The application of social marketing in reducing road traffic accidents amoung young male drivers: an investigation using physical fear threat appeals

There were 338 road fatalities on Irish roads in 2007. Research in 2007 by the Road Safety Authority in Ireland states that young male drivers (17 – 25 years) are seven times more likely to be killed on Irish roads than other road users. The car driver fatality rate was found to be approximately 10 times higher for young male drivers than for female drivers in 2000. Young male drivers in particular demonstrate a high proclivity for risky driving behaviours. These risky behaviours include drink driving, speeding, rug-driving and engaging in aggressive driving. Speed is the single largest contributing factor to road deaths in Ireland. Approximately 40% of fatal accidents are caused by excessive or inappropriate speed. This study focuses on how dangerous driving behaviours may be addressed through social marketing. This study analyses the appropriate level of fear that needs to be induced in order to change young male driving behaviour.

Ungulate behavioral responses to the heterogeneous road-network of a touristic protected area in Africa

Understanding how wildlife responds to road and traffic is essential for effective conservation. Yet, not many studies have evaluated how roads influence wildlife in protected areas, particularly within the large iconic African National Parks where tourism is mainly based on sightings from motorized vehicles with the consequent development and intense use of roads. To reduce this knowledge gap, we studied the behavioral response and local spatial distribution of impala Aepyceros melampus along the heterogeneous (with variation in road surface type and traffic intensity) road-network of Kruger National Park (KNP, South Africa). We surveyed different types of roads (paved and unpaved) recording the occurrence of flight responses among sighted impala and describing their local spatial distribution (in relation to the roads). We observed relatively few flight responses (19.5% of 118 observations), suggesting impalas could be partly habituated to vehicles in KNP. In addition, impala local distribution is apparently unaffected by unpaved roads, yet animals seem to avoid the close proximity of paved roads. Overall, our results suggest a negative, albeit small, effect of traffic intensity, and of presence of pavement on roads on the behavior of impala at KNP. Future studies would be necessary to understand how roads influence other species, but our results show that even within a protected area that has been well-visited for a long time, wildlife can still be affected by roads and traffic. This result has ecological (e.g., changes in spatial distribution of fauna) and management implications (e.g., challenges of facilitating wildlife sightings while minimizing disturbance) for protected areas where touristic activities are largely based on driving.

A Romano-British settlement with ovens and field system at Theobalds Road, Wivelsfield, East Sussex

Excavation west of Wivelsfield, East Sussex, revealed part of an early Romano-British settlement. One of the round-houses may have had a non-domestic, possibly ritual, function. The settlement appears to have been subsequently incorporated within a rectilinear arrangement of field/enclosure ditches. Along the edge of one of these ditches were built a series of features interpreted as ovens, of varying form and likely use, from which charred waste from cereal processing and charcoal from coppiced woodland were recovered.

Green infrastructure and ecosystem services - is the devil in the detail?

Background - Green infrastructure is a strategic network of green spaces designed to deliver ecosystem services to human communities. Green infrastructure is a convenient concept for urban policy makers, but the term is used too-generically and with limited understanding of the relative values or benefits of different types of green space and how these complement one another. At a finer scale/more practical level– little consideration is given to the composition of the plant-communities, yet this is what ultimately defines extent of service provision. This paper calls for greater attention to be paid to urban plantings with respect to ecosystem service delivery and for plant science to engage more-fully in identifying those plants that promote various services. Scope - Many urban plantings are designed based on aesthetics alone, with limited thought on how plant choice/composition provides other ecosystem services. Research is beginning to demonstrate, however, that landscape plants provide a range of important services, such as helping mitigate floods and alleviating heat islands, but that not all species are equally effective. The paper reviews a number of important services and demonstrates how genotype choice radically affects service delivery. Conclusions – Although research is in its infancy, data is being generated that relates plant traits to specific services; thereby helping identify genotypes that optimise service delivery. The urban environment, however, will become exceedingly bland if future planting is simply restricted to monocultures of a few ‘functional’ genotypes. Therefore, further information is required on how to design plant communities where the plants identified:- a/ provide more than a single benefit (multi-functionality) b/ complement each other in maximising the range of benefits that can be delivered in one location and c/ continue to maintain public acceptance through diversity. The identification/development of functional landscape plants is an exciting and potentially high impact arena for plant science.

Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil

In the metropolitan area of Sao Paulo, Brazil, ozone and particulate matter ( PM) are the air pollutants that pose the greatest threat to air quality, since the PM and the ozone precursors ( nitrogen oxides and volatile organic compounds) are the main source of air pollution from vehicular emissions. Vehicular emissions can be measured inside road tunnels, and those measurements can provide information about emission factors of in-use vehicles. Emission factors are used to estimate vehicular emissions and are described as the amount of species emitted per vehicle distance driven or per volume of fuel consumed. This study presents emission factor data for fine particles, coarse particles, inhalable particulate matter and black carbon, as well as size distribution data for inhalable particulate matter, as measured in March and May of 2004, respectively, in the Janio Quadros and Maria Maluf road tunnels, both located in Sao Paulo. The Janio Quadros tunnel carries mainly light-duty vehicles, whereas the Maria Maluf tunnel carries light-duty and heavy-duty vehicles. In the Janio Quadros tunnel, the estimated light-duty vehicle emission factors for the trace elements copper and bromine were 261 and 220 mu g km(-1), respectively, and 16, 197, 127 and 92 mg km(-1), respectively, for black carbon, inhalable particulate matter, coarse particles and fine particles. The mean contribution of heavy-duty vehicles to the emissions of black carbon, inhalable particulate matter, coarse particles and fine particles was, respectively 29, 4, 6 and 6 times higher than that of light-duty vehicles. The inhalable particulate matter emission factor for heavy-duty vehicles was 1.2 times higher than that found during dynamometer testing. In general, the particle emissions in Sao Paulo tunnels are higher than those found in other cities of the world.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

Automatically linking live experiences captured with a ubiquitous infrastructure

Ubiquitous computing aims at providing services to users in everyday environments such as the home. One research theme in this area is that of building capture and access applications which support information to be recorded ( captured) during a live experience toward automatically producing documents for review (accessed). The recording demands instrumented environments with devices such as microphones, cameras, sensors and electronic whiteboards. Since each experience is usually related to many others ( e. g. several meetings of a project), there is a demand for mechanisms supporting the automatic linking among documents relative to different experiences. In this paper we present original results relative to the integration of our previous efforts in the Infrastructure for Capturing, Accessing, Linking, Storing and Presenting information (CALiSP). Ubiquitous computing aims at providing services to users in everyday environments such as the home. One research theme in this area is that of building capture and access applications which support information to be recorded (captured) during a live experience toward automatically producing documents for review (accessed). The recording demands instrumented environments with devices such as microphones, cameras, sensors and electronic whiteboards. Since each experience is usually related to many others (e.g. several meetings of a project), there is a demand for mechanisms supporting the automatic linking among documents relative to different experiences. In this paper we present original results relative to the integration of our previous efforts in the Infrastructure for Capturing, Accessing, Linking, Storing and Presenting information (CALiSP).

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

The Relationship between Transport Infrastructure and EconomicGrowth: An Empirical Analysis Comparing Developing and DevelopedCountries

Gross domestic product (GDP) is generally considered as the most important index and comprehensive measure of the size of economy. This paper investigates empirically the relationship between transport infrastructure (focus on highways) and GDP growth based on a production function approach. The physical stocks of transport infrastructure were used instead of monetary data to measure public capital together with several other variables (labor and private capital) that were hypothesized to affect economic growth. Then we explore a number of subsequent studies that use panel data covering the period between 1992 and 2004. An investigation was done to compare developed countries and developing countries. Results indicate that physical units are positively and significantly related to economic growth. Furthermore there was an interesting finding that the output elasticity with respect to physical units for developed countries is higher than developing countries.