for estimating the basic roadside encroachment data without actually field collecting them. The method is developed by exploring the probabilistic relationships between a roadside encroachment event and a run-off-the-road event With some mild assumptions, the method is capable of providing a wide range of basic encroachment data from conventional accident prediction models. To illustrate the concept and use of such a method, some basic encroachment data are estimated for rural two-lane undivided roads. In addition, the estimated encroachment data are compared with the existing collected data. The illustration shows that the method described in this paper can be a viable approach to estimating basic encroachment data without actually collecting them which can be very costly