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Main Beach, Australia

Naish D.A.,Queensland Transport and Main Roads
INTERNOISE 2014 - 43rd International Congress on Noise Control Engineering: Improving the World Through Noise Control | Year: 2014

This paper presents a concept towards the development of a flexible method of predicting noise event probability across a road network using road function categories. The need for a flexible method is derived from the varying levels of data availability and data accuracy for different road authorities. A road authority should not be prevented in assessing noise events due to lack of highly detailed and accurate data. The benefits of a network scale method of predicting noise event probability include the ability to: (1) produce network scale noise event maps, (2) better conduct public health research, and (3) assess changes to the road traffic distribution across a network. These three listed benefits combined will allow application of localised transport-health research. The paper commences with a short review of literature and theoretical concepts, followed by an outline on the data and computational requirements for noise event probability prediction. Analysis of sample road vehicle speed and classification data is conducted across varying road functions. The concepts behind a flexible prediction method are then presented along with a discussion on its potential application, limitations and benefits. Source


Chowdhury S.,Central Queensland University | Verma B.,Central Queensland University | Stockwell D.,Queensland Transport and Main Roads
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Vegetation classification from satellite and aerial images is a common research area for fire risk assessment and environmental surveys for decades. Recently classification from video data obtained by vehicle mounted video in outdoor environments is receiving considerable attention due to the large number of real-world applications. However this is a very challenging task and requires novel research techniques. This paper presents an analysis of hybrid classification approach to distinguish vegetation in particularly the type of roadside grasses from videos recorded by the Queensland transport and main roads. The proposed framework can distinguish dense and non-dense grass regions from roadside video data. While most of the recent works focuses on infrared images, proposed approach uses image texture feature for vegetation region classification. Analysis of hybrid approach using texture feature and multiple classifiers is the main contribution of this research work. The classifiers include: Support Vector Machine (SVM), Neural Network (NN), k- Nearest Neighbor (k-NN), AdaBoost and Naïve Bayes. The different images were created from video data containing roadside vegetation in various conditions for training and testing purposes. The hybrid classification approach has been analysed on roadside data obtained and results are discussed. © Springer International Publishing Switzerland 2014. Source


Evert M.,Queensland Transport and Main Roads | Worden J.,University of Southern Queensland | Kidd P.,SMEC
Australian Geomechanics Journal | Year: 2014

This paper examines the engineering issues relating to the utilisation of a commonly used rock strength multiplier with Sandstone in Southeast Queensland (SEQLD) and Northeast New South Wales (NENSW). • For this paper, rock strength results provided by the Snowy Mountains Engineering Corporation (SMEC) in S-E QLD and N-E NSW are compared to, results of other, studies made in S-E QLD; in the Handbook of Geotechnical Investigation and Design Tables (Look, 2007). Source


Chowdhury S.,Central Queensland University | Verma B.,Central Queensland University | Stockwell D.,Queensland Transport and Main Roads
Expert Systems with Applications | Year: 2015

This paper presents a novel texture feature based multiple classifier technique and applies it to roadside vegetation classification. It is well-known that automation of roadside vegetation classification is one of the important issues emerging strongly in improving the fire risk and road safety. Hence, the application presented in this paper is significantly important for identifying fire risks and road safety. The images collected from outdoor environments such as roadside, are affected for a high variability of illumination conditions because of different weather conditions. This paper proposes a novel texture feature based robust expert system for vegetation identification. It consists of five steps, namely image pre-processing, feature extraction, training with multiple classifiers, classification, validation and statistical analysis. In the initial stage, Co-occurrence of Binary Pattern (CBP) technique is applied in order to obtain the texture feature relevant to vegetation in the roadside images. In the training and classification stages, three classifiers have been fused to combine the multiple decisions. The first classifier is based on Support Vector Machine, the second classifier is based on feed forward back-propagation neural network (FF-BPNN) and the third classifier is based on -Nearest Neighbor (k-NN). The proposed technique has been applied and evaluated on two types of vegetation images i.e. dense and sparse grasses. The classification accuracy with a success of 92.72% has been obtained using 5-fold cross validation approach. An (Analysis of Variance) test has also been conducted to show the statistical significance of results. © 2015 Elsevier Ltd. All rights reserved. Source


McKinnon S.,Queensland Transport and Main Roads | Voisey C.,Queensland Transport and Main Roads | Wood P.,Future Plus Environmental
Coasts and Ports 2013 | Year: 2013

The Department of Transport and Main Roads (TMR) manages Rosslyn Bay Boat Harbour, located south of Yeppoon in Central Queensland. Due to its location 40km north of the Fitzroy River entrance, the harbour experiences significant siltation and requires frequent (3-4 yearly) maintenance dredging. To support the maintenance dredging campaigns over the last eight years TMR has undertaken a range of modelling and monitoring investigations. This paper discusses the project learnings from these investigations, with a particular focus on the environmental monitoring methodologies, the monitoring outcomes and how these outcomes will be used to refine future monitoring strategies for this site. The Water Quality data collected shows that wind, wave and freshwater flooding events during the dredging project caused high turbidity and low light conditions at the sensitive sites monitored and at the control site 10km south. For this reason no impacts could be attributed to the dredging project itself. Hydrographic Monitoring indicated that 75% of the material placed at the disposal site had been re-suspended and redistributed during the works. As of the time of writing the benthic surveys were not complete however these results post dredge and then 12 months after the works will be important in guiding ongoing management of Rosslyn Bay Boat Harbour. Source

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