Optimisation of speed camera locations using genetic algorithm and pattern search

Road traffic accidents continue to be a public health problem and are a global issue due to the huge financial burden they place on families and society as a whole. Speed has been identified as a major contributor to the severity of traffic accidents and there is the need for better speed management if road traffic accidents are to be reduced. Over the years various measures have been implemented to manage vehicle speeds. The use of speed cameras and vehicle activated signs in recent times has contributed to the reduction of vehicle speeds to various extents. Speed cameras use punitive measures whereas vehicle activated signs do not so their use depends on various factors. Engineers, planners and decision makers responsible for determining the best place to mount a speed camera or vehicle activated sign along a road have based their decision on experience, site characteristics and available guidelines (Department for Transport, 2007; Department for Transport, 2006; Department for Transport, 2003). These decisions can be subjective and indications are that a more formal and directed approach aimed at bringing these available guidelines together in a model will be beneficial in making the right decision as to where to place a speed camera or vehicle activated sign is to be made. The use of optimisation techniques have been applied in other areas of research but this has been clearly absent in the Transport Safety sector. This research aims to contribute to speed reduction by developing a model to help decision makers determine the optimum location for a speed control device. In order to achieve this, the first study involved the development of an Empirical Bayes Negative Binomial regression accident prediction model to predict the number of fatal and serious accidents combined and the number of slight accidents. The accident prediction model that was used explored the effect of certain geometric and traffic characteristics on the effect of the severity of road traffic accident numbers on selected A-roads within the Nottinghamshire and Leicestershire regions of United Kingdom. On A-roads some model variables (n=10) were found to be statistically significant for slight accidents and (n=6) for fatal and serious accidents. The next study used the accident prediction model developed in two optimisation techniques to help predict the optimal location for speed cameras or vehicle activated signs. Pattern Search and Genetic Algorithms were the two main types of optimisation techniques utilised in this thesis. The results show that the two methods did produce similar results in some instances but different in others. Optimised results were compared to some existing sites with speed cameras some of the results obtained from the optimisation techniques used were within proximity of about 160m. A validation method was applied to the genetic algorithm and pattern search optimisation methods. The pattern search method was found to be more consistent than the genetic algorithm method. Genetic algorithm results produced slightly different results at validation in comparison with the initial results. T-test results show a significant difference in the function values for the validated genetic algorithm (M= 607649.34, SD= 1055520.75) and the validated pattern search function values (M= 2.06, SD= 1.17) under the condition t (79) = 5.15, p=0.000. There is a role that optimisation techniques can play in helping to determine the optimum location for a speed camera or vehicle activated sign based on a set of objectives and specified constraints. The research findings as a whole show that speed cameras and vehicle activated signs are an effective speed management tool. Their deployment however needs to be carefully considered by engineers, planners and decision makers so as to achieve the required level of effectiveness. The use of optimisation techniques which has been generally absent in the Transport Safety sector has been shown in this thesis to have the potential to contribute to improve speed management. There is however no doubt that this research will stimulate interest in this rather new but high potential area of Transport Safety

Social-emotional functioning in young people with symptoms of eating disorders: A gender inclusive analogue study.

INTRODUCTION: Contemporary models of eating disorders (EDs) suggest that EDs are maintained by social-emotional difficulties. However, supporting evidence is derived largely from female, clinic-based samples. This study, which refrained from gender specific inclusion criteria, aimed to improve understanding of social-emotional functioning in a large community-based analogue sample of young adults aged 16-26. METHODS: Five hundred and forty-four participants (85.1% female; mean age 21, SD = 4.3) completed the Eating Attitudes Test, Clinical Outcomes in Routine Evaluation, Difficulties in Emotion Regulation Scale, Social Phobia Inventory, Revised Social Anhedonia Scale, Toronto Alexithymia Scale, and the Reading the Mind in the Eyes Task. RESULTS: One hundred and sixty-four participants scored over the EAT-26 clinical cutoff, and a two-way multivariate analysis of covariance found a medium-sized, statistically significant main effect of group on social-emotional functioning (F(5, 530) = 6.204, p ≤ .001, Wilks' Λ = 0.945, d = 0.48.), suggesting that individuals with significant ED symptoms found it more challenging to notice, label, and regulate emotions in themselves and recognize emotions in others. Gender did not significantly impact social-emotional functioning (F(10, 1,060) = 0.556, p = .850, Wilks' Λ = 0.990), and there was no significant group by gender interaction (F(10, 1,060) = 0.688, p = .737, Wilks' Λ = 0.987). CONCLUSION: These data suggest that the social-emotional difficulties, particularly with emotion recognition and regulation, present in clinical samples are also evident in young people of all genders with significant disordered eating. Future work could aim to recruit an even more gender-diverse community sample to further elucidate social-emotional functioning in individuals in the community with significant disordered eating

Visualizing the diffusion of digital mammography in New York State

Digital mammography saw rapid adoption during the first decade of the 2000s. According to data maintained by the Food and Drug Administration, which regulates mammography machines, fewer than 1 percent of the machines in 2001 were digital. By 2014, this figure had risen to 94%. We were interested in identifying the times and locations where the technology was introduced within the state of New York as a way of illustrating the uneven introduction of this technology. While the diffusion of medical innovation has been well-studied, there have not been many instances where maps or geographic information science methods have been used to inform the interpretation. Here we illustrate the adoption of digital mammography in New York as of 2005, 2008, and 2011

Cost of illness of hyponatremia in the United States

BACKGROUND: Hyponatremia is a disorder of fluid and electrolyte balance characterized by a relative excess of body water relative to body sodium content. It is the most common electrolyte disorder encountered in clinical medicine and is associated with negative outcomes in many chronic diseases. However, there is limited information in the literature about health care resource use and costs attributable to the effects of the condition. The purpose of this analysis was to estimate the annual cost of illness of hyponatremia in the United States. METHODS: The study utilized a prevalence-based cost of illness framework that incorporated data from publicly available databases, published literature and a consensus panel of expert physicians. Panel members provided information on: classification of hyponatremia patients, treatment settings for hyponatremia (i.e., hospital, emergency room, doctor's office), and health care resource use associated with the diagnosis and treatment of hyponatremia. Low and high prevalence scenarios were estimated and utilized in a spreadsheet-based cost of illness model. Costs were assigned to units of resources and summarized across treatment settings. RESULTS: The prevalence estimate for hyponatremia ranged from 3.2 million to 6.1 million persons in the U.S. on an annual basis. Approximately 1% of patients were classified as having acute and symptomatic hyponatremia, 4% acute and asymptomatic, 15%–20% chronic and symptomatic, and 75–80% chronic and asymptomatic. Of patients treated for hyponatremia, 55%–63% are initially treated as inpatients, 25% are initially treated in the emergency room, and 13%–20% are treated solely in the office setting. The direct costs of treating hyponatremia in the U.S. on an annual basis were estimated to range between $1.6 billion and$3.6 billion. CONCLUSION: Treatment of hyponatremia represents a significant healthcare burden in the U.S. Newer therapies that may reduce the burden of hyponatremia in the inpatient setting could minimize the costs associated with this condition

Current practices in spatial analysis of cancer data: data characteristics and data sources for geographic studies of cancer

The use of spatially referenced data in cancer studies is gaining in prominence, fueled by the development and availability of spatial analytic tools and the broadening recognition of the linkages between geography and health. We provide an overview of some of the unique characteristics of spatial data, followed by an account of the major types and sources of data used in the spatial analysis of cancer, including data from cancer registries, population data, health surveys, environmental data, and remote sensing data. We cite numerous examples of recent studies that have used these data, with a focus on etiological research

Geographic disparities in colorectal cancer survival

© 2009 Henry et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Public domain small-area cancer incidence data for New York State, 2005-2009

There has long been a demand for cancer incidence data at a fine geographic resolution for use in etiologic hypothesis generation and testing, methodological evaluation, and teaching. In this paper we describe a public domain data set containing data for 23 anatomic sites of cancer diagnosed in New York State between 2005 and 2009 at the level of the census block group. The data set includes 524,503 tumors distributed across 13,823 block groups with an average population of about 1,400. In addition, the data have been linked with race and ethnicity and with socioeconomic indicators such as income, educational attainment, and language proficiency. We demonstrate the application of the data set by confirming two well-established relationships: that between breast cancer and median household income, and that between stomach cancer and Asian race. We foresee that this data set will serve as the basis for a wide range of spatial analyses and serve as a benchmark data set for evaluating spatial methods in the future