The effect of billboard design specifications on driving: A driving simulator study.

Research on the effect of advertising billboards on road safety has accumulated over the past seven decades, but has led to inconclusive data, which prevent clear-cut conclusions. To enhance road safety, it was suggested that researchers should shift their efforts to exploring which billboard characteristics are distracting by nature. This line of research may promote the establishment of concrete guidelines for the least distracting permissible billboards. A previous study classified billboards into three clusters: 1. Loaded (colorful billboards with small quantities of graphic elements and large quantities of text); 2. Graphical (colorful billboards with large quantities of graphic elements and small quantities of text); and 3. Minimal (billboards with few or no graphic elements, few colors, and a small amount of text). The current study systematically explores the effect of these three clusters on drivers' performance in a driving simulator. Eighteen participants drove in scenarios which systematically manipulated the following variables: the perceptual load on the road, the perceptual load on the sides of the road, location of preplanned critical events, and the presence of billboards from each one of the three previously identified clusters. The findings show that the presence of billboards from the Loaded and Minimal clusters significantly compromised road safety in various experimental conditions. However, the presence of billboards from the Graphical cluster significantly affected drivers' performance only in one experimental condition. The conclusion, for the time being, is that Graphical billboards, which include a large quantity of graphic elements with few or no textual elements, are the least harmful while driving.

Effects of longitudinal pavement edgeline condition on driver lane deviation.

Single vehicle crashes, particularly those classified as run-off-the-road, are very common on two-lane rural highways. One method to potentially reduce such crashes is to provide additional driver information in the form of wider longitudinal edgeline pavement markings. However, since these markings deteriorate over time, the primary objective of this research was to study the effects of longitudinal edgeline pavement markings with varying deterioration levels and widths and to assess a driver's ability to maintain lane position. The University of Idaho's driving simulator was used to examine these effects by incorporating different marking deterioration percentages and roadway geometries on a two-lane rural highway environment. Two different pavement marking widths (4 and 6 inch) and four different deterioration levels (0%, 25%, 50%, and 75%) were assessed in daytime and nighttime conditions as part of this study. The results determined that while wider 6-in longitudinal edgeline pavement markings compared with standard four inch edgeline markings did not cause any significant changes in driver lane deviation during the day, statistically significant differences were observed in nighttime driving conditions. Drivers consistently maintained a lane position that slightly favored the edgeline side throughout the study and increasingly shifted away from the centerline as edgeline deterioration worsened.

Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods.

This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements.

Consenso español sobre los riesgos y detección de la hiperprolactinemia iatrogénica por antipsicóticos: ¿existe convergencia con otras guías clínicas de manejo de la hiperprolactinemia? / Spanish consensus on the risks and detection of antipsychotic drug-related hyperprolactinaemia: is there convergence with other clinical guidelines for the management of hyperprolactinaemia?

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The influence of traffic signal solutions on self-reported road-crossing behavior

Injury to pedestrians is a major safety hazard in many countries. Since the beginning of the last century, modern cities have been designed around the use of motor vehicles despite the unfavourable interactions between the vehicles and pedestrians. This push towards urbanization resulted in a substantial number of crashes and fatalities involving pedestrians every day, all over the world. Thus, improving the design of urban cities and townships is a pressing issue for modern society. The study presented here provides a characterization of pedestrian safety problems, with the emphasis on signalized crosswalks (i.e. traffic signal) design solutions. We tested the impact of seven different traffic light configurations (steady [green, yellow, and red], flashing [green, yellow, and red], and light off) on pedestrian self-reported road-crossing behavior, using a 11-point scale -ranging from 0 («I never cross in this situation») to 10 («I always cross in this situation»). Results showed that mandatory solutions (steady green vs. steady red) are the best solutions to avoid unsafe pedestrian behaviors while crossing controlled intersections (frequency of crossing: Mgreen = 9.4 ± 1 vs. Mred = 2.6 ± 2). These findings offer important guidelines for the design of future traffic signals for encouraging a pedestrian/transit-friendly environment (AU)

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A regional waterway management system for balancing recreational boating and resource protection.

Florida's coasts have been transformed over the past three decades as population growth and unprecedented demand for individual shore access to bays and estuaries led to the creation of residential canal developments. Thousands of miles of channels and basins were dredged as a by-product of this urbanization process. The navigable waterways that resulted are now being stressed by increasing boat traffic and canal-side activities. Recognizing their common goal to preserve the recreational and ecological value of southwest Florida waterways, the Florida Department of Environmental Protection, the four-county West Coast Inland Navigation District, and the University of Florida Sea Grant College Program signed a Memorandum of Agreement. The signatories agreed to develop a science-based Regional Waterway Management System (RWMS), which is a new approach to waterway planning and permitting based on carefully mapped channel depths, a census of actual boat populations, and the spatial extent of natural resources. The RWMS provides a comprehensive, regional overview of channel conditions and the geographic distribution and severity of existing impediments to safe navigation and resource protection. RWMS information and analyses result in regional-scale permitting to accommodate water-dependent uses while minimizing environmental impacts and reducing public expenditures. Compared with traditional approaches to waterway management, the science-based RWMS is relatively unbiased, objective, transparent, ecologically sound, and fiscally prudent.